@misc{lee_bigvgan_2023,
	title = {{BigVGAN}: {A} {Universal} {Neural} {Vocoder} with {Large}-{Scale} {Training}},
	shorttitle = {{BigVGAN}},
	url = {http://arxiv.org/abs/2206.04658},
	doi = {10.48550/arXiv.2206.04658},
	abstract = {Despite recent progress in generative adversarial network (GAN)-based vocoders, where the model generates raw waveform conditioned on acoustic features, it is challenging to synthesize high-fidelity audio for numerous speakers across various recording environments. In this work, we present BigVGAN, a universal vocoder that generalizes well for various out-of-distribution scenarios without fine-tuning. We introduce periodic activation function and anti-aliased representation into the GAN generator, which brings the desired inductive bias for audio synthesis and significantly improves audio quality. In addition, we train our GAN vocoder at the largest scale up to 112M parameters, which is unprecedented in the literature. We identify and address the failure modes in large-scale GAN training for audio, while maintaining high-fidelity output without over-regularization. Our BigVGAN, trained only on clean speech (LibriTTS), achieves the state-of-the-art performance for various zero-shot (out-of-distribution) conditions, including unseen speakers, languages, recording environments, singing voices, music, and instrumental audio. We release our code and model at: https://github.com/NVIDIA/BigVGAN},
	urldate = {2025-07-02},
	publisher = {arXiv},
	author = {Lee, Sang-gil and Ping, Wei and Ginsburg, Boris and Catanzaro, Bryan and Yoon, Sungroh},
	month = feb,
	year = {2023},
	note = {arXiv:2206.04658 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@article{medic_short-_2017,
	title = {Short- and long-term health consequences of sleep disruption},
	volume = {9},
	issn = {null},
	doi = {10.2147/NSS.S134864},
	abstract = {Sleep plays a vital role in brain function and systemic physiology across many body systems. Problems with sleep are widely prevalent and include deficits in quantity and quality of sleep; sleep problems that impact the continuity of sleep are collectively referred to as sleep disruptions. Numerous factors contribute to sleep disruption, ranging from lifestyle and environmental factors to sleep disorders and other medical conditions. Sleep disruptions have substantial adverse short- and long-term health consequences. A literature search was conducted to provide a nonsystematic review of these health consequences (this review was designed to be nonsystematic to better focus on the topics of interest due to the myriad parameters affected by sleep). Sleep disruption is associated with increased activity of the sympathetic nervous system and hypothalamic–pituitary–adrenal axis, metabolic effects, changes in circadian rhythms, and proinflammatory responses. In otherwise healthy adults, short-term consequences of sleep disruption include increased stress responsivity, somatic pain, reduced quality of life, emotional distress and mood disorders, and cognitive, memory, and performance deficits. For adolescents, psychosocial health, school performance, and risk-taking behaviors are impacted by sleep disruption. Behavioral problems and cognitive functioning are associated with sleep disruption in children. Long-term consequences of sleep disruption in otherwise healthy individuals include hypertension, dyslipidemia, cardiovascular disease, weight-related issues, metabolic syndrome, type 2 diabetes mellitus, and colorectal cancer. All-cause mortality is also increased in men with sleep disturbances. For those with underlying medical conditions, sleep disruption may diminish the health-related quality of life of children and adolescents and may worsen the severity of common gastrointestinal disorders. As a result of the potential consequences of sleep disruption, health care professionals should be cognizant of how managing underlying medical conditions may help to optimize sleep continuity and consider prescribing interventions that minimize sleep disruption.},
	urldate = {2025-05-07},
	journal = {Nature and Science of Sleep},
	author = {Medic, Goran and , Micheline, Wille and and Hemels, Michiel EH},
	month = may,
	year = {2017},
	keywords = {adolescents, adults, children, health status, sleep, sleep disorders},
	pages = {151--161},
}

@article{loewy_music_2020,
	title = {Music {Therapy} as a {Potential} {Intervention} for {Sleep} {Improvement}},
	volume = {12},
	issn = {null},
	doi = {10.2147/NSS.S194938},
	abstract = {Sleep deficiency is linked to chronic health problems, such as heart disease, kidney disease, high blood pressure, diabetes, stroke, obesity, and depression. Healthcare practitioners are increasingly paying close attention to sleep and its impact on health and wellness as a measure of critical vitality. Sleep’s impact on neurologic function, and cognitive endurance affect capacity throughout the lifespan. This article will address recent findings related to the potential of music to induce sleep in illness and wellness. Music therapy research findings and its efficacy as a potent cost-effective intervention will be highlighted.},
	urldate = {2025-06-12},
	journal = {Nature and Science of Sleep},
	author = {Loewy, Joanne},
	month = jan,
	year = {2020},
	keywords = {music sedation, music therapy, sleep induction},
	pages = {1--9},
}

@article{gebara_effect_2018,
	title = {Effect of insomnia treatments on depression: {A} systematic review and meta-analysis},
	volume = {35},
	copyright = {© 2018 Wiley Periodicals, Inc.},
	issn = {1520-6394},
	shorttitle = {Effect of insomnia treatments on depression},
	doi = {10.1002/da.22776},
	abstract = {Background Insomnia is frequently co-morbid with depression, with a bidirectional relationship between these disorders. There is evidence that insomnia-specific interventions, such as cognitive behavioral therapy for insomnia, may lead to improvements in depression. The purpose of this systematic review and meta-analysis is to determine whether treatment of insomnia leads to improved depression outcomes in individuals with both insomnia and depression. Methods We conduct a systematic review and meta-analysis to explore the effect of treatment for insomnia disorder on depression in patients with both disorders. Results Three thousand eight hundred and fifteen studies were reviewed, and 23 studies met inclusion criteria. Although all of the studies suggested a positive clinical effect of insomnia treatment on depression outcomes, most of the results were not statistically significant. Although the interventions and populations were highly variable, the meta-analysis indicates moderate to large effect size (ES) improvement in depression as measured with the Hamilton Depression Rating Scale (ES = −1.29, 95\%CI [−2.11, −0.47]) and Beck Depression Inventory (ES = −0.68, 95\%CI [−1.29, −0.06]). Conclusions These results support that treating insomnia in patients with depression has a positive effect on mood. Future trials are needed to identify the subtypes of patients whose depression improves during treatment with insomnia-specific interventions, and to identify the mechanisms by which treating insomnia improves mood.},
	language = {en},
	number = {8},
	urldate = {2025-05-08},
	journal = {Depression and Anxiety},
	author = {Gebara, , Marie Anne and Siripong, Nalyn and DiNapoli, Elizabeth A. and Maree, Rachel D. and Germain, Anne and Reynolds, Charles F. and Kasckow, John W. and Weiss, Patricia M. and Karp, Jordan F.},
	year = {2018},
	keywords = {cognitive therapy, comorbidity, depressive disorder, major, remission induction, sleep initiation and maintenance disorders},
	pages = {717--731},
}

@article{lai_music_2005,
	title = {Music improves sleep quality in older adults},
	volume = {49},
	issn = {1365-2648},
	doi = {10.1111/j.1365-2648.2004.03281.x},
	abstract = {Aim. The aim of this paper is to report an investigation of the effects of soft music on sleep quality in older community-dwelling men and women in Taiwan. Background. Sleep is a complex rhythmic state that may be affected by the ageing process. Few studies have focused on the effects of music, a non-pharmacological method of improving the quality of sleep in older adults. Method. A randomized controlled trial was used with a two-group repeated measures design. Sixty people aged 60–83 years with difficulty in sleeping were recruited through community leaders and screened using the Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale. Those reporting depression, cognitive impairment, medical or environmental problems that might interfere with sleep; and those who used sleeping medications, meditation, or caffeine at bedtime were excluded. Participants listened to their choice among six 45-minute sedative music tapes at bedtime for 3 weeks. There were five types of Western and one of Chinese music. Sleep quality was measured with the PSQI before the study and at three weekly post-tests. Groups were comparable on demographic variables, anxiety, depressive symptoms, physical activity, bedtime routine, herbal tea use, napping, pain, and pretest overall sleep quality. Results. Music resulted in significantly better sleep quality in the experimental group, as well as significantly better components of sleep quality: better perceived sleep quality, longer sleep duration, greater sleep efficiency, shorter sleep latency, less sleep disturbance and less daytime dysfunction (P = 0·04–0·001). Sleep improved weekly, indicating a cumulative dose effect. Conclusion. The findings provide evidence for the use of soothing music as an empirically-based intervention for sleep in older people.},
	language = {en},
	number = {3},
	urldate = {2023-11-23},
	journal = {Journal of Advanced Nursing},
	author = {Lai, Hui-Ling and Good, Marion},
	year = {2005},
	keywords = {community-residing elders, music, nursing, sleep quality},
	pages = {234--244},
}

@article{lee2013does,
  title={Does music therapy improve mental health in adults? A review},
  author={Lee, Jungup and Thyer, Bruce A},
  journal={Journal of Human Behavior in the Social Environment},
  volume={23},
  number={5},
  pages={591--603},
  year={2013},
  publisher={Taylor \& Francis}
}


@article{riemann_insomnia_2022,
	title = {Insomnia disorder: {State} of the science and challenges for the future},
	volume = {31},
	copyright = {© 2022 The Authors. Journal of Sleep Research published by John Wiley \& Sons Ltd on behalf of European Sleep Research Society.},
	issn = {1365-2869},
	shorttitle = {Insomnia disorder},
	doi = {10.1111/jsr.13604},
	abstract = {Insomnia disorder comprises symptoms during night and day that strongly affect quality of life and wellbeing. Prolonged sleep latency, difficulties to maintain sleep and early morning wakening characterize sleep complaints, whereas fatigue, reduced attention, impaired cognitive functioning, irritability, anxiety and low mood are key daytime impairments. Insomnia disorder is well acknowledged in all relevant diagnostic systems: Diagnostic and Statistical Manual of the American Psychiatric Association, 5th revision, International Classification of Sleep Disorders, 3rd version, and International Classification of Diseases, 11th revision. Insomnia disorder as a chronic condition is frequent (up to 10\% of the adult population, with a preponderance of females), and signifies an important and independent risk factor for physical and, especially, mental health. Insomnia disorder diagnosis primarily rests on self-report. Objective measures like actigraphy or polysomnography are not (yet) part of the routine diagnostic canon, but play an important role in research. Disease concepts of insomnia range from cognitive-behavioural models to (epi-) genetics and psychoneurobiological approaches. The latter is derived from knowledge about basic sleep–wake regulation and encompass theories like rapid eye movement sleep instability/restless rapid eye movement sleep. Cognitive-behavioural models of insomnia led to the conceptualization of cognitive-behavioural therapy for insomnia, which is now considered as first-line treatment for insomnia worldwide. Future research strategies will include the combination of experimental paradigms with neuroimaging and may benefit from more attention to dysfunctional overnight alleviation of distress in insomnia. With respect to therapy, cognitive-behavioural therapy for insomnia merits widespread implementation, and digital cognitive-behavioural therapy may assist delivery along treatment guidelines. However, given the still considerable proportion of patients responding insufficiently to cognitive-behavioural therapy for insomnia, fundamental studies are highly necessary to better understand the brain and behavioural mechanisms underlying insomnia. Mediators and moderators of treatment response/non-response and the associated development of tailored and novel interventions also require investigation. Recent studies suggest that treatment of insomnia may prove to add significantly as a preventive strategy to combat the global burden of mental disorders.},
	language = {en},
	number = {4},
	urldate = {2024-09-20},
	journal = {Journal of Sleep Research},
	author = {Riemann, Dieter and Benz, Fee and Dressle, Raphael J. and Espie, Colin A. and Johann, Anna F. and Blanken, Tessa F. and Leerssen, Jeanne and Wassing, Rick and Henry, Alasdair L. and Kyle, Simon D. and Spiegelhalder, Kai and Van Someren, Eus J. W.},
	year = {2022},
	keywords = {CBT-I, anxiety, depression, insomnia, insomnia models, prevention, treatment guidelines},
	pages = {e13604},
}

@inproceedings{tulilaulu_sleep_2012,
	address = {Berlin, Heidelberg},
	title = {Sleep {Musicalization}: {Automatic} {Music} {Composition} from {Sleep} {Measurements}},
	isbn = {978-3-642-34156-4},
	shorttitle = {Sleep {Musicalization}},
	doi = {10.1007/978-3-642-34156-4_36},
	abstract = {We introduce data musicalization as a novel approach to aid analysis and understanding of sleep measurement data. Data musicalization is the process of automatically composing novel music, with given data used to guide the process. We present Sleep Musicalization, a methodology that reads a signal from state-of-the-art mattress sensor, uses highly non-trivial data analysis methods to measure sleep from the signal, and then composes music from the measurements. As a result, Sleep Musicalization produces music that reflects the user’s sleep during a night and complements visualizations of sleep measurements. The ultimate goal is to help users improve their sleep and well-being. For practical use and later evaluation of the methodology, we have built a public web service at http://sleepmusicalization.netfor users of the sleep sensors.},
	language = {en},
	booktitle = {Advances in {Intelligent} {Data} {Analysis} {XI}},
	publisher = {Springer},
	author = {Tulilaulu, Aurora and Paalasmaa, Joonas and Waris, Mikko and Toivonen, Hannu},
	editor = {Hollmén, Jaakko and Klawonn, Frank and Tucker, Allan},
	year = {2012},
	keywords = {Composition Algorithm, Deep Sleep, Light Sleep, Sleep Measurement, Sleep Stage},
	pages = {392--403},
}

@inproceedings{gui_adapting_2024,
	title = {Adapting {Frechet} {Audio} {Distance} for {Generative} {Music} {Evaluation}},
	doi = {10.1109/ICASSP48485.2024.10446663},
	abstract = {The growing popularity of generative music models underlines the need for perceptually relevant, objective music quality metrics. The Frechet Audio Distance (FAD) is commonly used for this purpose even though its correlation with perceptual quality is understudied. We show that FAD performance may be hampered by sample size bias, poor choice of audio embeddings, or the use of biased or low-quality reference sets. We propose reducing sample size bias by extrapolating scores towards an infinite sample size. Through comparisons with MusicCaps labels and a listening test we identify audio embeddings and music reference sets that yield FAD scores well-correlated with acoustic and musical quality. Our results suggest that per-song FAD can be useful to identify outlier samples and predict perceptual quality for a range of music sets and generative models. Finally, we release a toolkit that allows adapting FAD for generative music evaluation.},
	urldate = {2025-06-16},
	booktitle = {{ICASSP} 2024 - 2024 {IEEE} {International} {Conference} on {Acoustics}, {Speech} and {Signal} {Processing} ({ICASSP})},
	author = {Gui, Azalea and Gamper, Hannes and Braun, Sebastian and Emmanouilidou, Dimitra},
	month = apr,
	year = {2024},
	note = {ISSN: 2379-190X},
	keywords = {Acoustics, Adaptation models, Correlation, Evaluation Metrics, Frechet Audio Distance, Generative model, Measurement, Music/Audio generation, Predictive models, Quality Assessment, Signal processing, Speech processing},
	pages = {1331--1335},
}

@inproceedings{kilgour_frechet_2019,
	title = {Fréchet {Audio} {Distance}: {A} {Reference}-{Free} {Metric} for {Evaluating} {Music} {Enhancement} {Algorithms}},
	shorttitle = {Fréchet {Audio} {Distance}},
	doi = {10.21437/Interspeech.2019-2219},
	abstract = {We propose the Fre´chet Audio Distance (FAD), a novel, reference-free evaluation metric for music enhancement algorithms. We demonstrate how typical evaluation metrics for speech enhancement and blind source separation can fail to accurately measure the perceived effect of a wide variety of distortions. As an alternative, we propose adapting the Fre´chet Inception Distance (FID) metric used to evaluate generative image models to the audio domain. FAD is validated using a wide variety of artiﬁcial distortions and is compared to the signal based metrics signal to distortion ratio (SDR), cosine distance, and magnitude L2 distance. We show that, with a correlation coefﬁcient of 0.52, FAD correlates more closely with human perception than either SDR, cosine distance or magnitude L2 distance, with correlation coefﬁcients of 0.39, −0.15 and −0.01 respectively.},
	language = {en},
	urldate = {2025-06-16},
	booktitle = {Interspeech 2019},
	publisher = {ISCA},
	author = {Kilgour, Kevin and Zuluaga, Mauricio and Roblek, Dominik and Sharifi, Matthew},
	month = sep,
	year = {2019},
	pages = {2350--2354},
}

@article{moore_dysfunctions_1988,
	title = {The {Dysfunctions} of {MIDI}},
	volume = {12},
	issn = {0148-9267},
	url = {https://www.jstor.org/stable/3679834},
	doi = {10.2307/3679834},
	number = {1},
	urldate = {2025-06-12},
	journal = {Computer Music Journal},
	author = {Moore, F. Richard},
	year = {1988},
	note = {Publisher: The MIT Press},
	pages = {19--28},
}

@article{castellon_towards_nodate,
	title = {Towards realistic {MIDI} instrument synthesizers},
	abstract = {Despite decades of research and widespread commercial interest, synthesis of realistic musical instrument audio from real-time MIDI input remains elusive. In this work, we propose a learning-based system for creating bespoke, MIDI-driven instrument synthesizers. Our system takes as training data audio recordings of a particular instrument and, optionally, scores corresponding to those recordings. To prepare the data, we use an off-the-shelf transcription (or score alignment) strategy to create time-aligned MIDI and audio. Then, we train recurrent neural networks to learn a distribution over instrument audio given MIDI. We demonstrate our method on the task of violin synthesis. Our user study indicates that our proposed system can learn a real-time-capable, MIDI-controllable violin synthesizer which produces fairly realistic audio given only a few minutes of amateur recordings.},
	language = {en},
	author = {Castellon, Rodrigo and Donahue, Chris and Liang, Percy},
}

@article{wang_music_2014,
	title = {Music therapy improves sleep quality in acute and chronic sleep disorders: {A} meta-analysis of 10 randomized studies},
	volume = {51},
	issn = {0020-7489},
	shorttitle = {Music therapy improves sleep quality in acute and chronic sleep disorders},
	doi = {10.1016/j.ijnurstu.2013.03.008},
	abstract = {Objective
To evaluate the efficacy of music therapy for acute and chronic sleep disorders in adults.
Design
Systematic review and meta-analysis.
Data sources
A systematic search of publications in PubMed, Embase, and the Cochrane Library without language restriction was performed.
Review methods
Studies with randomized controlled design and adult participants were included if music was applied in a passive way to improve sleep quality. Subgroup analysis was conducted to explore the sources of heterogeneity.
Results
Ten studies involving 557 participants were identified. The sleep quality was improved significantly by music (standard mean difference: −0.63; 95\% CI: −0.92 to −0.34; p{\textless}0.001), with significant heterogeneity across studies. Subgroup analysis found heterogeneity between subgroups with objective or subjective assessing methods of sleep quality, and between subgroups with difference follow-up durations. No evidence of publication bias was observed.
Conclusion
Music can assist in improving sleep quality of patients with acute and chronic sleep disorders. For chronic sleep disorders, music showed a cumulative dose effect and a follow-up duration more than three weeks is necessary for assessing its efficacy.},
	number = {1},
	urldate = {2025-06-12},
	journal = {International Journal of Nursing Studies},
	author = {Wang, Chun-Fang and Sun, Ying-Li and Zang, Hong-Xin},
	month = jan,
	year = {2014},
	keywords = {Meta-analysis, Music, Sleep disorders, Sleep quality},
	pages = {51--62},
}

@article{altena_sleep_2008,
	title = {Sleep loss affects vigilance: effects of chronic insomnia and sleep therapy},
	volume = {17},
	copyright = {© 2008 European Sleep Research Society},
	issn = {1365-2869},
	shorttitle = {Sleep loss affects vigilance},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-2869.2008.00671.x},
	doi = {10.1111/j.1365-2869.2008.00671.x},
	abstract = {Although complaints of impaired daytime functioning are essential to the diagnosis of primary insomnia, objective evidence for cognitive dysfunction has been hard to establish. A prerequisite for understanding the neurocognitive consequences of primary insomnia is to establish task paradigms that robustly differentiate insomniacs from well-sleeping subjects. We hypothesized that the decline in performance that typically occurs with an increasing cognitive demand would provide a more sensitive measure than performance on a single task version. The hypothesis was tested, first, by assessing the performance on two vigilance tasks with different cognitive demands in 25 elderly patients with primary insomnia and 13 healthy well-sleeping age-matched subjects. Secondly, we investigated the performance response to sleep therapy using a waiting-list controlled design. Sleep therapy consisted of a multi-component intervention including sleep restriction, cognitive behavioral therapy, bright-light therapy, structured physical activity and body temperature manipulations. The results show that insomniacs differed markedly from controls in their reaction times across tasks with different cognitive demands: patients responded faster on the ‘simple’ vigilance task, yet slower on the ‘complex’ vigilance task. Sleep therapy effectively restored normal performance: patients became significantly slower on the ‘simple’ task and faster on the ‘complex’ task, returning to the performance levels of control subjects. These findings indicate that the performance decline associated with increasing cognitive demands is possibly the first sensitive and robust measure of the neurocognitive sequelae of insomnia. We suggest that future studies on cognition in primary insomnia should apply a design that varies task demands.},
	language = {en},
	number = {3},
	urldate = {2025-06-12},
	journal = {Journal of Sleep Research},
	author = {Altena, Ellemarije and Van Der Werf, Ysbrand D. and Strijers, Rob L. M. and Van Someren, Eus J. W.},
	year = {2008},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2869.2008.00671.x},
	keywords = {arousal, attention, insomnia, sleep disorders, task complexity, vigilance},
	pages = {335--343},
}

@article{van_maanen_effects_2016,
	title = {The effects of light therapy on sleep problems: {A} systematic review and meta-analysis},
	volume = {29},
	issn = {1087-0792},
	shorttitle = {The effects of light therapy on sleep problems},
	url = {https://www.sciencedirect.com/science/article/pii/S1087079215001136},
	doi = {10.1016/j.smrv.2015.08.009},
	abstract = {Although bright light therapy seems a promising treatment for sleep problems, research shows inconclusive results. This meta-analysis is the first to systematically review the effect of light therapy on sleep problems in general and on specific types of sleep problems in particular (circadian rhythm sleep disorders, insomnia, sleep problems related to Alzheimer's disease and dementia). Fifty-three studies with a total of 1154 participants were included. Overall effects and effects on separate circadian and sleep outcomes were examined. We calculated Hedges' g effect sizes and we investigated the effects of twelve moderators (design-related, treatment-related, participant-related). Light therapy was found effective in the treatment of sleep problems in general (g = 0.39), and for circadian rhythm sleep disorders (g = 0.41), insomnia (g = 0.47), and sleep problems related to Alzheimer's disease/dementia (g = 0.30) specifically. For circadian rhythm sleep disorders, effects were smaller for randomised controlled trials. For insomnia, we found larger effects for studies using a higher light intensity, and for sleep problems related to Alzheimer's disease/dementia larger effects were found for studies with more female participants. There was indication of publication bias. To conclude, light therapy is effective for sleep problems in general, particularly for circadian outcomes and insomnia symptoms. However, most effect sizes are small to medium.},
	urldate = {2025-06-12},
	journal = {Sleep Medicine Reviews},
	author = {van Maanen, Annette and Meijer, Anne Marie and van der Heijden, Kristiaan B. and Oort, Frans J.},
	month = oct,
	year = {2016},
	keywords = {Bright light therapy, Circadian rhythm sleep disorders, Insomnia, Meta-analysis, Sleep problems},
	pages = {52--62},
}

@article{hromadka_knowledge_nodate,
	title = {Knowledge {Distillation} with {Training} {Dynamics}},
	copyright = {All rights reserved},
	abstract = {In this study, we examine the efficacy of Knowledge Distillation in conjunction with Dataset Cartography within Computer Vision. Our aim was to ascertain whether training on subsets of data, selected based on training dynamics, can enhance the performance of student models while being cost-effective. Contrary to expectations set by similar NLP studies, our experiments across various architectures and datasets (CIFAR10, CIFAR100, MNIST) revealed that randomly selected data subsets consistently outperformed those chosen based on training dynamics. This unexpected outcome suggests for the task of dataset selection in Knowledge Distillation, particularly in the Computer Vision domain, a training set that is unconditionally representative of the larger dataset is better than a training dynamic-informed curriculum. Our findings highlight the complex interplay between data selection and model performance, opening new avenues for research in effective training strategies. We publish our code for future research.},
	language = {en},
	author = {Hromádka, Timo and Rex, Evan},
}

@article{hromadka_investigating_nodate,
	title = {Investigating {Training} {Dynamics} {Across} {Model} {Sizes}},
	copyright = {All rights reserved},
	abstract = {A commonly perceived notion in deep learning is that more data results in more robust models. However, strong models can underperform on out-of-distribution (OOD) datasets, despite effective in-distribution (ID) performance. This project explores the variability in data points’ contribution to OOD generalization, building on Dataset Cartography by Swayamdipta et al. [23]. In this work training dynamics are calculated, and this revelead an ‘ambiguous’ subset of data capable of promoting OOD generalization. This project extends this work by examining the relationship between model architecture, size, and training dynamics. I reveal that larger models exhibit higher confidence and and lower variability in data points, but these points don’t necessarily align across different model sizes. Through multiple ID-OOD dataset pairings, I demonstrate a significant drop in OOD generalization in smaller models, while uncovering the transferability potential in ambiguous data regions. Furthermore, this project confirms the potential of using hard-to-learn data points for identifying mislabeled samples in crowd-sourced datasets, and demonstrates that smaller models can be used for this purpose as well.},
	language = {en},
	author = {Hromádka, Timo},
}

@article{hromadka_knowledge_nodate-1,
	title = {Knowledge {Distillation} with {Training} {Dynamics}},
	copyright = {All rights reserved},
	abstract = {In this study, we examine the efficacy of Knowledge Distillation in conjunction with Dataset Cartography within Computer Vision. Our aim was to ascertain whether training on subsets of data, selected based on training dynamics, can enhance the performance of student models while being cost-effective. Contrary to expectations set by similar NLP studies, our experiments across various architectures and datasets (CIFAR10, CIFAR100, MNIST) revealed that randomly selected data subsets consistently outperformed those chosen based on training dynamics. This unexpected outcome suggests for the task of dataset selection in Knowledge Distillation, particularly in the Computer Vision domain, a training set that is unconditionally representative of the larger dataset is better than a training dynamic-informed curriculum. Our findings highlight the complex interplay between data selection and model performance, opening new avenues for research in effective training strategies. We publish our code for future research.},
	language = {en},
	author = {Hromádka, Timo and Rex, Evan},
}

@misc{payne_musenet_2019,
	type = {Blog},
	title = {{MuseNet} {\textbar} {OpenAI}},
	url = {https://openai.com/index/musenet/},
	urldate = {2024-11-07},
	author = {Payne, Christine},
	month = apr,
	year = {2019},
}

@article{ramar_sleep_2021,
	title = {Sleep is essential to health: an {American} {Academy} of {Sleep} {Medicine} position statement},
	volume = {17},
	issn = {1550-9389, 1550-9397},
	shorttitle = {Sleep is essential to health},
	doi = {10.5664/jcsm.9476},
	language = {en},
	number = {10},
	urldate = {2025-05-20},
	journal = {Journal of Clinical Sleep Medicine},
	author = {Ramar, Kannan and Malhotra, Raman K. and Carden, Kelly A. and Martin, Jennifer L. and Abbasi-Feinberg, Fariha and Aurora, R. Nisha and Kapur, Vishesh K. and Olson, Eric J. and Rosen, Carol L. and Rowley, James A. and Shelgikar, Anita V. and Trotti, Lynn Marie},
	month = oct,
	year = {2021},
	pages = {2115--2119},
}

@misc{noauthor_sleep_nodate,
	title = {Sleep is essential to health: an {American} {Academy} of {Sleep} {Medicine} position statement {\textbar} {Journal} of {Clinical} {Sleep} {Medicine}},
	url = {https://jcsm.aasm.org/doi/10.5664/jcsm.9476},
	urldate = {2025-05-20},
}

@article{consensus_conference_panel_joint_2015,
	title = {Joint {Consensus} {Statement} of the {American} {Academy} of {Sleep} {Medicine} and {Sleep} {Research} {Society} on the {Recommended} {Amount} of {Sleep} for a {Healthy} {Adult}: {Methodology} and {Discussion}},
	volume = {38},
	issn = {1550-9109},
	shorttitle = {Joint {Consensus} {Statement} of the {American} {Academy} of {Sleep} {Medicine} and {Sleep} {Research} {Society} on the {Recommended} {Amount} of {Sleep} for a {Healthy} {Adult}},
	doi = {10.5665/sleep.4886},
	abstract = {The American Academy of Sleep Medicine and Sleep Research Society recently released a Consensus Statement regarding the recommended amount of sleep to promote optimal health in adults. This paper describes the methodology, background literature, voting process, and voting results for the consensus statement. In addition, we address important assumptions and challenges encountered during the consensus process. Finally, we outline future directions that will advance our understanding of sleep need and place sleep duration in the broader context of sleep health.},
	language = {eng},
	number = {8},
	journal = {Sleep},
	author = {{Consensus Conference Panel} and Watson, Nathaniel F. and Badr, M. Safwan and Belenky, Gregory and Bliwise, Donald L. and Buxton, Orfeu M. and Buysse, Daniel and Dinges, David F. and Gangwisch, James and Grandner, Michael A. and Kushida, Clete and Malhotra, Raman K. and Martin, Jennifer L. and Patel, Sanjay R. and Quan, Stuart F. and Tasali, Esra},
	month = aug,
	year = {2015},
	pmid = {26194576},
	pmcid = {PMC4507722},
	keywords = {Academies and Institutes, Adult, Consensus, Health, Humans, Research, Sleep, Sleep Medicine Specialty, Societies, Scientific, United States, adult, consensus, health, recommendation, sleep duration},
	pages = {1161--1183},
}

@article{guerriero_artificial_2021,
	title = {Artificial intelligence ({AI}) in the detection of rectosigmoid deep endometriosis},
	volume = {261},
	issn = {0301-2115},
	url = {https://www.sciencedirect.com/science/article/pii/S0301211521001731},
	doi = {10.1016/j.ejogrb.2021.04.012},
	abstract = {Objectives
The aim of this study was to compare the accuracy of seven classical Machine Learning (ML) models trained with ultrasound (US) soft markers to raise suspicion of endometriotic bowel involvement.
Materials and Methods
Input data to the models was retrieved from a database of a previously published study on bowel endometriosis performed on 333 patients. The following models have been tested: k-nearest neighbors algorithm (k-NN), Naive Bayes, Neural Networks (NNET-neuralnet), Support Vector Machine (SVM), Decision Tree, Random Forest, and Logistic Regression. The data driven strategy has been to split randomly the complete dataset in two different datasets. The training dataset and the test dataset with a 67 \% and 33 \% of the original cases respectively. All models were trained on the training dataset and the predictions have been evaluated using the test dataset. The best model was chosen based on the accuracy demonstrated on the test dataset. The information used in all the models were: age; presence of US signs of uterine adenomyosis; presence of an endometrioma; adhesions of the ovary to the uterus; presence of “kissing ovaries”; absence of sliding sign. All models have been trained using CARET package in R with ten repeated 10-fold cross-validation. Accuracy, Sensitivity, Specificity, positive (PPV) and negative (NPV) predictive value were calculated using a 50 \% threshold. Presence of intestinal involvement was defined in all cases in the test dataset with an estimated probability greater than 0.5.
Results
In our previous study from where the inputs were retrieved, 106 women had a final expert US diagnosis of rectosigmoid endometriosis. In term of diagnostic accuracy the best model was the Neural Net (Accuracy, 0.73; sensitivity, 0.72; specificity 0.73; PPV 0.52; and NPV 0.86) but without significant difference with the others.
Conclusions
The accuracy of ultrasound soft markers in raising suspicion of rectosigmoid endometriosis using Artificial Intelligence (AI) models showed similar results to the logistic model.},
	urldate = {2025-05-12},
	journal = {European Journal of Obstetrics \& Gynecology and Reproductive Biology},
	author = {Guerriero, Stefano and Pascual, MariaAngela and Ajossa, Silvia and Neri, Manuela and Musa, Eleonora and Graupera, Betlem and Rodriguez, Ignacio and Alcazar, Juan Luis},
	month = jun,
	year = {2021},
	keywords = {Artificial intelligence, Endometriosis, Ultrasonography},
	pages = {29--33},
}

@article{snider_image_2022,
	title = {An image classification deep-learning algorithm for shrapnel detection from ultrasound images},
	volume = {12},
	copyright = {2022 This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-022-12367-2},
	doi = {10.1038/s41598-022-12367-2},
	abstract = {Ultrasound imaging is essential for non-invasively diagnosing injuries where advanced diagnostics may not be possible. However, image interpretation remains a challenge as proper expertise may not be available. In response, artificial intelligence algorithms are being investigated to automate image analysis and diagnosis. Here, we highlight an image classification convolutional neural network for detecting shrapnel in ultrasound images. As an initial application, different shrapnel types and sizes were embedded first in a tissue mimicking phantom and then in swine thigh tissue. The algorithm architecture was optimized stepwise by minimizing validation loss and maximizing F1 score. The final algorithm design trained on tissue phantom image sets had an F1 score of 0.95 and an area under the ROC curve of 0.95. It maintained higher than a 90\% accuracy for each of 8 shrapnel types. When trained only on swine image sets, the optimized algorithm format had even higher metrics: F1 and area under the ROC curve of 0.99. Overall, the algorithm developed resulted in strong classification accuracy for both the tissue phantom and animal tissue. This framework can be applied to other trauma relevant imaging applications such as internal bleeding to further simplify trauma medicine when resources and image interpretation are scarce.},
	language = {en},
	number = {1},
	urldate = {2025-05-12},
	journal = {Scientific Reports},
	author = {Snider, Eric J. and Hernandez-Torres, Sofia I. and Boice, Emily N.},
	month = may,
	year = {2022},
	note = {Publisher: Nature Publishing Group},
	keywords = {Biomedical engineering, Medical research, Ultrasonography},
	pages = {8427},
}

@article{harmsen_consensus_2022,
	title = {Consensus on revised definitions of {Morphological} {Uterus} {Sonographic} {Assessment} ({MUSA}) features of adenomyosis: results of modified {Delphi} procedure},
	volume = {60},
	copyright = {© 2021 The Authors. Ultrasound in Obstetrics \& Gynecology published by John Wiley \& Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.},
	issn = {1469-0705},
	shorttitle = {Consensus on revised definitions of {Morphological} {Uterus} {Sonographic} {Assessment} ({MUSA}) features of adenomyosis},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/uog.24786},
	doi = {10.1002/uog.24786},
	abstract = {Objectives To evaluate whether the Morphological Uterus Sonographic Assessment (MUSA) features of adenomyosis need to be better defined and, if deemed necessary, to reach consensus on the updated definitions. Methods A modified Delphi procedure was performed among European gynecologists with expertise in ultrasound diagnosis of adenomyosis. To identify MUSA features that might need revision, 15 two-dimensional (2D) video recordings (four recordings also included three-dimensional (3D) still images) of transvaginal ultrasound (TVS) examinations of the uterus were presented in the first Delphi round (online questionnaire). Experts were asked to confirm or refute the presence of each of the nine MUSA features of adenomyosis (described in the original MUSA consensus statement) in each of the 15 videoclips and to provide comments. In the second Delphi round (online questionnaire), the results of the first round and suggestions for revision of MUSA features were shared with the experts before they were asked to assess a new set of 2D and 3D still images of TVS examinations and to provide feedback on the proposed revisions. A third Delphi round (virtual group meeting) was conducted to discuss and reach final consensus on revised definitions of MUSA features. Consensus was predefined as at least 66.7\% agreement between experts. Results Of 18 invited experts, 16 agreed to participate in the Delphi procedure. Eleven experts completed and four experts partly finished the first round. The experts identified a need for more detailed definitions of some MUSA features. They recommended use of 3D ultrasound to optimize visualization of the junctional zone. Fifteen experts participated in the second round and reached consensus on the presence or absence of ultrasound features of adenomyosis in most of the still images. Consensus was reached for all revised definitions except those for subendometrial lines and buds and interrupted junctional zone. Thirteen experts joined the online meeting, in which they discussed and agreed on final revisions of the MUSA definitions. There was consensus on the need to distinguish between direct features of adenomyosis, i.e. features indicating presence of ectopic endometrial tissue in the myometrium, and indirect features, i.e. features reflecting changes in the myometrium secondary to presence of endometrial tissue in the myometrium. Myometrial cysts, hyperechogenic islands and echogenic subendometrial lines and buds were classified unanimously as direct features of adenomyosis. Globular uterus, asymmetrical myometrial thickening, fan-shaped shadowing, translesional vascularity, irregular junctional zone and interrupted junctional zone were classified as indirect features of adenomyosis. Conclusion Consensus between gynecologists with expertise in ultrasound diagnosis of adenomyosis was achieved regarding revised definitions of the MUSA features of adenomyosis and on the classification of MUSA features as direct or indirect signs of adenomyosis. © 2021 The Authors. Ultrasound in Obstetrics \& Gynecology published by John Wiley \& Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.},
	language = {en},
	number = {1},
	urldate = {2025-05-12},
	journal = {Ultrasound in Obstetrics \& Gynecology},
	author = {Harmsen, M. J. and Van den Bosch, T. and de Leeuw, R. A. and Dueholm, M. and Exacoustos, C. and Valentin, L. and Hehenkamp, W. J. K. and Groenman, F. and De Bruyn, C. and Rasmussen, C. and Lazzeri, L. and Jokubkiene, L. and Jurkovic, D. and Naftalin, J. and Tellum, T. and Bourne, T. and Timmerman, D. and Huirne, J. a. F.},
	year = {2022},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/uog.24786},
	keywords = {Delphi technique, adenomyosis, consensus, ultrasonography},
	pages = {118--131},
}

@misc{noauthor_stard_nodate,
	title = {{STARD} 2015: {An} {Updated} {List} of {Essential} {Items} for {Reporting} {Diagnostic} {Accuracy} {Studies} {\textbar} {Clinical} {Chemistry} {\textbar} {Oxford} {Academic}},
	url = {https://academic.oup.com/clinchem/article/61/12/1446/5611598?login=true},
	urldate = {2025-05-12},
}

@article{savcenko_detection_1998,
	title = {Detection of subtle abnormalities on chest radiographs after irreversible compression},
	volume = {206},
	issn = {0033-8419},
	doi = {10.1148/radiology.206.3.9494474},
	abstract = {PURPOSE: To assess the effect of wavelet-based compression of posteroanterior chest radiographs on detection of small uncalcified pulmonary nodules and fibrosis.
MATERIALS AND METHODS: Computed tomography (CT) of the chest was used to identify 20 patients with normal posteroanterior chest radiographs, 20 with a solitary uncalcified pulmonary nodule 1-2 cm in diameter, and 20 with fibrotic disease. A double-blind protocol for readings of original images and images compressed at 40:1 and 80:1 was analyzed by using the nonparametric receiver operating characteristic to measure differences in diagnostic accuracy and their statistical significance.
RESULTS: There was no substantial difference in the overall diagnostic accuracy (measured by the area under the curve index) for both nodules and fibrosis between images compressed at 40:1 and 80:1 and uncompressed images. Readers tended to perform better on images compressed at 40:1 compared with uncompressed images. The "high-sensitivity" portion of the 80:1 compression curve for nodules was below that for the uncompressed curve, although this was not statistically significant.
CONCLUSION: Lossy compression of chest radiographs at 40:1 can be used without decreased diagnostic accuracy for detection of pulmonary nodules and fibrosis. There is no statistically significant difference in diagnostic accuracy at 80:1 compression, but detection ability is decreased.},
	language = {eng},
	number = {3},
	journal = {Radiology},
	author = {Savcenko, V. and Erickson, B. J. and Palisson, P. M. and Persons, K. R. and Manduca, A. and Hartman, T. E. and Harms, G. F. and Brown, L. R.},
	month = mar,
	year = {1998},
	pmid = {9494474},
	keywords = {Algorithms, Double-Blind Method, Humans, Image Processing, Computer-Assisted, Pulmonary Fibrosis, ROC Curve, Radiographic Image Enhancement, Radiography, Thoracic, Retrospective Studies, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Solitary Pulmonary Nodule, Tomography, X-Ray Computed},
	pages = {609--616},
}

@article{cosman_thoracic_1994,
	title = {Thoracic {CT} images: effect of lossy image compression on diagnostic accuracy},
	volume = {190},
	issn = {0033-8419},
	shorttitle = {Thoracic {CT} images},
	doi = {10.1148/radiology.190.2.8284409},
	abstract = {PURPOSE: To evaluate the effects of lossy image (noninvertible) compression on diagnostic accuracy of thoracic computed tomographic images.
MATERIALS AND METHODS: Sixty images from patients with mediastinal adenopathy and pulmonary nodules were compressed to six different levels with tree-structured vector quantization. Three radiologists then used the original and compressed images for diagnosis. Unlike many previous receiver operating characteristic-based studies that used confidence rankings and binary detection tasks, this study examined the sensitivity and predictive value positive scores from nonbinary detection tasks.
RESULTS: At the 5\% significance level, there was no statistically significant difference in diagnostic accuracy of image assessment at compression rates of up to 9:1.
CONCLUSION: The techniques presented for evaluation of image quality do not depend on the specific compression algorithm and provide a useful approach to evaluation of the benefits of any lossy image processing technique.},
	language = {eng},
	number = {2},
	journal = {Radiology},
	author = {Cosman, P. C. and Davidson, H. C. and Bergin, C. J. and Tseng, C. W. and Moses, L. E. and Riskin, E. A. and Olshen, R. A. and Gray, R. M.},
	month = feb,
	year = {1994},
	pmid = {8284409},
	keywords = {Humans, Image Processing, Computer-Assisted, Lung, Mediastinum, Predictive Value of Tests, Radiography, Thoracic, Sensitivity and Specificity, Tomography, X-Ray Computed},
	pages = {517--524},
}

@article{parthasarathy_visual_2023,
	title = {Visual adaptation to medical images: a comparison of digital mammography and tomosynthesis},
	volume = {10},
	issn = {2329-4302},
	shorttitle = {Visual adaptation to medical images},
	doi = {10.1117/1.JMI.10.S1.S11909},
	abstract = {PURPOSE: Radiologists and other image readers spend prolonged periods inspecting medical images. The visual system can rapidly adapt or adjust sensitivity to the images that an observer is currently viewing, and previous studies have demonstrated that this can lead to pronounced changes in the perception of mammogram images. We compared these adaptation effects for images from different imaging modalities to explore both general and modality-specific consequences of adaptation in medical image perception.
APPROACH: We measured perceptual changes induced by adaptation to images acquired by digital mammography (DM) or digital breast tomosynthesis (DBT), which have both similar and distinct textural properties. Participants (nonradiologists) adapted to images from the same patient acquired from each modality or for different patients with American College of Radiology-Breast Imaging Reporting and Data System (BI-RADS) classification of dense or fatty tissue. The participants then judged the appearance of composite images formed by blending the two adapting images (i.e., DM versus DBT or dense versus fatty in each modality).
RESULTS: Adaptation to either modality produced similar significant shifts in the perception of dense and fatty textures, reducing the salience of the adapted component in the test images. In side-by-side judgments, a modality-specific adaptation effect was not observed. However, when the images were directly fixated during adaptation and testing, so that the textural differences between the modalities were more visible, significantly different changes in the sensitivity to the noise in the images were observed.
CONCLUSIONS: These results confirm that observers can readily adapt to the visual properties or spatial textures of medical images in ways that can bias their perception of the images, and that adaptation can also be selective for the distinctive visual features of images acquired by different modalities.},
	language = {eng},
	number = {Suppl 1},
	journal = {Journal of Medical Imaging (Bellingham, Wash.)},
	author = {Parthasarathy, Mohana Kuppuswamy and Zuley, Margarita L. and Bandos, Andriy I. and Abbey, Craig K. and Webster, Michael A.},
	month = feb,
	year = {2023},
	pmid = {37114188},
	pmcid = {PMC10128168},
	keywords = {adaptation, mammography, noise perception, texture perception, tomosynthesis},
	pages = {S11909},
}

@article{riemann_european_2017,
	title = {European guideline for the diagnosis and treatment of insomnia},
	volume = {26},
	copyright = {© 2017 European Sleep Research Society},
	issn = {1365-2869},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/jsr.12594},
	doi = {10.1111/jsr.12594},
	abstract = {This European guideline for the diagnosis and treatment of insomnia was developed by a task force of the European Sleep Research Society, with the aim of providing clinical recommendations for the management of adult patients with insomnia. The guideline is based on a systematic review of relevant meta-analyses published till June 2016. The target audience for this guideline includes all clinicians involved in the management of insomnia, and the target patient population includes adults with chronic insomnia disorder. The GRADE (Grading of Recommendations Assessment, Development and Evaluation) system was used to grade the evidence and guide recommendations. The diagnostic procedure for insomnia, and its co-morbidities, should include a clinical interview consisting of a sleep history (sleep habits, sleep environment, work schedules, circadian factors), the use of sleep questionnaires and sleep diaries, questions about somatic and mental health, a physical examination and additional measures if indicated (i.e. blood tests, electrocardiogram, electroencephalogram; strong recommendation, moderate- to high-quality evidence). Polysomnography can be used to evaluate other sleep disorders if suspected (i.e. periodic limb movement disorder, sleep-related breathing disorders), in treatment-resistant insomnia, for professional at-risk populations and when substantial sleep state misperception is suspected (strong recommendation, high-quality evidence). Cognitive behavioural therapy for insomnia is recommended as the first-line treatment for chronic insomnia in adults of any age (strong recommendation, high-quality evidence). A pharmacological intervention can be offered if cognitive behavioural therapy for insomnia is not sufficiently effective or not available. Benzodiazepines, benzodiazepine receptor agonists and some antidepressants are effective in the short-term treatment of insomnia (≤4 weeks; weak recommendation, moderate-quality evidence). Antihistamines, antipsychotics, melatonin and phytotherapeutics are not recommended for insomnia treatment (strong to weak recommendations, low- to very-low-quality evidence). Light therapy and exercise need to be further evaluated to judge their usefulness in the treatment of insomnia (weak recommendation, low-quality evidence). Complementary and alternative treatments (e.g. homeopathy, acupuncture) are not recommended for insomnia treatment (weak recommendation, very-low-quality evidence).},
	language = {en},
	number = {6},
	urldate = {2025-05-08},
	journal = {Journal of Sleep Research},
	author = {Riemann, Dieter and Baglioni, Chiara and Bassetti, Claudio and Bjorvatn, Bjørn and Dolenc Groselj, Leja and Ellis, Jason G. and Espie, Colin A. and Garcia-Borreguero, Diego and Gjerstad, Michaela and Gonçalves, Marta and Hertenstein, Elisabeth and Jansson-Fröjmark, Markus and Jennum, Poul J. and Leger, Damien and Nissen, Christoph and Parrino, Liborio and Paunio, Tiina and Pevernagie, Dirk and Verbraecken, Johan and Weeß, Hans-Günter and Wichniak, Adam and Zavalko, Irina and Arnardottir, Erna S. and Deleanu, Oana-Claudia and Strazisar, Barbara and Zoetmulder, Marielle and Spiegelhalder, Kai},
	year = {2017},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/jsr.12594},
	keywords = {CBT-I, evidence-based medicine, hypnotics},
	pages = {675--700},
}

@article{van_cauter_impact_2007,
	title = {Impact of {Sleep} and {Sleep} {Loss} on {Neuroendocrine} and {Metabolic} {Function}},
	volume = {67},
	issn = {0301-0163},
	doi = {10.1159/000097543},
	abstract = {Background: Sleep exerts important modulatory effects on neuroendocrine function and glucose regulation. During the past few decades, sleep curtailment has become a very common behavior in industrialized countries. This trend toward shorter sleep times has occurred over the same time period as the dramatic increases in the prevalence of obesity and diabetes. Aims: This article will review rapidly accumulating laboratory and epidemiologic evidence indicating that chronic partial sleep loss could play a role in the current epidemics of obesity and diabetes. Conclusions: Laboratory studies in healthy young volunteers have shown that experimental sleep restriction is associated with a dysregulation of the neuroendocrine control of appetite consistent with increased hunger and with alterations in parameters of glucose tolerance suggestive of an increased risk of diabetes. Epidemiologic findings in both children and adults are consistent with the laboratory data.},
	number = {Suppl. 1},
	urldate = {2025-05-07},
	journal = {Hormone Research},
	author = {van Cauter, Eve and Holmbäck, Ulf and Knutson, Kristen and Leproult, Rachel and Miller, Annette and Nedeltcheva, Arlet and Pannain, Silvana and Penev, Plamen and Tasali, Esra and Spiegel, Karine},
	month = feb,
	year = {2007},
	pages = {2--9},
}

@article{ramar_sleep_nodate,
	title = {Sleep is essential to health: an {American} {Academy} of {Sleep} {Medicine} position statement},
	volume = {17},
	shorttitle = {Sleep is essential to health},
	url = {https://jcsm.aasm.org/doi/10.5664/jcsm.9476},
	doi = {10.5664/jcsm.9476},
	abstract = {Sleep is a biological necessity, and insufficient sleep and untreated sleep disorders are detrimental for health, well-being, and public safety. Healthy People 2030 includes several sleep-related objectives with the goal to improve health, productivity, well-being, quality of life, and safety by helping people get enough sleep. In addition to adequate sleep duration, healthy sleep requires good quality, appropriate timing, regularity, and the absence of sleep disorders. It is the position of the American Academy of Sleep Medicine (AASM) that sleep is essential to health. There is a significant need for greater emphasis on sleep health in education, clinical practice, inpatient and long-term care, public health promotion, and the workplace. More sleep and circadian research is needed to further elucidate the importance of sleep for public health and the contributions of insufficient sleep to health disparities.Citation:Ramar K, Malhotra RK, Carden KA, et al. Sleep is essential to health: an American Academy of Sleep Medicine position statement. J Clin Sleep Med. 2021;17(10):2115–2119.},
	number = {10},
	urldate = {2025-05-07},
	journal = {Journal of Clinical Sleep Medicine},
	author = {Ramar, Kannan and Malhotra, Raman K. and Carden, Kelly A. and Martin, Jennifer L. and Abbasi, -Feinberg Fariha and Aurora, R. Nisha and Kapur, Vishesh K. and Olson, Eric J. and Rosen, Carol L. and Rowley, James A. and Shelgikar, Anita V. and Trotti, Lynn Marie},
	note = {Publisher: American Academy of Sleep Medicine},
	pages = {2115--2119},
}

@misc{peebles_scalable_2023,
	title = {Scalable {Diffusion} {Models} with {Transformers}},
	url = {http://arxiv.org/abs/2212.09748},
	doi = {10.48550/arXiv.2212.09748},
	abstract = {We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass complexity as measured by Gflops. We find that DiTs with higher Gflops -- through increased transformer depth/width or increased number of input tokens -- consistently have lower FID. In addition to possessing good scalability properties, our largest DiT-XL/2 models outperform all prior diffusion models on the class-conditional ImageNet 512x512 and 256x256 benchmarks, achieving a state-of-the-art FID of 2.27 on the latter.},
	urldate = {2025-04-18},
	publisher = {arXiv},
	author = {Peebles, William and Xie, Saining},
	month = mar,
	year = {2023},
	note = {arXiv:2212.09748 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@misc{copet_simple_2023,
	title = {Simple and {Controllable} {Music} {Generation}},
	url = {https://arxiv.org/abs/2306.05284v3},
	abstract = {We tackle the task of conditional music generation. We introduce MusicGen, a single Language Model (LM) that operates over several streams of compressed discrete music representation, i.e., tokens. Unlike prior work, MusicGen is comprised of a single-stage transformer LM together with efficient token interleaving patterns, which eliminates the need for cascading several models, e.g., hierarchically or upsampling. Following this approach, we demonstrate how MusicGen can generate high-quality samples, both mono and stereo, while being conditioned on textual description or melodic features, allowing better controls over the generated output. We conduct extensive empirical evaluation, considering both automatic and human studies, showing the proposed approach is superior to the evaluated baselines on a standard text-to-music benchmark. Through ablation studies, we shed light over the importance of each of the components comprising MusicGen. Music samples, code, and models are available at https://github.com/facebookresearch/audiocraft},
	language = {en},
	urldate = {2025-03-19},
	journal = {arXiv.org},
	author = {Copet, Jade and Kreuk, Felix and Gat, Itai and Remez, Tal and Kant, David and Synnaeve, Gabriel and Adi, Yossi and Défossez, Alexandre},
	month = jun,
	year = {2023},
}

@misc{saharia_photorealistic_2022,
	title = {Photorealistic {Text}-to-{Image} {Diffusion} {Models} with {Deep} {Language} {Understanding}},
	url = {http://arxiv.org/abs/2205.11487},
	doi = {10.48550/arXiv.2205.11487},
	abstract = {We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation. Our key discovery is that generic large language models (e.g. T5), pretrained on text-only corpora, are surprisingly effective at encoding text for image synthesis: increasing the size of the language model in Imagen boosts both sample fidelity and image-text alignment much more than increasing the size of the image diffusion model. Imagen achieves a new state-of-the-art FID score of 7.27 on the COCO dataset, without ever training on COCO, and human raters find Imagen samples to be on par with the COCO data itself in image-text alignment. To assess text-to-image models in greater depth, we introduce DrawBench, a comprehensive and challenging benchmark for text-to-image models. With DrawBench, we compare Imagen with recent methods including VQ-GAN+CLIP, Latent Diffusion Models, and DALL-E 2, and find that human raters prefer Imagen over other models in side-by-side comparisons, both in terms of sample quality and image-text alignment. See https://imagen.research.google/ for an overview of the results.},
	urldate = {2025-03-14},
	publisher = {arXiv},
	author = {Saharia, Chitwan and Chan, William and Saxena, Saurabh and Li, Lala and Whang, Jay and Denton, Emily and Ghasemipour, Seyed Kamyar Seyed and Ayan, Burcu Karagol and Mahdavi, S. Sara and Lopes, Rapha Gontijo and Salimans, Tim and Ho, Jonathan and Fleet, David J. and Norouzi, Mohammad},
	month = may,
	year = {2022},
	note = {arXiv:2205.11487 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@misc{ashraf_resolution_2024,
	title = {Resolution limit of the eye: how many pixels can we see?},
	shorttitle = {Resolution limit of the eye},
	url = {http://arxiv.org/abs/2410.06068},
	doi = {10.48550/arXiv.2410.06068},
	abstract = {As large engineering efforts go towards improving the resolution of mobile, AR and VR displays, it is important to know the maximum resolution at which further improvements bring no noticeable benefit. This limit is often referred to as the "retinal resolution", although the limiting factor may not necessarily be attributed to the retina. To determine the ultimate resolution at which an image appears sharp to our eyes with no perceivable blur, we created an experimental setup with a sliding display, which allows for continuous control of the resolution. The lack of such control was the main limitation of the previous studies. We measure achromatic (black-white) and chromatic (red-green and yellow-violet) resolution limits for foveal vision, and at two eccentricities (10 and 20 deg). Our results demonstrate that the resolution limit is higher than what was previously believed, reaching 94 pixels-per-degree (ppd) for foveal achromatic vision, 89 ppd for red-green patterns, and 53 ppd for yellow-violet patterns. We also observe a much larger drop in the resolution limit for chromatic patterns (red-green and yellow-violet) than for achromatic. Our results set the north star for display development, with implications for future imaging, rendering and video coding technologies.},
	urldate = {2025-03-12},
	publisher = {arXiv},
	author = {Ashraf, Maliha and Chapiro, Alexandre and Mantiuk, Rafał K.},
	month = oct,
	year = {2024},
	note = {arXiv:2410.06068 [cs]},
	keywords = {Computer Science - Graphics, Computer Science - Human-Computer Interaction, Computer Science - Multimedia, Electrical Engineering and Systems Science - Image and Video Processing},
}

@article{mukhamediya_effect_2023,
	title = {On the {Effect} of {Log}-{Mel} {Spectrogram} {Parameter} {Tuning} for {Deep} {Learning}-{Based} {Speech} {Emotion} {Recognition}},
	volume = {11},
	issn = {2169-3536},
	doi = {10.1109/ACCESS.2023.3287093},
	abstract = {Speech emotion recognition (SER) has become a major area of investigation in human-computer interaction. Conventionally, SER is formulated as a classification problem that follows a common methodology: (i) extracting features from speech signals; and (ii) constructing an emotion classifier using extracted features. With the advent of deep learning, however, the former stage is integrated into the latter. That is to say, deep neural networks (DNNs), which are trained using log-Mel spectrograms (LMS) of audio waveforms, extract discriminative features from LMS. A critical issue, and one that is often overlooked, is that this procedure is done without relating the choice of LMS parameters to the performance of the trained DNN classifiers. It is commonplace in SER studies that practitioners assume some “usual” values for these parameters and devote major efforts to training and comparing various DNN architectures. In contrast with this common approach, in this work we choose a single lightweight pre-trained architecture, namely, SqueezeNet, and shift our main effort into tuning LMS parameters. Our empirical results using three publicly available SER datasets show that: (i) parameters of LMS can considerably affect the performance of DNNs; and (ii) by tuning LMS parameters, highly competitive classification performance can be achieved. In particular, treating LMS parameters as hyperparameters and tuning them led to 23\%, 10\%, and 11\% improvement in contrast with the use of “usual” values of LMS parameters in EmoDB, IEMOCAP, and SAVEE datasets, respectively.},
	urldate = {2025-03-09},
	journal = {IEEE Access},
	author = {Mukhamediya, Azamat and Fazli, Siamac and Zollanvari, Amin},
	year = {2023},
	note = {Conference Name: IEEE Access},
	keywords = {Computer architecture, Convolutional neural networks, Emotion recognition, Feature extraction, Log-Mel spectrogram, Spectrogram, SqueezeNet, Tuning, Windows, speech emotion recognition},
	pages = {61950--61957},
}

@misc{ning_diffrhythm_2025,
	title = {{DiffRhythm}: {Blazingly} {Fast} and {Embarrassingly} {Simple} {End}-to-{End} {Full}-{Length} {Song} {Generation} with {Latent} {Diffusion}},
	shorttitle = {{DiffRhythm}},
	url = {http://arxiv.org/abs/2503.01183},
	doi = {10.48550/arXiv.2503.01183},
	abstract = {Recent advancements in music generation have garnered significant attention, yet existing approaches face critical limitations. Some current generative models can only synthesize either the vocal track or the accompaniment track. While some models can generate combined vocal and accompaniment, they typically rely on meticulously designed multi-stage cascading architectures and intricate data pipelines, hindering scalability. Additionally, most systems are restricted to generating short musical segments rather than full-length songs. Furthermore, widely used language model-based methods suffer from slow inference speeds. To address these challenges, we propose DiffRhythm, the first latent diffusion-based song generation model capable of synthesizing complete songs with both vocal and accompaniment for durations of up to 4m45s in only ten seconds, maintaining high musicality and intelligibility. Despite its remarkable capabilities, DiffRhythm is designed to be simple and elegant: it eliminates the need for complex data preparation, employs a straightforward model structure, and requires only lyrics and a style prompt during inference. Additionally, its non-autoregressive structure ensures fast inference speeds. This simplicity guarantees the scalability of DiffRhythm. Moreover, we release the complete training code along with the pre-trained model on large-scale data to promote reproducibility and further research.},
	urldate = {2025-03-09},
	publisher = {arXiv},
	author = {Ning, Ziqian and Chen, Huakang and Jiang, Yuepeng and Hao, Chunbo and Ma, Guobin and Wang, Shuai and Yao, Jixun and Xie, Lei},
	month = mar,
	year = {2025},
	note = {arXiv:2503.01183 [eess]},
	keywords = {Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{novack_ditto_2024,
	title = {{DITTO}: {Diffusion} {Inference}-{Time} {T}-{Optimization} for {Music} {Generation}},
	shorttitle = {{DITTO}},
	url = {http://arxiv.org/abs/2401.12179},
	doi = {10.48550/arXiv.2401.12179},
	abstract = {We propose Diffusion Inference-Time T-Optimization (DITTO), a general-purpose frame-work for controlling pre-trained text-to-music diffusion models at inference-time via optimizing initial noise latents. Our method can be used to optimize through any differentiable feature matching loss to achieve a target (stylized) output and leverages gradient checkpointing for memory efficiency. We demonstrate a surprisingly wide-range of applications for music generation including inpainting, outpainting, and looping as well as intensity, melody, and musical structure control - all without ever fine-tuning the underlying model. When we compare our approach against related training, guidance, and optimization-based methods, we find DITTO achieves state-of-the-art performance on nearly all tasks, including outperforming comparable approaches on controllability, audio quality, and computational efficiency, thus opening the door for high-quality, flexible, training-free control of diffusion models. Sound examples can be found at https://DITTO-Music.github.io/web/.},
	urldate = {2025-02-28},
	publisher = {arXiv},
	author = {Novack, Zachary and McAuley, Julian and Berg-Kirkpatrick, Taylor and Bryan, Nicholas J.},
	month = jun,
	year = {2024},
	note = {arXiv:2401.12179 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{manor_zero-shot_2024,
	title = {Zero-{Shot} {Unsupervised} and {Text}-{Based} {Audio} {Editing} {Using} {DDPM} {Inversion}},
	url = {http://arxiv.org/abs/2402.10009},
	doi = {10.48550/arXiv.2402.10009},
	abstract = {Editing signals using large pre-trained models, in a zero-shot manner, has recently seen rapid advancements in the image domain. However, this wave has yet to reach the audio domain. In this paper, we explore two zero-shot editing techniques for audio signals, which use DDPM inversion with pre-trained diffusion models. The first, which we coin ZEro-shot Text-based Audio (ZETA) editing, is adopted from the image domain. The second, named ZEro-shot UnSupervized (ZEUS) editing, is a novel approach for discovering semantically meaningful editing directions without supervision. When applied to music signals, this method exposes a range of musically interesting modifications, from controlling the participation of specific instruments to improvisations on the melody. Samples and code can be found in https://hilamanor.github.io/AudioEditing/ .},
	urldate = {2025-02-28},
	publisher = {arXiv},
	author = {Manor, Hila and Michaeli, Tomer},
	month = may,
	year = {2024},
	note = {arXiv:2402.10009 [cs]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{ashraf_resolution_2024-1,
	title = {Resolution limit of the eye: how many pixels can we see?},
	shorttitle = {Resolution limit of the eye},
	url = {http://arxiv.org/abs/2410.06068},
	doi = {10.48550/arXiv.2410.06068},
	abstract = {As large engineering efforts go towards improving the resolution of mobile, AR and VR displays, it is important to know the maximum resolution at which further improvements bring no noticeable benefit. This limit is often referred to as the "retinal resolution", although the limiting factor may not necessarily be attributed to the retina. To determine the ultimate resolution at which an image appears sharp to our eyes with no perceivable blur, we created an experimental setup with a sliding display, which allows for continuous control of the resolution. The lack of such control was the main limitation of the previous studies. We measure achromatic (black-white) and chromatic (red-green and yellow-violet) resolution limits for foveal vision, and at two eccentricities (10 and 20 deg). Our results demonstrate that the resolution limit is higher than what was previously believed, reaching 94 pixels-per-degree (ppd) for foveal achromatic vision, 89 ppd for red-green patterns, and 53 ppd for yellow-violet patterns. We also observe a much larger drop in the resolution limit for chromatic patterns (red-green and yellow-violet) than for achromatic. Our results set the north star for display development, with implications for future imaging, rendering and video coding technologies.},
	urldate = {2025-02-26},
	publisher = {arXiv},
	author = {Ashraf, Maliha and Chapiro, Alexandre and Mantiuk, Rafał K.},
	month = oct,
	year = {2024},
	note = {arXiv:2410.06068 [cs]},
	keywords = {Computer Science - Graphics, Computer Science - Human-Computer Interaction, Computer Science - Multimedia, Electrical Engineering and Systems Science - Image and Video Processing},
}

@inproceedings{hromadka_kinitveraai_2023,
	address = {Toronto, Canada},
	title = {{KInITVeraAI} at {SemEval}-2023 {Task} 3: {Simple} yet {Powerful} {Multilingual} {Fine}-{Tuning} for {Persuasion} {Techniques} {Detection}},
	copyright = {All rights reserved},
	shorttitle = {{KInITVeraAI} at {SemEval}-2023 {Task} 3},
	url = {https://aclanthology.org/2023.semeval-1.86},
	doi = {10.18653/v1/2023.semeval-1.86},
	abstract = {This paper presents the best-performing solution to the SemEval 2023 Task 3 on the subtask 3 dedicated to persuasion techniques detection. Due to a high multilingual character of the input data and a large number of 23 predicted labels (causing a lack of labelled data for some language-label combinations), we opted for fine-tuning pre-trained transformer-based language models. Conducting multiple experiments, we find the best configuration, which consists of large multilingual model (XLM-RoBERTa large) trained jointly on all input data, with carefully calibrated confidence thresholds for seen and surprise languages separately. Our final system performed the best on 6 out of 9 languages (including two surprise languages) and achieved highly competitive results on the remaining three languages.},
	urldate = {2024-12-03},
	booktitle = {Proceedings of the 17th {International} {Workshop} on {Semantic} {Evaluation} ({SemEval}-2023)},
	publisher = {Association for Computational Linguistics},
	author = {Hromadka, Timo and Smolen, Timotej and Remis, Tomas and Pecher, Branislav and Srba, Ivan},
	editor = {Ojha, Atul Kr. and Doğruöz, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa},
	month = jul,
	year = {2023},
	pages = {629--637},
}

@inproceedings{pikuliak_multilingual_2023,
	address = {Singapore},
	title = {Multilingual {Previously} {Fact}-{Checked} {Claim} {Retrieval}},
	copyright = {All rights reserved},
	url = {https://aclanthology.org/2023.emnlp-main.1027},
	doi = {10.18653/v1/2023.emnlp-main.1027},
	abstract = {Fact-checkers are often hampered by the sheer amount of online content that needs to be fact-checked. NLP can help them by retrieving already existing fact-checks relevant to the content being investigated. This paper introduces a new multilingual dataset for previously fact-checked claim retrieval. We collected 28k posts in 27 languages from social media, 206k fact-checks in 39 languages written by professional fact-checkers, as well as 31k connections between these two groups. This is the most extensive and the most linguistically diverse dataset of this kind to date. We evaluated how different unsupervised methods fare on this dataset and its various dimensions. We show that evaluating such a diverse dataset has its complexities and proper care needs to be taken before interpreting the results. We also evaluated a supervised fine-tuning approach, improving upon the unsupervised method significantly.},
	urldate = {2024-12-03},
	booktitle = {Proceedings of the 2023 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Pikuliak, Matúš and Srba, Ivan and Moro, Robert and Hromadka, Timo and Smoleň, Timotej and Melišek, Martin and Vykopal, Ivan and Simko, Jakub and Podroužek, Juraj and Bielikova, Maria},
	editor = {Bouamor, Houda and Pino, Juan and Bali, Kalika},
	month = dec,
	year = {2023},
	pages = {16477--16500},
}

@article{hromadka_investigating_nodate-1,
	title = {Investigating {Diffusion} {Models} for {Music} {Generation}},
	copyright = {All rights reserved},
	language = {en},
	author = {Hromadka, Timo},
}

@article{stokes_activity-silent_2015,
	title = {‘{Activity}-silent’ working memory in prefrontal cortex: a dynamic coding framework},
	volume = {19},
	issn = {13646613},
	shorttitle = {‘{Activity}-silent’ working memory in prefrontal cortex},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1364661315001023},
	doi = {10.1016/j.tics.2015.05.004},
	language = {en},
	number = {7},
	urldate = {2024-12-01},
	journal = {Trends in Cognitive Sciences},
	author = {Stokes, Mark G.},
	month = jul,
	year = {2015},
	pages = {394--405},
}

@article{kuhl_more_2011,
	title = {More is not always better: paradoxical effects of repetition on semantic accessibility},
	volume = {18},
	copyright = {http://www.springer.com/tdm},
	issn = {1069-9384, 1531-5320},
	shorttitle = {More is not always better},
	url = {http://link.springer.com/10.3758/s13423-011-0110-0},
	doi = {10.3758/s13423-011-0110-0},
	abstract = {Repetition normally enhances memory. While in some cases the benefit of added repetition may be incremental, few would expect that massed repetition could actually reverse the benefits of brief repetition. Here we report two experiments that document a clear example of a paradoxical effect of massed repetition. Subjects first repeated words (e.g., “sheep”) aloud one at a time for 0, 5, 10, 20, or 40 s. A free association phase followed in which cues could be completed with repeated words (e.g., “herd s\_\_\_” for “sheep”) or with semantically associated words (e.g., “fabric w\_\_\_” for “wool”). Brief periods of repetition (5–10 s) resulted in priming, as would be expected based on research on repetition priming and spreading activation. Longer periods of repetition (20–40 s), however, abolished priming. Interestingly, this massed-repetition decrement was particularly robust for semantic associates of repeated words, and was evident after a 15-min delay. These findings document a paradoxical feature of the effects of rehearsal on memory: When ideas are repeated often enough, the benefits of rehearsal can actually be reversed.},
	language = {en},
	number = {5},
	urldate = {2024-12-01},
	journal = {Psychonomic Bulletin \& Review},
	author = {Kuhl, Brice A. and Anderson, Michael C.},
	month = oct,
	year = {2011},
	pages = {964--972},
}

@article{phan_automatic_2022,
	title = {Automatic sleep staging of {EEG} signals: recent development, challenges, and future directions},
	volume = {43},
	issn = {1361-6579},
	shorttitle = {Automatic sleep staging of {EEG} signals},
	doi = {10.1088/1361-6579/ac6049},
	abstract = {Modern deep learning holds a great potential to transform clinical studies of human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep-staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments. This review aims to provide the shared view of the authors on the most recent state-of-the-art developments in automatic sleep staging, the challenges that still need to be addressed, and the future directions needed for automatic sleep scoring to achieve clinical value.},
	language = {eng},
	number = {4},
	journal = {Physiological Measurement},
	author = {Phan, Huy and Mikkelsen, Kaare},
	month = apr,
	year = {2022},
	pmid = {35320788},
	keywords = {EEG, Electroencephalography, Humans, Polysomnography, Sleep, Sleep Stages, automatic sleep staging, deep learning, deep neural networks, sleep monitoring, sleep scoring},
}

@article{malafeev_automatic_2018,
	title = {Automatic {Human} {Sleep} {Stage} {Scoring} {Using} {Deep} {Neural} {Networks}},
	volume = {12},
	issn = {1662-453X},
	url = {https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2018.00781/full},
	doi = {10.3389/fnins.2018.00781},
	abstract = {{\textless}p{\textgreater}The classification of sleep stages is the first and an important step in the quantitative analysis of polysomnographic recordings. Sleep stage scoring relies heavily on visual pattern recognition by a human expert and is time consuming and subjective. Thus, there is a need for automatic classification. In this work we developed machine learning algorithms for sleep classification: random forest (RF) classification based on features and artificial neural networks (ANNs) working both with features and raw data. We tested our methods in healthy subjects and in patients. Most algorithms yielded good results comparable to human interrater agreement. Our study revealed that deep neural networks (DNNs) working with raw data performed better than feature-based methods. We also demonstrated that taking the local temporal structure of sleep into account a priori is important. Our results demonstrate the utility of neural network architectures for the classification of sleep.{\textless}/p{\textgreater}},
	language = {English},
	urldate = {2024-11-28},
	journal = {Frontiers in Neuroscience},
	author = {Malafeev, Alexander and Laptev, Dmitry and Bauer, Stefan and Omlin, Ximena and Wierzbicka, Aleksandra and Wichniak, Adam and Jernajczyk, Wojciech and Riener, Robert and Buhmann, Joachim and Achermann, Peter},
	month = nov,
	year = {2018},
	note = {Publisher: Frontiers},
	keywords = {Automatic scoring, EEG, Sleep, deep learning, machine learning},
}

@article{kavurmaci_effect_2020,
	title = {Effect of {Music} {Therapy} on {Sleep} {Quality}},
	volume = {26},
	issn = {1078-6791},
	abstract = {CONTEXT: One of the important functions of nurses is to prepare a relaxing environment for patients, for whom they look after and fulfill sleep needs. Today, music therapy, one of nonpharmacological treatment approaches, can be used for enhancing the sleep quality of individuals. The increase of individuals' sleep quality by using music therapy will enable nurses to intervene in problems associated with sleep disorders and prevent insomnia and other relevant problems.
OBJECTIVE: This study was conducted to determine the effect of music therapy on sleep quality of the students.
DESIGN: The research was an experimental pretest-posttest control group design.
SETTING: The research was conducted among the students studying in the nursing department at a university in the east of Turkey in the spring term of the academic year of 2015-2016.
PARTICIPANT: Students who (1) got a total score of 5 and higher according to the Pittsburgh Sleep Quality Index (PSQI), (2) had no neurological/psychiatric disorder, (3) had no hearing problem, (4) received no medical treatment regarding sleep, and (5) volunteered to participate in the study were included in the study. The students who were included in the study were divided into experimental and control groups.
INVENTION: Students in the experimental group were instructed to keep the volume of the personal MP3 player to be used for listening to the music therapy below 70\% and to use it for 1 h/d at most. No intervention was applied to students in the control group. They were asked to sustain their normal sleeping habits.
OUTCOME MEASURE: To collect the data, a questionnaire prepared by the researchers for determining sociodemographic characteristics of the students and PSQI were used.
RESULTS: As a result of the study, it was found that the posttest PSQI mean scores of students in the experimental group, listening to music therapy, were lower than the mean scores of the students in the control group, and the difference between the groups was statistically significant.
CONCLUSIONS: As a result of this study, it was determined that music therapy increased the sleep quality of students. Being one of the nonpharmacological treatment approaches intended for solving sleep problems, music therapy is a pain-free, safe, and affordable treatment method without any side effect that could be used in every area of health.},
	language = {eng},
	number = {4},
	journal = {Alternative Therapies in Health and Medicine},
	author = {Kavurmaci, Mehtap and Dayapoğlu, Nuray and Tan, Mehtap},
	month = jul,
	year = {2020},
	pmid = {31221932},
	keywords = {Adult, Female, Humans, Male, Music, Music Therapy, Sleep, Sleep Initiation and Maintenance Disorders, Sleep Wake Disorders, Students, Turkey, Young Adult},
	pages = {22--26},
}

@article{harmat_music_2008,
	title = {Music improves sleep quality in students},
	volume = {62},
	issn = {1365-2648},
	doi = {10.1111/j.1365-2648.2008.04602.x},
	abstract = {AIM: This paper is a report of a study to investigate the effects of music on sleep quality in young participants with poor sleep.
BACKGROUND: Sleep disorders may result in fatigue, tiredness, depression and problems in daytime functioning. Music can reduce sympathetic nervous system activity, decrease anxiety, blood pressure, heart and respiratory rate and may have positive effects on sleep via muscle relaxation and distraction from thoughts. Control groups have not been used in most previous studies.
METHODS: We used a three-group repeated measures design. Ninety-four students (aged between 19 and 28 years) with sleep complaints were studied in 2006. Participants listened for 45 minutes either to relaxing classical music (Group 1) or an audiobook (Group 2) at bedtime for 3 weeks. The control group (Group 3) received no intervention. Sleep quality was measured using the Pittsburg Sleep Quality Index before the study and weekly during the intervention. Depressive symptoms in experimental group participants were measured using the Beck Depression Inventory.
RESULTS: Repeated measures anova revealed a main effect of TIME (P {\textless} 0.0001) and an interaction between TIME and GROUPS (P {\textless} 0.0001). Post hoc tests with Bonferroni correction showed that music statistically significantly improved sleep quality (P {\textless} 0.0001). Sleep quality did not improve statistically significantly in the audiobook and the control group. Depressive symptoms decreased statistically significantly in the music group (P {\textless} 0.0001), but not in the group listening to audiobooks.
CONCLUSION: Relaxing classical music is an effective intervention in reducing sleeping problems. Nurses could use this safe, cheap and easy to learn method to treat insomnia.},
	language = {eng},
	number = {3},
	journal = {Journal of Advanced Nursing},
	author = {Harmat, László and Takács, Johanna and Bódizs, Róbert},
	month = may,
	year = {2008},
	pmid = {18426457},
	keywords = {Adult, Compact Disks, Depression, Female, Humans, Male, Music Therapy, Psychiatric Status Rating Scales, Sleep Initiation and Maintenance Disorders, Statistics as Topic, Students, Surveys and Questionnaires},
	pages = {327--335},
}

@article{kavurmaci_effect_2020-1,
	title = {Effect of {Music} {Therapy} on {Sleep} {Quality}},
	volume = {26},
	issn = {1078-6791},
	abstract = {CONTEXT: One of the important functions of nurses is to prepare a relaxing environment for patients, for whom they look after and fulfill sleep needs. Today, music therapy, one of nonpharmacological treatment approaches, can be used for enhancing the sleep quality of individuals. The increase of individuals' sleep quality by using music therapy will enable nurses to intervene in problems associated with sleep disorders and prevent insomnia and other relevant problems.
OBJECTIVE: This study was conducted to determine the effect of music therapy on sleep quality of the students.
DESIGN: The research was an experimental pretest-posttest control group design.
SETTING: The research was conducted among the students studying in the nursing department at a university in the east of Turkey in the spring term of the academic year of 2015-2016.
PARTICIPANT: Students who (1) got a total score of 5 and higher according to the Pittsburgh Sleep Quality Index (PSQI), (2) had no neurological/psychiatric disorder, (3) had no hearing problem, (4) received no medical treatment regarding sleep, and (5) volunteered to participate in the study were included in the study. The students who were included in the study were divided into experimental and control groups.
INVENTION: Students in the experimental group were instructed to keep the volume of the personal MP3 player to be used for listening to the music therapy below 70\% and to use it for 1 h/d at most. No intervention was applied to students in the control group. They were asked to sustain their normal sleeping habits.
OUTCOME MEASURE: To collect the data, a questionnaire prepared by the researchers for determining sociodemographic characteristics of the students and PSQI were used.
RESULTS: As a result of the study, it was found that the posttest PSQI mean scores of students in the experimental group, listening to music therapy, were lower than the mean scores of the students in the control group, and the difference between the groups was statistically significant.
CONCLUSIONS: As a result of this study, it was determined that music therapy increased the sleep quality of students. Being one of the nonpharmacological treatment approaches intended for solving sleep problems, music therapy is a pain-free, safe, and affordable treatment method without any side effect that could be used in every area of health.},
	language = {eng},
	number = {4},
	journal = {Alternative Therapies in Health and Medicine},
	author = {Kavurmaci, Mehtap and Dayapoğlu, Nuray and Tan, Mehtap},
	month = jul,
	year = {2020},
	pmid = {31221932},
	keywords = {Adult, Female, Humans, Male, Music, Music Therapy, Sleep, Sleep Initiation and Maintenance Disorders, Sleep Wake Disorders, Students, Turkey, Young Adult},
	pages = {22--26},
}

@inproceedings{sarkar_should_2023,
	address = {Hamburg Germany},
	title = {Should {Computers} {Be} {Easy} {To} {Use}? {Questioning} the {Doctrine} of {Simplicity} in {User} {Interface} {Design}},
	isbn = {978-1-4503-9422-2},
	shorttitle = {Should {Computers} {Be} {Easy} {To} {Use}?},
	url = {https://dl.acm.org/doi/10.1145/3544549.3582741},
	doi = {10.1145/3544549.3582741},
	abstract = {That computers should be easy to learn and use is a rarely-questioned tenet of user interface design. But what do we gain from prioritising usability and learnability, and what do we lose? I explore how simplicity is not an inevitable truth of user interface design, but rather contingent on a series of events in the evolution of software. Not only does a rigid adherence to this doctrine place an artificial ceiling on the power and flexibility of software, but it is also culturally relative, privileging certain information cultures over others. I propose that for feature-rich software, negotiated complexity is a better target than simplicity, and we must revisit the ill-regarded relationship between learning, documentation, and software.},
	language = {en},
	urldate = {2024-11-28},
	booktitle = {Extended {Abstracts} of the 2023 {CHI} {Conference} on {Human} {Factors} in {Computing} {Systems}},
	publisher = {ACM},
	author = {Sarkar, Advait},
	month = apr,
	year = {2023},
	pages = {1--10},
}

@article{liu_automatic_nodate,
	title = {Automatic {Sleep} {Quality} {Evaluation} for {Insomnia} through {Single}-{Channel} {Electroencephalogram}},
	author = {Liu, Haochen},
}

@article{buysse_pittsburgh_1989,
	title = {The {Pittsburgh} {Sleep} {Quality} {Index}: a new instrument for psychiatric practice and research},
	volume = {28},
	issn = {0165-1781},
	shorttitle = {The {Pittsburgh} {Sleep} {Quality} {Index}},
	doi = {10.1016/0165-1781(89)90047-4},
	abstract = {Despite the prevalence of sleep complaints among psychiatric patients, few questionnaires have been specifically designed to measure sleep quality in clinical populations. The Pittsburgh Sleep Quality Index (PSQI) is a self-rated questionnaire which assesses sleep quality and disturbances over a 1-month time interval. Nineteen individual items generate seven "component" scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The sum of scores for these seven components yields one global score. Clinical and clinimetric properties of the PSQI were assessed over an 18-month period with "good" sleepers (healthy subjects, n = 52) and "poor" sleepers (depressed patients, n = 54; sleep-disorder patients, n = 62). Acceptable measures of internal homogeneity, consistency (test-retest reliability), and validity were obtained. A global PSQI score greater than 5 yielded a diagnostic sensitivity of 89.6\% and specificity of 86.5\% (kappa = 0.75, p less than 0.001) in distinguishing good and poor sleepers. The clinimetric and clinical properties of the PSQI suggest its utility both in psychiatric clinical practice and research activities.},
	language = {eng},
	number = {2},
	journal = {Psychiatry Research},
	author = {Buysse, D. J. and Reynolds, C. F. and Monk, T. H. and Berman, S. R. and Kupfer, D. J.},
	month = may,
	year = {1989},
	pmid = {2748771},
	keywords = {Adult, Aged, Aged, 80 and over, Depression, Female, Humans, Male, Middle Aged, Psychological Tests, Psychometrics, Sleep Initiation and Maintenance Disorders, Sleep Stages},
	pages = {193--213},
}

@article{lai-tan_toward_2023,
	title = {Toward {Personalized} {Music}-{Therapy}: {A} {Neurocomputational} {Modeling} {Perspective}},
	volume = {22},
	issn = {1558-2590},
	shorttitle = {Toward {Personalized} {Music}-{Therapy}},
	url = {https://ieeexplore.ieee.org/document/10170750/?arnumber=10170750},
	doi = {10.1109/MPRV.2023.3285087},
	abstract = {Music therapy has emerged recently as a successful intervention that improves patient outcomes in a large range of neurological and mood disorders without adverse effects. Brain networks are entrained to music in ways that can be explained both via top-down and bottom-up processes. In particular, the direct interaction of auditory with the motor and the reward system via a predictive framework explains the efficacy of music-based interventions in motor rehabilitation. In this article, we provide a brief overview of current theories of music perception and processing. Subsequently, we summarize the evidence of music-based interventions primarily in motor, emotional, and cardiovascular regulation. We highlight opportunities to improve the quality of life and reduce the stress beyond the clinic environment and in healthy individuals. This relatively unexplored area requires an understanding of how we can personalize and automate music selection processes to fit individual needs and tasks via feedback loops mediated by measurements of neurophysiological responses.},
	number = {3},
	urldate = {2024-11-27},
	journal = {IEEE Pervasive Computing},
	author = {Lai-Tan, Nicole and Philiastides, Marios G. and Kawsar, Fahim and Deligianni, Fani},
	month = jul,
	year = {2023},
	note = {Conference Name: IEEE Pervasive Computing},
	keywords = {Adaptation models, Brain modeling, Medical treatment, Oscillators, Predictive models, Rhythm, Synchronization},
	pages = {27--37},
}

@misc{lan_high_2024,
	title = {High {Fidelity} {Text}-{Guided} {Music} {Editing} via {Single}-{Stage} {Flow} {Matching}},
	url = {http://arxiv.org/abs/2407.03648},
	doi = {10.48550/arXiv.2407.03648},
	abstract = {We introduce MelodyFlow, an efficient text-controllable high-fidelity music generation and editing model. It operates on continuous latent representations from a low frame rate 48 kHz stereo variational auto encoder codec. Based on a diffusion transformer architecture trained on a flow-matching objective the model can edit diverse high quality stereo samples of variable duration, with simple text descriptions. We adapt the ReNoise latent inversion method to flow matching and compare it with the original implementation and naive denoising diffusion implicit model (DDIM) inversion on a variety of music editing prompts. Our results indicate that our latent inversion outperforms both ReNoise and DDIM for zero-shot test-time text-guided editing on several objective metrics. Subjective evaluations exhibit a substantial improvement over previous state of the art for music editing. Code and model weights will be publicly made available. Samples are available at https://melodyflow.github.io.},
	urldate = {2024-11-12},
	publisher = {arXiv},
	author = {Lan, Gael Le and Shi, Bowen and Ni, Zhaoheng and Srinivasan, Sidd and Kumar, Anurag and Ellis, Brian and Kant, David and Nagaraja, Varun and Chang, Ernie and Hsu, Wei-Ning and Shi, Yangyang and Chandra, Vikas},
	month = oct,
	year = {2024},
	note = {arXiv:2407.03648},
	keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{chen_musicldm_2023,
	title = {{MusicLDM}: {Enhancing} {Novelty} in {Text}-to-{Music} {Generation} {Using} {Beat}-{Synchronous} {Mixup} {Strategies}},
	shorttitle = {{MusicLDM}},
	url = {http://arxiv.org/abs/2308.01546},
	doi = {10.48550/arXiv.2308.01546},
	abstract = {Diffusion models have shown promising results in cross-modal generation tasks, including text-to-image and text-to-audio generation. However, generating music, as a special type of audio, presents unique challenges due to limited availability of music data and sensitive issues related to copyright and plagiarism. In this paper, to tackle these challenges, we first construct a state-of-the-art text-to-music model, MusicLDM, that adapts Stable Diffusion and AudioLDM architectures to the music domain. We achieve this by retraining the contrastive language-audio pretraining model (CLAP) and the Hifi-GAN vocoder, as components of MusicLDM, on a collection of music data samples. Then, to address the limitations of training data and to avoid plagiarism, we leverage a beat tracking model and propose two different mixup strategies for data augmentation: beat-synchronous audio mixup and beat-synchronous latent mixup, which recombine training audio directly or via a latent embeddings space, respectively. Such mixup strategies encourage the model to interpolate between musical training samples and generate new music within the convex hull of the training data, making the generated music more diverse while still staying faithful to the corresponding style. In addition to popular evaluation metrics, we design several new evaluation metrics based on CLAP score to demonstrate that our proposed MusicLDM and beat-synchronous mixup strategies improve both the quality and novelty of generated music, as well as the correspondence between input text and generated music.},
	urldate = {2024-11-08},
	publisher = {arXiv},
	author = {Chen, Ke and Wu, Yusong and Liu, Haohe and Nezhurina, Marianna and Berg-Kirkpatrick, Taylor and Dubnov, Shlomo},
	month = aug,
	year = {2023},
	note = {arXiv:2308.01546},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{zeghidour_soundstream_2021,
	title = {{SoundStream}: {An} {End}-to-{End} {Neural} {Audio} {Codec}},
	shorttitle = {{SoundStream}},
	url = {http://arxiv.org/abs/2107.03312},
	abstract = {We present SoundStream, a novel neural audio codec that can efficiently compress speech, music and general audio at bitrates normally targeted by speech-tailored codecs. SoundStream relies on a model architecture composed by a fully convolutional encoder/decoder network and a residual vector quantizer, which are trained jointly end-to-end. Training leverages recent advances in text-to-speech and speech enhancement, which combine adversarial and reconstruction losses to allow the generation of high-quality audio content from quantized embeddings. By training with structured dropout applied to quantizer layers, a single model can operate across variable bitrates from 3kbps to 18kbps, with a negligible quality loss when compared with models trained at fixed bitrates. In addition, the model is amenable to a low latency implementation, which supports streamable inference and runs in real time on a smartphone CPU. In subjective evaluations using audio at 24kHz sampling rate, SoundStream at 3kbps outperforms Opus at 12kbps and approaches EVS at 9.6kbps. Moreover, we are able to perform joint compression and enhancement either at the encoder or at the decoder side with no additional latency, which we demonstrate through background noise suppression for speech.},
	urldate = {2024-11-08},
	publisher = {arXiv},
	author = {Zeghidour, Neil and Luebs, Alejandro and Omran, Ahmed and Skoglund, Jan and Tagliasacchi, Marco},
	month = jul,
	year = {2021},
	note = {arXiv:2107.03312},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{noauthor_soundstream_nodate,
	title = {{SoundStream}: {An} {End}-to-{End} {Neural} {Audio} {Codec}},
	shorttitle = {{SoundStream}},
	url = {http://research.google/blog/soundstream-an-end-to-end-neural-audio-codec/},
	abstract = {Posted by Neil Zeghidour, Research Scientist and Marco Tagliasacchi, Staff Research Scientist, Google Research Audio codecs are used to efficiently...},
	language = {en},
	urldate = {2024-11-08},
}

@misc{noauthor_announcing_nodate,
	title = {Announcing {Audiogen} {Codec} {\textbar} {Audiogen} {Blog}},
	url = {https://www.audiogen.co/blog/announcing-audiogen-codec},
	abstract = {A low compression 48khz stereo neural audio codec for general audio, optimizing for audio fidelity.},
	language = {en},
	urldate = {2024-11-08},
	journal = {Audiogen},
}

@misc{du_funcodec_2023,
	title = {{FunCodec}: {A} {Fundamental}, {Reproducible} and {Integrable} {Open}-source {Toolkit} for {Neural} {Speech} {Codec}},
	shorttitle = {{FunCodec}},
	url = {http://arxiv.org/abs/2309.07405},
	doi = {10.48550/arXiv.2309.07405},
	abstract = {This paper presents FunCodec, a fundamental neural speech codec toolkit, which is an extension of the open-source speech processing toolkit FunASR. FunCodec provides reproducible training recipes and inference scripts for the latest neural speech codec models, such as SoundStream and Encodec. Thanks to the unified design with FunASR, FunCodec can be easily integrated into downstream tasks, such as speech recognition. Along with FunCodec, pre-trained models are also provided, which can be used for academic or generalized purposes. Based on the toolkit, we further propose the frequency-domain codec models, FreqCodec, which can achieve comparable speech quality with much lower computation and parameter complexity. Experimental results show that, under the same compression ratio, FunCodec can achieve better reconstruction quality compared with other toolkits and released models. We also demonstrate that the pre-trained models are suitable for downstream tasks, including automatic speech recognition and personalized text-to-speech synthesis. This toolkit is publicly available at https://github.com/alibaba-damo-academy/FunCodec.},
	urldate = {2024-11-08},
	publisher = {arXiv},
	author = {Du, Zhihao and Zhang, Shiliang and Hu, Kai and Zheng, Siqi},
	month = oct,
	year = {2023},
	note = {arXiv:2309.07405},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{noauthor_210703312_nodate,
	title = {[2107.03312] {SoundStream}: {An} {End}-to-{End} {Neural} {Audio} {Codec}},
	url = {https://arxiv.org/abs/2107.03312},
	urldate = {2024-11-08},
}

@misc{wu_codec-superb_2024,
	title = {Codec-{SUPERB} @ {SLT} 2024: {A} lightweight benchmark for neural audio codec models},
	shorttitle = {Codec-{SUPERB} @ {SLT} 2024},
	url = {http://arxiv.org/abs/2409.14085},
	abstract = {Neural audio codec models are becoming increasingly important as they serve as tokenizers for audio, enabling efficient transmission or facilitating speech language modeling. The ideal neural audio codec should maintain content, paralinguistics, speaker characteristics, and audio information even at low bitrates. Recently, numerous advanced neural codec models have been proposed. However, codec models are often tested under varying experimental conditions. As a result, we introduce the Codec-SUPERB challenge at SLT 2024, designed to facilitate fair and lightweight comparisons among existing codec models and inspire advancements in the field. This challenge brings together representative speech applications and objective metrics, and carefully selects license-free datasets, sampling them into small sets to reduce evaluation computation costs. This paper presents the challenge's rules, datasets, five participant systems, results, and findings.},
	urldate = {2024-11-08},
	publisher = {arXiv},
	author = {Wu, Haibin and Chen, Xuanjun and Lin, Yi-Cheng and Chang, Kaiwei and Du, Jiawei and Lu, Ke-Han and Liu, Alexander H. and Chung, Ho-Lam and Wu, Yuan-Kuei and Yang, Dongchao and Liu, Songxiang and Wu, Yi-Chiao and Tan, Xu and Glass, James and Watanabe, Shinji and Lee, Hung-yi},
	month = sep,
	year = {2024},
	note = {arXiv:2409.14085 
version: 1},
	keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{wu_towards_2024,
	title = {Towards audio language modeling -- an overview},
	url = {http://arxiv.org/abs/2402.13236},
	abstract = {Neural audio codecs are initially introduced to compress audio data into compact codes to reduce transmission latency. Researchers recently discovered the potential of codecs as suitable tokenizers for converting continuous audio into discrete codes, which can be employed to develop audio language models (LMs). Numerous high-performance neural audio codecs and codec-based LMs have been developed. The paper aims to provide a thorough and systematic overview of the neural audio codec models and codec-based LMs.},
	urldate = {2024-11-08},
	publisher = {arXiv},
	author = {Wu, Haibin and Chen, Xuanjun and Lin, Yi-Cheng and Chang, Kai-wei and Chung, Ho-Lam and Liu, Alexander H. and Lee, Hung-yi},
	month = feb,
	year = {2024},
	note = {arXiv:2402.13236 
version: 1},
	keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{defossez_decoding_2023,
	title = {Decoding speech perception from non-invasive brain recordings},
	url = {http://arxiv.org/abs/2208.12266},
	doi = {10.48550/arXiv.2208.12266},
	abstract = {Decoding speech from brain activity is a long-awaited goal in both healthcare and neuroscience. Invasive devices have recently led to major milestones in that regard: deep learning algorithms trained on intracranial recordings now start to decode elementary linguistic features (e.g. letters, words, spectrograms). However, extending this approach to natural speech and non-invasive brain recordings remains a major challenge. Here, we introduce a model trained with contrastive-learning to decode self-supervised representations of perceived speech from the non-invasive recordings of a large cohort of healthy individuals. To evaluate this approach, we curate and integrate four public datasets, encompassing 175 volunteers recorded with magneto- or electro-encephalography (M/EEG), while they listened to short stories and isolated sentences. The results show that our model can identify, from 3 seconds of MEG signals, the corresponding speech segment with up to 41\% accuracy out of more than 1,000 distinct possibilities on average across participants, and more than 80\% in the very best participants - a performance that allows the decoding of words and phrases absent from the training set. The comparison of our model to a variety of baselines highlights the importance of (i) a contrastive objective, (ii) pretrained representations of speech and (iii) a common convolutional architecture simultaneously trained across multiple participants. Finally, the analysis of the decoder's predictions suggests that they primarily depend on lexical and contextual semantic representations. Overall, this effective decoding of perceived speech from non-invasive recordings delineates a promising path to decode language from brain activity, without putting patients at risk for brain surgery.},
	urldate = {2024-11-08},
	publisher = {arXiv},
	author = {Défossez, Alexandre and Caucheteux, Charlotte and Rapin, Jérémy and Kabeli, Ori and King, Jean-Rémi},
	month = oct,
	year = {2023},
	note = {arXiv:2208.12266},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing, Quantitative Biology - Neurons and Cognition},
}

@misc{ma_foundation_2024,
	title = {Foundation {Models} for {Music}: {A} {Survey}},
	shorttitle = {Foundation {Models} for {Music}},
	url = {http://arxiv.org/abs/2408.14340},
	doi = {10.48550/arXiv.2408.14340},
	abstract = {In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This comprehensive review examines state-of-the-art (SOTA) pre-trained models and foundation models in music, spanning from representation learning, generative learning and multimodal learning. We first contextualise the significance of music in various industries and trace the evolution of AI in music. By delineating the modalities targeted by foundation models, we discover many of the music representations are underexplored in FM development. Then, emphasis is placed on the lack of versatility of previous methods on diverse music applications, along with the potential of FMs in music understanding, generation and medical application. By comprehensively exploring the details of the model pre-training paradigm, architectural choices, tokenisation, finetuning methodologies and controllability, we emphasise the important topics that should have been well explored, like instruction tuning and in-context learning, scaling law and emergent ability, as well as long-sequence modelling etc. A dedicated section presents insights into music agents, accompanied by a thorough analysis of datasets and evaluations essential for pre-training and downstream tasks. Finally, by underscoring the vital importance of ethical considerations, we advocate that following research on FM for music should focus more on such issues as interpretability, transparency, human responsibility, and copyright issues. The paper offers insights into future challenges and trends on FMs for music, aiming to shape the trajectory of human-AI collaboration in the music realm.},
	urldate = {2024-11-07},
	publisher = {arXiv},
	author = {Ma, Yinghao and Øland, Anders and Ragni, Anton and Sette, Bleiz MacSen Del and Saitis, Charalampos and Donahue, Chris and Lin, Chenghua and Plachouras, Christos and Benetos, Emmanouil and Shatri, Elona and Morreale, Fabio and Zhang, Ge and Fazekas, György and Xia, Gus and Zhang, Huan and Manco, Ilaria and Huang, Jiawen and Guinot, Julien and Lin, Liwei and Marinelli, Luca and Lam, Max W. Y. and Sharma, Megha and Kong, Qiuqiang and Dannenberg, Roger B. and Yuan, Ruibin and Wu, Shangda and Wu, Shih-Lun and Dai, Shuqi and Lei, Shun and Kang, Shiyin and Dixon, Simon and Chen, Wenhu and Huang, Wenhao and Du, Xingjian and Qu, Xingwei and Tan, Xu and Li, Yizhi and Tian, Zeyue and Wu, Zhiyong and Wu, Zhizheng and Ma, Ziyang and Wang, Ziyu},
	month = sep,
	year = {2024},
	note = {arXiv:2408.14340},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@inproceedings{jiang_transformer_2020,
	title = {Transformer {VAE}: {A} {Hierarchical} {Model} for {Structure}-{Aware} and {Interpretable} {Music} {Representation} {Learning}},
	shorttitle = {Transformer {VAE}},
	doi = {10.1109/ICASSP40776.2020.9054554},
	abstract = {Structure awareness and interpretability are two of the most desired properties of music generation algorithms. Structure-aware models generate more natural and coherent music with long-term dependencies, while interpretable models are more friendly for human-computer interaction and co-creation. To achieve these two goals simultaneously, we designed the Transformer Variational AutoEncoder, a hierarchical model that unifies the efforts of two recent breakthroughs in deep music generation: 1) the Music Transformer and 2) Deep Music Analogy. The former learns long-term dependencies using attention mechanism, and the latter learns interpretable latent representations using a disentangled conditional-VAE. We showed that Transformer VAE is essentially capable of learning a context-sensitive hierarchical representation, regarding local representations as the context and the dependencies among the local representations as the global structure. By interacting with the model, we can achieve context transfer, realizing the imaginary situation of "what if" a piece is developed following the music flow of another piece.},
	urldate = {2024-11-07},
	booktitle = {{ICASSP} 2020 - 2020 {IEEE} {International} {Conference} on {Acoustics}, {Speech} and {Signal} {Processing} ({ICASSP})},
	author = {Jiang, Junyan and Xia, Gus G. and Carlton, Dave B. and Anderson, Chris N. and Miyakawa, Ryan H.},
	month = may,
	year = {2020},
	note = {ISSN: 2379-190X},
	keywords = {Acoustics, Conferences, Context modeling, Human computer interaction, Representation learning, Signal processing, Signal processing algorithms, Speech processing, Transformer, VAE, music structure},
	pages = {516--520},
}

@article{zhang_learning_2023,
	title = {Learning {Adversarial} {Transformer} for {Symbolic} {Music} {Generation}},
	volume = {34},
	issn = {2162-2388},
	doi = {10.1109/TNNLS.2020.2990746},
	abstract = {Symbolic music generation is still an unsettled problem facing several challenges. The complete music score is a quite long note sequence, which consists of multiple tracks with recurring elements and their variants at various levels. The transformer model, benefiting from its self-attention has shown advantages in modeling long sequences. There have been some attempts at applying the transformer-based model to music generation. However, previous works train the model using the same strategy as the text generation task, despite the obvious differences between the pattern of texts and musics. These models cannot consistently produce music samples of high quality. In this article, we propose a novel adversarial transformer to generate transformer to generate music pieces with high musicality. The generative adversarial learning and the self-attention networks are combined creatively. The generation of long sequence is guided by the adversarial objectives, which provides a strong regularization to enforce the transformer to focus on learning of the global and local structures. Instead of adopting the time-consuming Monte Carlo (MC) search method that is commonly used in the existing sequence generative models, we propose an effective and convenient method to compute the reward for each generated step (REGS) for the long sequence. The discriminator is trained to optimize the elaborately designed global and local loss objective functions simultaneously, which enables the discriminator to give reliable REGS for the generator. The adversarial objective combined with the teacher forcing objective is used to guide the training of the generator. The proposed model can be used to generate single-track or multitrack music pieces. Experiments show that our model can generate long music pieces with the improved quality compared with the original music transformers.},
	number = {4},
	urldate = {2024-11-07},
	journal = {IEEE Transactions on Neural Networks and Learning Systems},
	author = {Zhang, Ning},
	month = apr,
	year = {2023},
	note = {Conference Name: IEEE Transactions on Neural Networks and Learning Systems},
	keywords = {Adversarial learning, Bars, Computational modeling, Computer architecture, Generators, Learning systems, Task analysis, Training, long sequence generation, music generation, self-attention, transformer},
	pages = {1754--1763},
}

@article{guan_gan_2019,
	title = {A {GAN} {Model} {With} {Self}-attention {Mechanism} {To} {Generate} {Multi}-instruments {Symbolic} {Music}},
	copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
	url = {https://ieeexplore.ieee.org/document/8852291/},
	doi = {10.1109/IJCNN.2019.8852291},
	abstract = {GAN has recently been proved to be able to generate symbolic music in the form of piano-rolls. However, those existing GAN-based multi-track music generation methods are always unstable. Moreover, due to defects in the temporal features extraction, the generated multi-track music does not sound natural enough. Therefore, we propose a new GAN model with self-attention mechanism, DMB-GAN, which can extract more temporal features of music to generate multi-instruments music stably. First of all, to generate more consistent and natural single-track music, we introduce self-attention mechanism to enable GAN-based music generation model to extract not only spatial features but also temporal features. Secondly, to generate multi-instruments music with harmonic structure among all tracks, we construct a dual generative adversarial architecture with multi-branches, each branch for one track. Finally, to improve generated quality of multi-instruments symbolic music, we introduce switchable normalization to stabilize network training. The experimental results show that DMB-GAN can stably generate coherent, natural multi-instruments music with good quality.},
	urldate = {2024-11-07},
	journal = {2019 International Joint Conference on Neural Networks (IJCNN)},
	author = {Guan, Faqian and Yu, Chunyan and Yang, Suqiong},
	month = jul,
	year = {2019},
	note = {Conference Name: 2019 International Joint Conference on Neural Networks (IJCNN)
ISBN: 9781728119854
Place: Budapest, Hungary
Publisher: IEEE},
	pages = {1--6},
}

@misc{donahue_lakhnes_2019,
	title = {{LakhNES}: {Improving} multi-instrumental music generation with cross-domain pre-training},
	shorttitle = {{LakhNES}},
	url = {http://arxiv.org/abs/1907.04868},
	doi = {10.48550/arXiv.1907.04868},
	abstract = {We are interested in the task of generating multi-instrumental music scores. The Transformer architecture has recently shown great promise for the task of piano score generation; here we adapt it to the multi-instrumental setting. Transformers are complex, high-dimensional language models which are capable of capturing long-term structure in sequence data, but require large amounts of data to fit. Their success on piano score generation is partially explained by the large volumes of symbolic data readily available for that domain. We leverage the recently-introduced NES-MDB dataset of four-instrument scores from an early video game sound synthesis chip (the NES), which we find to be well-suited to training with the Transformer architecture. To further improve the performance of our model, we propose a pre-training technique to leverage the information in a large collection of heterogeneous music, namely the Lakh MIDI dataset. Despite differences between the two corpora, we find that this transfer learning procedure improves both quantitative and qualitative performance for our primary task.},
	urldate = {2024-11-07},
	publisher = {arXiv},
	author = {Donahue, Chris and Mao, Huanru Henry and Li, Yiting Ethan and Cottrell, Garrison W. and McAuley, Julian},
	month = jul,
	year = {2019},
	note = {arXiv:1907.04868},
	keywords = {Computer Science - Machine Learning, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning},
}

@misc{levy_controllable_2023,
	title = {Controllable {Music} {Production} with {Diffusion} {Models} and {Guidance} {Gradients}},
	url = {http://arxiv.org/abs/2311.00613},
	doi = {10.48550/arXiv.2311.00613},
	abstract = {We demonstrate how conditional generation from diffusion models can be used to tackle a variety of realistic tasks in the production of music in 44.1kHz stereo audio with sampling-time guidance. The scenarios we consider include continuation, inpainting and regeneration of musical audio, the creation of smooth transitions between two different music tracks, and the transfer of desired stylistic characteristics to existing audio clips. We achieve this by applying guidance at sampling time in a simple framework that supports both reconstruction and classification losses, or any combination of the two. This approach ensures that generated audio can match its surrounding context, or conform to a class distribution or latent representation specified relative to any suitable pre-trained classifier or embedding model. Audio samples are available at https://machinelearning.apple.com/research/controllable-music},
	urldate = {2024-11-07},
	publisher = {arXiv},
	author = {Levy, Mark and Giorgi, Bruno Di and Weers, Floris and Katharopoulos, Angelos and Nickson, Tom},
	month = dec,
	year = {2023},
	note = {arXiv:2311.00613},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{levy_controllable_2023-1,
	title = {Controllable {Music} {Production} with {Diffusion} {Models} and {Guidance} {Gradients}},
	url = {http://arxiv.org/abs/2311.00613},
	doi = {10.48550/arXiv.2311.00613},
	abstract = {We demonstrate how conditional generation from diffusion models can be used to tackle a variety of realistic tasks in the production of music in 44.1kHz stereo audio with sampling-time guidance. The scenarios we consider include continuation, inpainting and regeneration of musical audio, the creation of smooth transitions between two different music tracks, and the transfer of desired stylistic characteristics to existing audio clips. We achieve this by applying guidance at sampling time in a simple framework that supports both reconstruction and classification losses, or any combination of the two. This approach ensures that generated audio can match its surrounding context, or conform to a class distribution or latent representation specified relative to any suitable pre-trained classifier or embedding model. Audio samples are available at https://machinelearning.apple.com/research/controllable-music},
	urldate = {2024-11-07},
	publisher = {arXiv},
	author = {Levy, Mark and Giorgi, Bruno Di and Weers, Floris and Katharopoulos, Angelos and Nickson, Tom},
	month = dec,
	year = {2023},
	note = {arXiv:2311.00613},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{yu_museformer_2022,
	title = {Museformer: {Transformer} with {Fine}- and {Coarse}-{Grained} {Attention} for {Music} {Generation}},
	shorttitle = {Museformer},
	url = {http://arxiv.org/abs/2210.10349},
	abstract = {Symbolic music generation aims to generate music scores automatically. A recent trend is to use Transformer or its variants in music generation, which is, however, suboptimal, because the full attention cannot efficiently model the typically long music sequences (e.g., over 10,000 tokens), and the existing models have shortcomings in generating musical repetition structures. In this paper, we propose Museformer, a Transformer with a novel fine- and coarse-grained attention for music generation. Specifically, with the fine-grained attention, a token of a specific bar directly attends to all the tokens of the bars that are most relevant to music structures (e.g., the previous 1st, 2nd, 4th and 8th bars, selected via similarity statistics); with the coarse-grained attention, a token only attends to the summarization of the other bars rather than each token of them so as to reduce the computational cost. The advantages are two-fold. First, it can capture both music structure-related correlations via the fine-grained attention, and other contextual information via the coarse-grained attention. Second, it is efficient and can model over 3X longer music sequences compared to its full-attention counterpart. Both objective and subjective experimental results demonstrate its ability to generate long music sequences with high quality and better structures.},
	urldate = {2024-11-07},
	publisher = {arXiv},
	author = {Yu, Botao and Lu, Peiling and Wang, Rui and Hu, Wei and Tan, Xu and Ye, Wei and Zhang, Shikun and Qin, Tao and Liu, Tie-Yan},
	month = oct,
	year = {2022},
	note = {arXiv:2210.10349},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{huang_music_2018,
	title = {Music {Transformer}},
	url = {http://arxiv.org/abs/1809.04281},
	doi = {10.48550/arXiv.1809.04281},
	abstract = {Music relies heavily on repetition to build structure and meaning. Self-reference occurs on multiple timescales, from motifs to phrases to reusing of entire sections of music, such as in pieces with ABA structure. The Transformer (Vaswani et al., 2017), a sequence model based on self-attention, has achieved compelling results in many generation tasks that require maintaining long-range coherence. This suggests that self-attention might also be well-suited to modeling music. In musical composition and performance, however, relative timing is critically important. Existing approaches for representing relative positional information in the Transformer modulate attention based on pairwise distance (Shaw et al., 2018). This is impractical for long sequences such as musical compositions since their memory complexity for intermediate relative information is quadratic in the sequence length. We propose an algorithm that reduces their intermediate memory requirement to linear in the sequence length. This enables us to demonstrate that a Transformer with our modified relative attention mechanism can generate minute-long compositions (thousands of steps, four times the length modeled in Oore et al., 2018) with compelling structure, generate continuations that coherently elaborate on a given motif, and in a seq2seq setup generate accompaniments conditioned on melodies. We evaluate the Transformer with our relative attention mechanism on two datasets, JSB Chorales and Piano-e-Competition, and obtain state-of-the-art results on the latter.},
	urldate = {2024-11-07},
	publisher = {arXiv},
	author = {Huang, Cheng-Zhi Anna and Vaswani, Ashish and Uszkoreit, Jakob and Shazeer, Noam and Simon, Ian and Hawthorne, Curtis and Dai, Andrew M. and Hoffman, Matthew D. and Dinculescu, Monica and Eck, Douglas},
	month = dec,
	year = {2018},
	note = {arXiv:1809.04281},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning},
}

@misc{qu_mupt_2024,
	title = {{MuPT}: {A} {Generative} {Symbolic} {Music} {Pretrained} {Transformer}},
	shorttitle = {{MuPT}},
	url = {http://arxiv.org/abs/2404.06393},
	doi = {10.48550/arXiv.2404.06393},
	abstract = {In this paper, we explore the application of Large Language Models (LLMs) to the pre-training of music. While the prevalent use of MIDI in music modeling is well-established, our findings suggest that LLMs are inherently more compatible with ABC Notation, which aligns more closely with their design and strengths, thereby enhancing the model's performance in musical composition. To address the challenges associated with misaligned measures from different tracks during generation, we propose the development of a Synchronized Multi-Track ABC Notation (SMT-ABC Notation), which aims to preserve coherence across multiple musical tracks. Our contributions include a series of models capable of handling up to 8192 tokens, covering 90\% of the symbolic music data in our training set. Furthermore, we explore the implications of the Symbolic Music Scaling Law (SMS Law) on model performance. The results indicate a promising direction for future research in music generation, offering extensive resources for community-led research through our open-source contributions.},
	urldate = {2024-11-07},
	publisher = {arXiv},
	author = {Qu, Xingwei and Bai, Yuelin and Ma, Yinghao and Zhou, Ziya and Lo, Ka Man and Liu, Jiaheng and Yuan, Ruibin and Min, Lejun and Liu, Xueling and Zhang, Tianyu and Du, Xinrun and Guo, Shuyue and Liang, Yiming and Li, Yizhi and Wu, Shangda and Zhou, Junting and Zheng, Tianyu and Ma, Ziyang and Han, Fengze and Xue, Wei and Xia, Gus and Benetos, Emmanouil and Yue, Xiang and Lin, Chenghua and Tan, Xu and Huang, Stephen W. and Fu, Jie and Zhang, Ge},
	month = nov,
	year = {2024},
	note = {arXiv:2404.06393},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{ji_comprehensive_2020,
	title = {A {Comprehensive} {Survey} on {Deep} {Music} {Generation}: {Multi}-level {Representations}, {Algorithms}, {Evaluations}, and {Future} {Directions}},
	shorttitle = {A {Comprehensive} {Survey} on {Deep} {Music} {Generation}},
	url = {http://arxiv.org/abs/2011.06801},
	abstract = {The utilization of deep learning techniques in generating various contents (such as image, text, etc.) has become a trend. Especially music, the topic of this paper, has attracted widespread attention of countless researchers.The whole process of producing music can be divided into three stages, corresponding to the three levels of music generation: score generation produces scores, performance generation adds performance characteristics to the scores, and audio generation converts scores with performance characteristics into audio by assigning timbre or generates music in audio format directly. Previous surveys have explored the network models employed in the field of automatic music generation. However, the development history, the model evolution, as well as the pros and cons of same music generation task have not been clearly illustrated. This paper attempts to provide an overview of various composition tasks under different music generation levels, covering most of the currently popular music generation tasks using deep learning. In addition, we summarize the datasets suitable for diverse tasks, discuss the music representations, the evaluation methods as well as the challenges under different levels, and finally point out several future directions.},
	urldate = {2024-11-07},
	publisher = {arXiv},
	author = {Ji, Shulei and Luo, Jing and Yang, Xinyu},
	month = nov,
	year = {2020},
	note = {arXiv:2011.06801},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@article{wang_review_2024,
	title = {A review of intelligent music generation systems},
	volume = {36},
	issn = {1433-3058},
	url = {https://doi.org/10.1007/s00521-024-09418-2},
	doi = {10.1007/s00521-024-09418-2},
	abstract = {With the introduction of ChatGPT, the public’s perception of AI-generated content has begun to reshape. Artificial intelligence has significantly reduced the barrier to entry for non-professionals in creative endeavors, enhancing the efficiency of content creation. Recent advancements have seen significant improvements in the quality of symbolic music generation, which is enabled by the use of modern generative algorithms to extract patterns implicit in a piece of music based on rule constraints or a musical corpus. Nevertheless, existing literature reviews tend to present a conventional and conservative perspective on future development trajectories, with a notable absence of thorough benchmarking of generative models. This paper provides a survey and analysis of recent intelligent music generation techniques, outlining their respective characteristics and discussing existing methods for evaluation. Additionally, the paper compares the different characteristics of music generation techniques in the East and West as well as analysing the field’s development prospects.},
	language = {en},
	number = {12},
	urldate = {2024-11-07},
	journal = {Neural Computing and Applications},
	author = {Wang, Lei and Zhao, Ziyi and Liu, Hanwei and Pang, Junwei and Qin, Yi and Wu, Qidi},
	month = apr,
	year = {2024},
	keywords = {AI-generated content, Artificial Intelligence, Automatic composition, Music representations, Symbolic music generation},
	pages = {6381--6401},
}

@misc{huang_pop_2020,
	title = {Pop {Music} {Transformer}: {Beat}-based {Modeling} and {Generation} of {Expressive} {Pop} {Piano} {Compositions}},
	shorttitle = {Pop {Music} {Transformer}},
	url = {http://arxiv.org/abs/2002.00212},
	doi = {10.48550/arXiv.2002.00212},
	abstract = {A great number of deep learning based models have been recently proposed for automatic music composition. Among these models, the Transformer stands out as a prominent approach for generating expressive classical piano performance with a coherent structure of up to one minute. The model is powerful in that it learns abstractions of data on its own, without much human-imposed domain knowledge or constraints. In contrast with this general approach, this paper shows that Transformers can do even better for music modeling, when we improve the way a musical score is converted into the data fed to a Transformer model. In particular, we seek to impose a metrical structure in the input data, so that Transformers can be more easily aware of the beat-bar-phrase hierarchical structure in music. The new data representation maintains the flexibility of local tempo changes, and provides hurdles to control the rhythmic and harmonic structure of music. With this approach, we build a Pop Music Transformer that composes Pop piano music with better rhythmic structure than existing Transformer models.},
	urldate = {2024-11-07},
	publisher = {arXiv},
	author = {Huang, Yu-Siang and Yang, Yi-Hsuan},
	month = aug,
	year = {2020},
	note = {arXiv:2002.00212},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning},
}

@misc{prajwal_musicflow_2024,
	title = {{MusicFlow}: {Cascaded} {Flow} {Matching} for {Text} {Guided} {Music} {Generation}},
	shorttitle = {{MusicFlow}},
	url = {http://arxiv.org/abs/2410.20478},
	abstract = {We introduce MusicFlow, a cascaded text-to-music generation model based on flow matching. Based on self-supervised representations to bridge between text descriptions and music audios, we construct two flow matching networks to model the conditional distribution of semantic and acoustic features. Additionally, we leverage masked prediction as the training objective, enabling the model to generalize to other tasks such as music infilling and continuation in a zero-shot manner. Experiments on MusicCaps reveal that the music generated by MusicFlow exhibits superior quality and text coherence despite being over \$2{\textbackslash}sim5\$ times smaller and requiring \$5\$ times fewer iterative steps. Simultaneously, the model can perform other music generation tasks and achieves competitive performance in music infilling and continuation. Our code and model will be publicly available.},
	urldate = {2024-11-07},
	publisher = {arXiv},
	author = {Prajwal, K. R. and Shi, Bowen and Lee, Matthew and Vyas, Apoorv and Tjandra, Andros and Luthra, Mahi and Guo, Baishan and Wang, Huiyu and Afouras, Triantafyllos and Kant, David and Hsu, Wei-Ning},
	month = oct,
	year = {2024},
	note = {arXiv:2410.20478},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{wang_whole-song_2024,
	title = {Whole-{Song} {Hierarchical} {Generation} of {Symbolic} {Music} {Using} {Cascaded} {Diffusion} {Models}},
	url = {http://arxiv.org/abs/2405.09901},
	doi = {10.48550/arXiv.2405.09901},
	abstract = {Recent deep music generation studies have put much emphasis on long-term generation with structures. However, we are yet to see high-quality, well-structured whole-song generation. In this paper, we make the first attempt to model a full music piece under the realization of compositional hierarchy. With a focus on symbolic representations of pop songs, we define a hierarchical language, in which each level of hierarchy focuses on the semantics and context dependency at a certain music scope. The high-level languages reveal whole-song form, phrase, and cadence, whereas the low-level languages focus on notes, chords, and their local patterns. A cascaded diffusion model is trained to model the hierarchical language, where each level is conditioned on its upper levels. Experiments and analysis show that our model is capable of generating full-piece music with recognizable global verse-chorus structure and cadences, and the music quality is higher than the baselines. Additionally, we show that the proposed model is controllable in a flexible way. By sampling from the interpretable hierarchical languages or adjusting pre-trained external representations, users can control the music flow via various features such as phrase harmonic structures, rhythmic patterns, and accompaniment texture.},
	urldate = {2024-11-07},
	publisher = {arXiv},
	author = {Wang, Ziyu and Min, Lejun and Xia, Gus},
	month = may,
	year = {2024},
	note = {arXiv:2405.09901},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@article{perez-pozuelo_future_2020,
	title = {The future of sleep health: a data-driven revolution in sleep science and medicine},
	volume = {3},
	copyright = {2020 The Author(s)},
	issn = {2398-6352},
	shorttitle = {The future of sleep health},
	url = {https://www.nature.com/articles/s41746-020-0244-4},
	doi = {10.1038/s41746-020-0244-4},
	abstract = {In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human–computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-of-the-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.},
	language = {en},
	number = {1},
	urldate = {2024-11-06},
	journal = {npj Digital Medicine},
	author = {Perez-Pozuelo, Ignacio and Zhai, Bing and Palotti, Joao and Mall, Raghvendra and Aupetit, Michaël and Garcia-Gomez, Juan M. and Taheri, Shahrad and Guan, Yu and Fernandez-Luque, Luis},
	month = mar,
	year = {2020},
	note = {Publisher: Nature Publishing Group},
	keywords = {Biomedical engineering, Diagnostic markers, Predictive markers, Preventive medicine, Sleep},
	pages = {1--15},
}

@article{watson_artificial_2021,
	title = {Artificial intelligence and sleep: {Advancing} sleep medicine},
	volume = {59},
	issn = {1087-0792},
	shorttitle = {Artificial intelligence and sleep},
	url = {https://www.sciencedirect.com/science/article/pii/S1087079221000976},
	doi = {10.1016/j.smrv.2021.101512},
	abstract = {Artificial intelligence (AI) allows analysis of “big data” combining clinical, environmental and laboratory based objective measures to allow a deeper understanding of sleep and sleep disorders. This development has the potential to transform sleep medicine in coming years to the betterment of patient care and our collective understanding of human sleep. This review addresses the current state of the field starting with a broad definition of the various components and analytic methods deployed in AI. We review examples of AI use in screening, endotyping, diagnosing, and treating sleep disorders and place this in the context of precision/personalized sleep medicine. We explore the opportunities for AI to both facilitate and extend providers’ clinical impact and present ethical considerations regarding AI derived prognostic information. We cover early adopting specialties of AI in the clinical realm, such as radiology and pathology, to provide a road map for the challenges sleep medicine is likely to face when deploying this technology. Finally, we discuss pitfalls to ensure clinical AI implementation proceeds in the safest and most effective manner possible.},
	urldate = {2024-11-06},
	journal = {Sleep Medicine Reviews},
	author = {Watson, Nathaniel F. and Fernandez, Christopher R.},
	month = oct,
	year = {2021},
	keywords = {Artificial intelligence, Diagnosis, Endotype, Machine learning, Pharmacoepigenomics, Pharmacogenomics, Polysomnography, Sleep, Treatment},
	pages = {101512},
}

@article{goktepe-kavis_sleep_2024,
	title = {Sleep {Research} in the {Era} of {AI}},
	volume = {8},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2514-183X},
	url = {https://www.mdpi.com/2514-183X/8/1/13},
	doi = {10.3390/ctn8010013},
	abstract = {The field of sleep research is both broad and rapidly evolving. It spans from the diagnosis of sleep-related disorders to investigations of how sleep supports memory consolidation. The study of sleep includes a variety of approaches, starting with the sole focus on the visual interpretation of polysomnography characteristics and extending to the emergent use of advanced signal processing tools. Insights gained using artificial intelligence (AI) are rapidly reshaping the understanding of sleep-related disorders, enabling new approaches to basic neuroscientific studies. In this opinion article, we explore the emergent role of AI in sleep research, along two different axes: one clinical and one fundamental. In clinical research, we emphasize the use of AI for automated sleep scoring, diagnosing sleep-wake disorders and assessing measurements from wearable devices. In fundamental research, we highlight the use of AI to better understand the functional role of sleep in consolidating memories. While AI is likely to facilitate new advances in the field of sleep research, we also address challenges, such as bridging the gap between AI innovation and the clinic and mitigating inherent biases in AI models. AI has already contributed to major advances in the field of sleep research, and mindful deployment has the potential to enable further progress in the understanding of the neuropsychological benefits and functions of sleep.},
	language = {en},
	number = {1},
	urldate = {2024-11-06},
	journal = {Clinical and Translational Neuroscience},
	author = {Göktepe-Kavis, Pinar and Aellen, Florence M. and Alnes, Sigurd L. and Tzovara, Athina},
	month = mar,
	year = {2024},
	note = {Number: 1
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {artificial intelligence, electroencephalography, polysomnography, sleep research},
	pages = {13},
}

@book{blackwell_live_2022,
	title = {Live {Coding}: {A} {User}'s {Manual}},
	isbn = {978-0-262-37263-3},
	shorttitle = {Live {Coding}},
	url = {https://direct.mit.edu/books/oa-monograph/5495/Live-CodingA-User-s-Manual},
	abstract = {The first comprehensive introduction to the origins, aspirations, and evolution of live coding.Performative, improvised, on the fly: live coding is about h},
	language = {en},
	urldate = {2024-10-24},
	publisher = {The MIT Press},
	author = {Blackwell, Alan F. and Cocker, Emma and Cox, Geoff and McLean, Alex and Magnusson, Thor},
	month = nov,
	year = {2022},
	doi = {10.7551/mitpress/13770.001.0001},
}

@misc{santos_adaptive_2022,
	title = {An adaptive music generation architecture for games based on the deep learning {Transformer} mode},
	url = {http://arxiv.org/abs/2207.01698},
	doi = {10.48550/arXiv.2207.01698},
	abstract = {This paper presents an architecture for generating music for video games based on the Transformer deep learning model. Our motivation is to be able to customize the generation according to the taste of the player, who can select a corpus of training examples, corresponding to his preferred musical style. The system generates various musical layers, following the standard layering strategy currently used by composers designing video game music. To adapt the music generated to the game play and to the player(s) situation, we are using an arousal-valence model of emotions, in order to control the selection of musical layers. We discuss current limitations and prospects for the future, such as collaborative and interactive control of the musical components.},
	urldate = {2024-10-16},
	publisher = {arXiv},
	author = {Santos, Gustavo Amaral Costa dos and Baffa, Augusto and Briot, Jean-Pierre and Feijó, Bruno and Furtado, Antonio Luz},
	month = sep,
	year = {2022},
	note = {arXiv:2207.01698},
	keywords = {Computer Science - Machine Learning, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{chen_fastsag_2024,
	title = {{FastSAG}: {Towards} {Fast} {Non}-{Autoregressive} {Singing} {Accompaniment} {Generation}},
	shorttitle = {{FastSAG}},
	url = {http://arxiv.org/abs/2405.07682},
	doi = {10.48550/arXiv.2405.07682},
	abstract = {Singing Accompaniment Generation (SAG), which generates instrumental music to accompany input vocals, is crucial to developing human-AI symbiotic art creation systems. The state-of-the-art method, SingSong, utilizes a multi-stage autoregressive (AR) model for SAG, however, this method is extremely slow as it generates semantic and acoustic tokens recursively, and this makes it impossible for real-time applications. In this paper, we aim to develop a Fast SAG method that can create high-quality and coherent accompaniments. A non-AR diffusion-based framework is developed, which by carefully designing the conditions inferred from the vocal signals, generates the Mel spectrogram of the target accompaniment directly. With diffusion and Mel spectrogram modeling, the proposed method significantly simplifies the AR token-based SingSong framework, and largely accelerates the generation. We also design semantic projection, prior projection blocks as well as a set of loss functions, to ensure the generated accompaniment has semantic and rhythm coherence with the vocal signal. By intensive experimental studies, we demonstrate that the proposed method can generate better samples than SingSong, and accelerate the generation by at least 30 times. Audio samples and code are available at https://fastsag.github.io/.},
	urldate = {2024-10-16},
	publisher = {arXiv},
	author = {Chen, Jianyi and Xue, Wei and Tan, Xu and Ye, Zhen and Liu, Qifeng and Guo, Yike},
	month = may,
	year = {2024},
	note = {arXiv:2405.07682},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{karger_forecastbench_2024,
	title = {{ForecastBench}: {A} {Dynamic} {Benchmark} of {AI} {Forecasting} {Capabilities}},
	shorttitle = {{ForecastBench}},
	url = {http://arxiv.org/abs/2409.19839},
	doi = {10.48550/arXiv.2409.19839},
	abstract = {Forecasts of future events are essential inputs into informed decision-making. Machine learning (ML) systems have the potential to deliver forecasts at scale, but there is no framework for evaluating the accuracy of ML systems on a standardized set of forecasting questions. To address this gap, we introduce ForecastBench: a dynamic benchmark that evaluates the accuracy of ML systems on an automatically generated and regularly updated set of 1,000 forecasting questions. To avoid any possibility of data leakage, ForecastBench is comprised solely of questions about future events that have no known answer at the time of submission. We quantify the ability of current ML systems by collecting forecasts from expert (human) forecasters, the general public, and LLMs on a random subset of questions from the benchmark (N = 200). While LLMs have achieved super-human performance on many benchmarks, they perform less well here: expert forecasters outperform the top-performing LLM (p-values {\textless}= 0.01). We display system and human scores in a public leaderboard at www.forecastbench.org.},
	urldate = {2024-10-13},
	publisher = {arXiv},
	author = {Karger, Ezra and Bastani, Houtan and Yueh-Han, Chen and Jacobs, Zachary and Halawi, Danny and Zhang, Fred and Tetlock, Philip E.},
	month = sep,
	year = {2024},
	note = {arXiv:2409.19839},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{noauthor_forecastbench_nodate,
	title = {{ForecastBench} - a dynamic, continuously-updated forecasting {LLM} {Benchmark}},
	url = {https://www.forecastbench.org/},
	urldate = {2024-10-13},
}

@misc{schoenegger_wisdom_2024,
	title = {Wisdom of the {Silicon} {Crowd}: {LLM} {Ensemble} {Prediction} {Capabilities} {Rival} {Human} {Crowd} {Accuracy}},
	shorttitle = {Wisdom of the {Silicon} {Crowd}},
	url = {http://arxiv.org/abs/2402.19379},
	doi = {10.48550/arXiv.2402.19379},
	abstract = {Human forecasting accuracy in practice relies on the 'wisdom of the crowd' effect, in which predictions about future events are significantly improved by aggregating across a crowd of individual forecasters. Past work on the forecasting ability of large language models (LLMs) suggests that frontier LLMs, as individual forecasters, underperform compared to the gold standard of a human crowd forecasting tournament aggregate. In Study 1, we expand this research by using an LLM ensemble approach consisting of a crowd of twelve LLMs. We compare the aggregated LLM predictions on 31 binary questions to that of a crowd of 925 human forecasters from a three-month forecasting tournament. Our preregistered main analysis shows that the LLM crowd outperforms a simple no-information benchmark and is not statistically different from the human crowd. In exploratory analyses, we find that these two approaches are equivalent with respect to medium-effect-size equivalence bounds. We also observe an acquiescence effect, with mean model predictions being significantly above 50\%, despite an almost even split of positive and negative resolutions. Moreover, in Study 2, we test whether LLM predictions (of GPT-4 and Claude 2) can be improved by drawing on human cognitive output. We find that both models' forecasting accuracy benefits from exposure to the median human prediction as information, improving accuracy by between 17\% and 28\%: though this leads to less accurate predictions than simply averaging human and machine forecasts. Our results suggest that LLMs can achieve forecasting accuracy rivaling that of human crowd forecasting tournaments: via the simple, practically applicable method of forecast aggregation. This replicates the 'wisdom of the crowd' effect for LLMs, and opens up their use for a variety of applications throughout society.},
	urldate = {2024-10-13},
	publisher = {arXiv},
	author = {Schoenegger, Philipp and Tuminauskaite, Indre and Park, Peter S. and Tetlock, Philip E.},
	month = jul,
	year = {2024},
	note = {arXiv:2402.19379},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Machine Learning},
}

@misc{halawi_approaching_2024,
	title = {Approaching {Human}-{Level} {Forecasting} with {Language} {Models}},
	url = {http://arxiv.org/abs/2402.18563},
	abstract = {Forecasting future events is important for policy and decision making. In this work, we study whether language models (LMs) can forecast at the level of competitive human forecasters. Towards this goal, we develop a retrieval-augmented LM system designed to automatically search for relevant information, generate forecasts, and aggregate predictions. To facilitate our study, we collect a large dataset of questions from competitive forecasting platforms. Under a test set published after the knowledge cut-offs of our LMs, we evaluate the end-to-end performance of our system against the aggregates of human forecasts. On average, the system nears the crowd aggregate of competitive forecasters, and in some settings surpasses it. Our work suggests that using LMs to forecast the future could provide accurate predictions at scale and help to inform institutional decision making.},
	urldate = {2024-10-13},
	publisher = {arXiv},
	author = {Halawi, Danny and Zhang, Fred and Yueh-Han, Chen and Steinhardt, Jacob},
	month = feb,
	year = {2024},
	note = {arXiv:2402.18563 
version: 1},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Information Retrieval, Computer Science - Machine Learning},
}

@misc{huang_symbolic_2024,
	title = {Symbolic {Music} {Generation} with {Non}-{Differentiable} {Rule} {Guided} {Diffusion}},
	url = {http://arxiv.org/abs/2402.14285},
	abstract = {We study the problem of symbolic music generation (e.g., generating piano rolls), with a technical focus on non-differentiable rule guidance. Musical rules are often expressed in symbolic form on note characteristics, such as note density or chord progression, many of which are non-differentiable which pose a challenge when using them for guided diffusion. We propose Stochastic Control Guidance (SCG), a novel guidance method that only requires forward evaluation of rule functions that can work with pre-trained diffusion models in a plug-and-play way, thus achieving trainingfree guidance for non-differentiable rules for the first time. Additionally, we introduce a latent diffusion architecture for symbolic music generation with high time resolution, which can be composed with SCG in a plug-and-play fashion. Compared to standard strong baselines in symbolic music generation, this framework demonstrates marked advancements in music quality and rulebased controllability, outperforming current stateof-the-art generators in a variety of settings. For detailed demonstrations, code and model checkpoints, please visit our project website.},
	language = {en},
	urldate = {2024-10-02},
	publisher = {arXiv},
	author = {Huang, Yujia and Ghatare, Adishree and Liu, Yuanzhe and Hu, Ziniu and Zhang, Qinsheng and Sastry, Chandramouli S. and Gururani, Siddharth and Oore, Sageev and Yue, Yisong},
	month = sep,
	year = {2024},
	note = {arXiv:2402.14285 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@article{yamasato_how_2020,
	title = {How {Prescribed} {Music} and {Preferred} {Music} {Influence} {Sleep} {Quality} in {University} {Students}},
	volume = {45},
	issn = {2185-2243},
	abstract = {OBJECTIVE: The purpose of this study was to investigate the effect of listening to music on sleep quality in university students and to explore the influence of preferred music on this effect.
METHODS: University students identified by the Pittsburgh Sleep Quality Index (PSQI) as 'poor' sleepers (global score {\textgreater} 5) were assigned to three groups (a prescribed music group, preferred music group, and no-music group). During the period of intervention, the prescribed music group and preferred music group listened to their assigned type of music at home every day at bedtime.
RESULTS: Global PSQI scores after the intervention were significantly lower in the prescribed music group and preferred music group, but there was no significant reduction in the no-music group. Between the designated sedative music group and the each subject's preferred music group, both the PSQI score showed significant differences in several items, but variations were found in the results.
CONCLUSIONS: This study confirms that listening to music improves sleep quality, even among university students in Japan. Sleep quality was improved in both prescribed and preferred music groups, although the groups gave different responses to specific PSQI components, which suggests that sleep is potentially affected by music type.},
	language = {eng},
	number = {4},
	journal = {The Tokai Journal of Experimental and Clinical Medicine},
	author = {Yamasato, Ami and Kondo, Mayu and Hoshino, Shunya and Kikuchi, Jun and Ikeuchi, Mayumi and Yamazaki, Kiyoyuki and Okino, Shigeki and Yamamoto, Kenji},
	month = dec,
	year = {2020},
	pmid = {33300592},
	keywords = {Adolescent, Adult, Female, Humans, Japan, Male, Music, Music Therapy, Sleep, Sleep Initiation and Maintenance Disorders, Students, Surveys and Questionnaires, Universities, Young Adult},
	pages = {207--213},
}

@article{chang_effects_2012,
	title = {The effects of music on the sleep quality of adults with chronic insomnia using evidence from polysomnographic and self-reported analysis: a randomized control trial},
	volume = {49},
	issn = {1873-491X},
	shorttitle = {The effects of music on the sleep quality of adults with chronic insomnia using evidence from polysomnographic and self-reported analysis},
	doi = {10.1016/j.ijnurstu.2012.02.019},
	abstract = {BACKGROUND: Research-based evidence supports the therapeutic use of music to improve the sleep quality measured by self-reported questionnaires. However, scientific knowledge of the effects of music measured using standard polysomnography in chronic insomnia adults is currently insufficient.
OBJECTIVES: The objective of this study was to evaluate the effect of soothing music on objective and subjective sleep quality in adults with chronic insomnia.
METHODS: Fifty participants were enrolled in a randomized controlled trial conducted in the sleep laboratory of a hospital, with 25 participants allocated to the music group and 25 to the control group. For four days, the experimental group was exposed to soothing music selected by the participants or researchers for 45 min at nocturnal sleep time, whereas the control group was not exposed to music. Sleep was measured using polysomnography (PSG) and self-reported questionnaires. A general estimating equation was applied to analyze the data.
RESULTS: After controlling for baseline data, the music group had significantly better scores for rested rating (p=0.01), shortened stage 2 sleep (p=0.03), and prolonged REM sleep (p=0.04) compared to the control group, shown by the generalized estimating equations. However, there was no evidence of the effectiveness of music on other sleep parameters as measured by PSG. Additional findings indicate no difference in sleep quality between those who listened to their own preferred music (n=10) and those who listened to music selected by the researchers (n=15).
CONCLUSION: The results contribute to knowledge of the effectiveness of music as a therapy to improve sleep quality in adults experiencing insomnia. Listening to soothing music at nocturnal sleep time improved the rested rating scores, shortened stage 2 sleep, and prolonged REM sleep, but has little effect on sleep quality as measured by polysomnography and self-reported questionnaires.},
	language = {eng},
	number = {8},
	journal = {International Journal of Nursing Studies},
	author = {Chang, En-Ting and Lai, Hui-Ling and Chen, Pin-Wen and Hsieh, Yuan-Mei and Lee, Li-Hua},
	month = aug,
	year = {2012},
	pmid = {22494532},
	keywords = {Adult, Chronic Disease, Female, Humans, Male, Middle Aged, Music Therapy, Patient Satisfaction, Polysomnography, Sleep Initiation and Maintenance Disorders, Taiwan},
	pages = {921--930},
}

@article{dickson_music_2020,
	title = {Music on {Prescription} to {Aid} {Sleep} {Quality}: {A} {Literature} {Review}},
	volume = {11},
	issn = {1664-1078},
	shorttitle = {Music on {Prescription} to {Aid} {Sleep} {Quality}},
	doi = {10.3389/fpsyg.2020.01695},
	abstract = {{\textless}p{\textgreater}Research on the efficacy of music for improving sleep quality has produced mixed results. We investigated whether the number of music dosages could be a reason for the lack of clarity. Six longitudinal music sleep studies using the Pittsburgh Sleep Quality Index (PSQI) over 3 weeks were identified. Music when compared to active (audiobooks or medication) or passive controls significantly improved (improvement is reflected by a lower score) PSQI within the first or second week of prescription. The improvement was an average mean difference of −1.15 ({\textless}italic{\textgreater}SD{\textless}/italic{\textgreater} = 0.53) for each week. Music dosages continued to be associated with improved PSQI over a study that had a 3-month music intervention. One study with a low initial PSQI score resulted in poor sleepers (PSQI \&gt; 5) achieving healthy sleep (PSQI \&lt; 5) within 3 weeks of regular music intervention. For future studies, “prescribing” music beyond 3 weeks may lead to more instances of healthy sleep, particularly for those who have mild sleep problems. To explain the findings, we proposed that the relationship between weeks of music listening and improved PSQI are attributed to the truncation of poor bedtime habits linked to ruminative tendencies and consequent hyperarousal prior to the music intervention. Music listening at bedtime replaces those bad habits, we argue, by forming a new psychological link between bedtime and sleep through evaluative conditioning. The findings of the present study provide disarming evidence of the potential for prescription of music for treating mild sleep disorder.{\textless}/p{\textgreater}},
	language = {English},
	urldate = {2024-09-27},
	journal = {Frontiers in Psychology},
	author = {Dickson, Gaelen Thomas and Schubert, Emery},
	month = jul,
	year = {2020},
	note = {Publisher: Frontiers},
	keywords = {Arts on Prescription, Music, Sleep, conditioning, insomnia, review, therapy},
}

@article{urponen_self-evaluations_1988,
	title = {Self-evaluations of factors promoting and disturbing sleep: an epidemiological survey in {Finland}},
	volume = {26},
	issn = {0277-9536},
	shorttitle = {Self-evaluations of factors promoting and disturbing sleep},
	doi = {10.1016/0277-9536(88)90313-9},
	abstract = {The purpose of this epidemiological survey (N = 1600) was to describe the factors which middle-aged urban people in Finland perceived as promoting or disturbing sleep. The response rate was 75\%. The results suggested that quality of sleep is determined by numerous factors; social and psychological factors, health status, external sleeping conditions, life style and living habits. Every third respondent felt that exercise had a positive impact on sleep. Second in importance were reading and listening to music. Furthermore, sauna, shower and bath, stability in life, psychological factors, positive experience in work, satisfactory sexual life and good and quiet sleeping environment were reported to have positive effects on sleep. Men considered work-related pressure and fatigue (20\%) as the most important factor disturbing falling asleep or quality of sleep. In women's ranking work problems appeared no sooner than in the third place. Women reported worries, interpersonal problems, and marital and family discord as the most disturbing factors to sleep (37\%). Coffee in the evening had a negative effect on falling asleep. Although a 'nightcap' was considered to improve relaxation on falling sleep, men ranked alcohol as the fourth disturbing factor. Other disturbing factors were stress, irregularities in everyday life because of social events, travelling or atypical catnaps. Eating and exercising too heavily or too late in the evening were found to disturb sleep. On the other hand, temporary lack of exercise seemed to impair the quality of sleep. As external factors disturbing sleep the subjects considered noise light, too high room temperature, tight clothing, unfamiliar sleeping environment and restless children.(ABSTRACT TRUNCATED AT 250 WORDS)},
	language = {eng},
	number = {4},
	journal = {Social Science \& Medicine (1982)},
	author = {Urponen, H. and Vuori, I. and Hasan, J. and Partinen, M.},
	year = {1988},
	pmid = {3363395},
	keywords = {Adult, Female, Finland, Humans, Life Style, Male, Middle Aged, Random Allocation, Sampling Studies, Sleep},
	pages = {443--450},
}

@article{morin_epidemiology_2006,
	title = {Epidemiology of insomnia: prevalence, self-help treatments, consultations, and determinants of help-seeking behaviors},
	volume = {7},
	issn = {1389-9457},
	shorttitle = {Epidemiology of insomnia},
	doi = {10.1016/j.sleep.2005.08.008},
	abstract = {BACKGROUND AND PURPOSE: To estimate the prevalence of insomnia symptoms and syndrome in the general population, describe the types of self-help treatments and consultations initiated for insomnia, and examine help-seeking determinants.
PATIENTS AND METHODS: A randomly selected sample of 2001 French-speaking adults from the province of Quebec (Canada) responded to a telephone survey about sleep, insomnia, and its treatments.
RESULTS: Of the total sample, 25.3\% were dissatisfied with their sleep, 29.9\% reported insomnia symptoms, and 9.5\% met criteria for an insomnia syndrome. Thirteen percent of the respondents had consulted a healthcare provider specifically for insomnia in their lifetime, with general practitioners being the most frequently consulted. Daytime fatigue (48\%), psychological distress (40\%), and physical discomfort (22\%) were the main determinants prompting individuals with insomnia to seek treatment. Of the total sample, 15\% had used at least once herbal/dietary products to facilitate sleep and 11\% had used prescribed sleep medications in the year preceding the survey. Other self-help strategies employed to facilitate sleep included reading, listening to music, and relaxation.
CONCLUSIONS: These findings confirm the high prevalence of insomnia in the general population. While few insomnia sufferers seek professional consultations, many individuals initiate self-help treatments, particularly when daytime impairments such as fatigue become more noticeable. Improved knowledge of the determinants of help-seeking behaviors could guide the development of effective public health prevention and intervention programs to promote healthy sleep.},
	language = {eng},
	number = {2},
	journal = {Sleep Medicine},
	author = {Morin, C. M. and LeBlanc, M. and Daley, M. and Gregoire, J. P. and Mérette, C.},
	month = mar,
	year = {2006},
	pmid = {16459140},
	keywords = {Adolescent, Adult, Aged, Aged, 80 and over, Demography, Female, Humans, Male, Middle Aged, Patient Acceptance of Health Care, Prevalence, Referral and Consultation, Self-Help Groups, Sleep Initiation and Maintenance Disorders},
	pages = {123--130},
}

@article{aritake-okada_non-pharmacological_2009,
	title = {Non-{Pharmacological} {Self}-{Management} of {Sleep} {Among} the {Japanese} {General} {Population}},
	volume = {5},
	issn = {1550-9389},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2762720/},
	abstract = {Study Objectives:
The present study was conducted to clarify the prevalence of non-pharmacological self-management (nPSM) practices for obtaining good sleep and to identify favorable nPSM practices that could be applied for reducing excessive daytime sleepiness (EDS). We analyzed epidemiological data for an authentic representative sample of the Japanese population.

Methods:
Data obtained from 24,686 adults via a self-administered questionnaire completed in the Active Survey of Health and Welfare 2000 were used for analyses. The prevalence of individual nPSM practices was calculated by gender. Subsequently, the associations between such practices and EDS were examined using logistic regression analyses.

Results:
“Having a bath” was the most prevalent nPSM practice for both men (59.0\%) and women (64.4\%), followed by “maintaining a regular schedule” (men: 49.0\%, women: 58.6\%), “reading or listening to music” (men: 43.4\%, women: 49.4\%), “snacking on food and/or beverages” (men: 36.1\%, women: 27.9\%), and “exercising” (men: 26.2\%, women: 29.4\%). The prevalence of “maintaining a regular schedule” increased with age. Multiple logistic regression analyses revealed that having a bath and maintaining a regular schedule had negative associations with EDS, whereas snacking on food and/or beverages had a positive association.

Conclusions:
Having a bath and maintaining a regular schedule were identified as favorable nPSM practices for reducing EDS, whereas snacking on food and/or beverages was considered to be an unfavorable nPSM practice.

Citation:
Aritake-Okada S; Kaneita Y; Uchiyama M; Mishima K; Ohida T. Non-pharmacological self-management of sleep among the japanese general population. J Clin Sleep Med 2009;5(5):464-469},
	number = {5},
	urldate = {2024-09-27},
	journal = {Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine},
	author = {Aritake-Okada, Sayaka and Kaneita, Yoshitaka and Uchiyama, Makoto and Mishima, Kazuo and Ohida, Takashi},
	month = oct,
	year = {2009},
	pmid = {19961033},
	pmcid = {PMC2762720},
	pages = {464--469},
}

@article{loewy_music_2020,
	title = {Music {Therapy} as a {Potential} {Intervention} for {Sleep} {Improvement}},
	volume = {12},
	issn = {1179-1608},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954684/},
	doi = {10.2147/NSS.S194938},
	abstract = {Sleep deficiency is linked to chronic health problems, such as heart disease, kidney disease, high blood pressure, diabetes, stroke, obesity, and depression. Healthcare practitioners are increasingly paying close attention to sleep and its impact on health and wellness as a measure of critical vitality. Sleep’s impact on neurologic function, and cognitive endurance affect capacity throughout the lifespan. This article will address recent findings related to the potential of music to induce sleep in illness and wellness. Music therapy research findings and its efficacy as a potent cost-effective intervention will be highlighted.},
	urldate = {2024-09-27},
	journal = {Nature and Science of Sleep},
	author = {Loewy, Joanne},
	month = jan,
	year = {2020},
	pmid = {32021519},
	pmcid = {PMC6954684},
	pages = {1--9},
}

@article{agres_affectmachine-classical_2023,
	title = {{AffectMachine}-{Classical}: a novel system for generating affective classical music},
	volume = {14},
	issn = {1664-1078},
	shorttitle = {{AffectMachine}-{Classical}},
	url = {https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2023.1158172/full},
	doi = {10.3389/fpsyg.2023.1158172},
	abstract = {{\textless}p{\textgreater}This work introduces a new music generation system, called AffectMachine-Classical, that is capable of generating affective Classic music in real-time. AffectMachine was designed to be incorporated into biofeedback systems (such as brain-computer-interfaces) to help users become aware of, and ultimately mediate, their own dynamic affective states. That is, this system was developed for music-based MedTech to support real-time emotion self-regulation in users. We provide an overview of the rule-based, probabilistic system architecture, describing the main aspects of the system and how they are novel. We then present the results of a listener study that was conducted to validate the ability of the system to reliably convey target emotions to listeners. The findings indicate that AffectMachine-Classical is very effective in communicating various levels of Arousal ({\textless}italic{\textgreater}R{\textless}/italic{\textgreater}$^{\textrm{2}}$ = 0.96) to listeners, and is also quite convincing in terms of Valence ({\textless}italic{\textgreater}R{\textless}/italic{\textgreater}$^{\textrm{2}}$ = 0.90). Future work will embed AffectMachine-Classical into biofeedback systems, to leverage the efficacy of the affective music for emotional wellbeing in listeners.{\textless}/p{\textgreater}},
	language = {English},
	urldate = {2024-09-27},
	journal = {Frontiers in Psychology},
	author = {Agres, Kat R. and Dash, Adyasha and Chua, Phoebe},
	month = jun,
	year = {2023},
	note = {Publisher: Frontiers},
	keywords = {Affective Computing, Algorithmic composition, Emotion Regulation, automatic music generation system, listener validation study, music MedTech},
}

@misc{pasini_music2latent_2024,
	title = {{Music2Latent}: {Consistency} {Autoencoders} for {Latent} {Audio} {Compression}},
	shorttitle = {{Music2Latent}},
	url = {https://arxiv.org/abs/2408.06500v1},
	abstract = {Efficient audio representations in a compressed continuous latent space are critical for generative audio modeling and Music Information Retrieval (MIR) tasks. However, some existing audio autoencoders have limitations, such as multi-stage training procedures, slow iterative sampling, or low reconstruction quality. We introduce Music2Latent, an audio autoencoder that overcomes these limitations by leveraging consistency models. Music2Latent encodes samples into a compressed continuous latent space in a single end-to-end training process while enabling high-fidelity single-step reconstruction. Key innovations include conditioning the consistency model on upsampled encoder outputs at all levels through cross connections, using frequency-wise self-attention to capture long-range frequency dependencies, and employing frequency-wise learned scaling to handle varying value distributions across frequencies at different noise levels. We demonstrate that Music2Latent outperforms existing continuous audio autoencoders in sound quality and reconstruction accuracy while achieving competitive performance on downstream MIR tasks using its latent representations. To our knowledge, this represents the first successful attempt at training an end-to-end consistency autoencoder model.},
	language = {en},
	urldate = {2024-09-27},
	journal = {arXiv.org},
	author = {Pasini, Marco and Lattner, Stefan and Fazekas, George},
	month = aug,
	year = {2024},
}

@article{hu_make_2024,
	title = {Make a song curative: {A} spatio-temporal therapeutic music transfer model for anxiety reduction},
	volume = {240},
	issn = {0957-4174},
	shorttitle = {Make a song curative},
	url = {https://www.sciencedirect.com/science/article/pii/S0957417423026635},
	doi = {10.1016/j.eswa.2023.122161},
	abstract = {Anxiety is a prevalent mental health issue that affects both individuals and society. Music therapy has been found effective in reducing anxiety in clinical trials. However, the available music pieces for therapy are limited and may not meet the requirements of individuals from diverse backgrounds. To overcome this limitation, we propose a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) for anxiety reduction. The StTMTM can transfer a piece of music to a new therapeutic music piece that satisfies both the criteria for music therapy and the requirements of individuals. Our approach involves first developing a music feature extraction network that can extract spatio-temporal music features. We then train the network on different genres and transfer the learning results from genre classification to music transfer for anxiety reduction. We also propose a deep learning-based music transfer network with melody and accompaniment encoders and a newly designed loss function to modify a piece of music to be therapeutic. Experimental results on 92 subjects demonstrate that the transferred therapeutic music produced by the StTMTM exhibits exceptional performance in anxiety reduction. Overall, our model has the potential to make music therapy more accessible and effective for individuals with diverse backgrounds and needs.},
	urldate = {2024-09-24},
	journal = {Expert Systems with Applications},
	author = {Hu, Zhejing and Chen, Gong and Liu, Yan and Ma, Xiao and Guan, Nianhong and Wang, Xiaoying},
	month = apr,
	year = {2024},
	keywords = {Artificial music intelligence, Automatic music generation, Music style transfer, Music therapy},
	pages = {122161},
}

@article{medic_short-_2017,
	title = {Short- and long-term health consequences of sleep disruption},
	volume = {9},
	issn = {1179-1608},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5449130/},
	doi = {10.2147/NSS.S134864},
	abstract = {Sleep plays a vital role in brain function and systemic physiology across many body systems. Problems with sleep are widely prevalent and include deficits in quantity and quality of sleep; sleep problems that impact the continuity of sleep are collectively referred to as sleep disruptions. Numerous factors contribute to sleep disruption, ranging from lifestyle and environmental factors to sleep disorders and other medical conditions. Sleep disruptions have substantial adverse short- and long-term health consequences. A literature search was conducted to provide a nonsystematic review of these health consequences (this review was designed to be nonsystematic to better focus on the topics of interest due to the myriad parameters affected by sleep). Sleep disruption is associated with increased activity of the sympathetic nervous system and hypothalamic–pituitary–adrenal axis, metabolic effects, changes in circadian rhythms, and proinflammatory responses. In otherwise healthy adults, short-term consequences of sleep disruption include increased stress responsivity, somatic pain, reduced quality of life, emotional distress and mood disorders, and cognitive, memory, and performance deficits. For adolescents, psychosocial health, school performance, and risk-taking behaviors are impacted by sleep disruption. Behavioral problems and cognitive functioning are associated with sleep disruption in children. Long-term consequences of sleep disruption in otherwise healthy individuals include hypertension, dyslipidemia, cardiovascular disease, weight-related issues, metabolic syndrome, type 2 diabetes mellitus, and colorectal cancer. All-cause mortality is also increased in men with sleep disturbances. For those with underlying medical conditions, sleep disruption may diminish the health-related quality of life of children and adolescents and may worsen the severity of common gastrointestinal disorders. As a result of the potential consequences of sleep disruption, health care professionals should be cognizant of how managing underlying medical conditions may help to optimize sleep continuity and consider prescribing interventions that minimize sleep disruption.},
	urldate = {2024-09-20},
	journal = {Nature and Science of Sleep},
	author = {Medic, Goran and Wille, Micheline and Hemels, Michiel EH},
	month = may,
	year = {2017},
	pmid = {28579842},
	pmcid = {PMC5449130},
	pages = {151--161},
}

@misc{pasini_music2latent_2024-1,
	title = {{Music2Latent}: {Consistency} {Autoencoders} for {Latent} {Audio} {Compression}},
	shorttitle = {{Music2Latent}},
	url = {http://arxiv.org/abs/2408.06500},
	doi = {10.48550/arXiv.2408.06500},
	abstract = {Efficient audio representations in a compressed continuous latent space are critical for generative audio modeling and Music Information Retrieval (MIR) tasks. However, some existing audio autoencoders have limitations, such as multi-stage training procedures, slow iterative sampling, or low reconstruction quality. We introduce Music2Latent, an audio autoencoder that overcomes these limitations by leveraging consistency models. Music2Latent encodes samples into a compressed continuous latent space in a single end-to-end training process while enabling high-fidelity single-step reconstruction. Key innovations include conditioning the consistency model on upsampled encoder outputs at all levels through cross connections, using frequency-wise self-attention to capture long-range frequency dependencies, and employing frequency-wise learned scaling to handle varying value distributions across frequencies at different noise levels. We demonstrate that Music2Latent outperforms existing continuous audio autoencoders in sound quality and reconstruction accuracy while achieving competitive performance on downstream MIR tasks using its latent representations. To our knowledge, this represents the first successful attempt at training an end-to-end consistency autoencoder model.},
	urldate = {2024-09-20},
	publisher = {arXiv},
	author = {Pasini, Marco and Lattner, Stefan and Fazekas, George},
	month = aug,
	year = {2024},
	note = {arXiv:2408.06500 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@article{williams_ai_2019,
	title = {{AI} and {Automatic} {Music} {Generation} for {Mindfulness}},
	abstract = {This paper presents an architecture for the creation of emotionally congruent music using machine learning aided sound synthesis. Our system can generate a small corpus of music using Hidden Markov Models; we can label the pieces with emotional tags using data elicited from questionnaires. This produces a corpus of labelled music underpinned by perceptual evaluations. We then analyse participant’s galvanic skin response (GSR) while listening to our generated music pieces and the emotions they describe in a questionnaire conducted after listening. These analyses reveal that there is a direct correlation between the calmness/scariness of a musical piece, the users’ GSR reading and the emotions they describe feeling. From these, we will be able to estimate an emotional state using biofeedback as a control signal for a machine-learning algorithm, which generates new musical structures according to a perceptually informed musical feature similarity model. Our case study suggests various applications including in gaming, automated soundtrack generation, and mindfulness.},
	language = {en},
	author = {Williams, Duncan and Hodge, Victoria J and Gega, Lina and Murphy, Damian and Cowling, Peter I and Drachen, Anders},
	year = {2019},
}

@article{tzanetakis_musical_2002,
	title = {Musical genre classification of audio signals},
	volume = {10},
	copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
	issn = {1063-6676, 1558-2353},
	url = {https://ieeexplore.ieee.org/document/1021072/},
	doi = {10.1109/TSA.2002.800560},
	abstract = {Musical genres are categorical labels created by humans to characterize pieces of music. A musical genre is characterized by the common characteristics shared by its members. These characteristics typically are related to the instrumentation, rhythmic structure, and harmonic content of the music. Genre hierarchies are commonly used to structure the large collections of music available on the Web. Currently musical genre annotation is performed manually. Automatic musical genre classification can assist or replace the human user in this process and would be a valuable addition to music information retrieval systems. In addition, automatic musical genre classification provides a framework for developing and evaluating features for any type of content-based analysis of musical signals.},
	language = {en},
	number = {5},
	urldate = {2024-09-02},
	journal = {IEEE Transactions on Speech and Audio Processing},
	author = {Tzanetakis, G. and Cook, P.},
	month = jul,
	year = {2002},
	pages = {293--302},
}

@article{trahan_music_2018,
	title = {The music that helps people sleep and the reasons they believe it works: {A} mixed methods analysis of online survey reports},
	volume = {13},
	issn = {1932-6203},
	shorttitle = {The music that helps people sleep and the reasons they believe it works},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0206531},
	doi = {10.1371/journal.pone.0206531},
	abstract = {Sleep loss is a widespread problem with serious physical and economic consequences. Music can impact upon physical, psychological and emotional states, which may explain anecdotal reports of its success as an everyday sleep aid. However, there is a lack of systematic data on how widely it is used, why people opt for music as a sleep aid, or what music works; hence the underlying drivers to music-sleep effects remain unclear. We investigated music as a sleep aid within the general public via a mixed methods data online survey (n = 651) that scored musicality, sleep habits, and open text responses on what music helps sleep and why. In total, 62\% of respondents stated that they used music to help them sleep. They reported fourteen musical genres comprising 545 artists. Linear modelling found stress, age, and music use as significant predictors of sleep quality (PSQI) scores. Regression tree modelling revealed that younger people with higher musical engagement were significantly more likely to use music to aid sleep. Thematic analysis of the open text responses generated four themes that described why people believe music helps sleep: music offers unique properties that stimulate sleep (Provide), music is part of a normal sleep routine (Habit), music induces a physical or mental state conducive to sleep (State), and music blocks an internal or external stimulus that would otherwise disrupt sleep (Distract). This survey provides new evidence into the relationship between music and sleep in a population that ranged widely in age, musicality, sleep habits and stress levels. In particular, the results highlight the varied pathways of effect between music and sleep. Diversity was observed both in music choices, which reflected idiosyncratic preferences rather than any consistent musical structure, and in the reasons why music supports good sleep, which went far beyond simple physical/mental relaxation.},
	language = {en},
	number = {11},
	urldate = {2024-09-02},
	journal = {PLOS ONE},
	author = {Trahan, Tabitha and Durrant, Simon J. and Müllensiefen, Daniel and Williamson, Victoria J.},
	month = nov,
	year = {2018},
	note = {Publisher: Public Library of Science},
	keywords = {Bioacoustics, Habits, Music cognition, Music perception, Music therapy, Relaxation (psychology), Sleep, Surveys},
	pages = {e0206531},
}

@misc{dash_ai-based_2023,
	title = {{AI}-{Based} {Affective} {Music} {Generation} {Systems}: {A} {Review} of {Methods}, and {Challenges}},
	shorttitle = {{AI}-{Based} {Affective} {Music} {Generation} {Systems}},
	url = {http://arxiv.org/abs/2301.06890},
	doi = {10.48550/arXiv.2301.06890},
	abstract = {Music is a powerful medium for altering the emotional state of the listener. In recent years, with significant advancement in computing capabilities, artificial intelligence-based (AI-based) approaches have become popular for creating affective music generation (AMG) systems that are empowered with the ability to generate affective music. Entertainment, healthcare, and sensor-integrated interactive system design are a few of the areas in which AI-based affective music generation (AI-AMG) systems may have a significant impact. Given the surge of interest in this topic, this article aims to provide a comprehensive review of AI-AMG systems. The main building blocks of an AI-AMG system are discussed, and existing systems are formally categorized based on the core algorithm used for music generation. In addition, this article discusses the main musical features employed to compose affective music, along with the respective AI-based approaches used for tailoring them. Lastly, the main challenges and open questions in this field, as well as their potential solutions, are presented to guide future research. We hope that this review will be useful for readers seeking to understand the state-of-the-art in AI-AMG systems, and gain an overview of the methods used for developing them, thereby helping them explore this field in the future.},
	urldate = {2024-08-28},
	publisher = {arXiv},
	author = {Dash, Adyasha and Agres, Kat R.},
	month = jan,
	year = {2023},
	note = {arXiv:2301.06890 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction},
}

@article{wallis_computer-generating_2008,
	series = {Proceedings of the {International} {Conference} on {Digital} {Audio} {Effects}, {DAFx}},
	title = {Computer-{Generating} emotional music: 11th {International} {Conference} on {Digital} {Audio} {Effects}, {DAFx} 2008},
	issn = {9789512295173},
	shorttitle = {Computer-{Generating} emotional music},
	url = {http://www.scopus.com/inward/record.url?scp=70350205034&partnerID=8YFLogxK},
	abstract = {This paper explores one way to use music in the context of affective design. We've made a real-time music generator that is designed around the concepts of valence and arousal, which are two components of certain models of emotion. When set to a desired valence and arousal, the algorithm plays music corresponding to the intersection of these two parameters. We designed our algorithm using psychological theory of emotion and parametrized features of music which have been tested for affect. The results are a modular algorithm design, in which our parameters can be implemented in other affective music algorithms. We describe our implementation of these parameters, and our strategy for manipulating the parameters to generate musical emotion. Finally we discuss possible applications for these techniques in the fields of the arts, medical systems, and research applications. We believe that further work will result in a music generator which can produce music in any of a wide variety of commonly-perceived emotional connotations on command.},
	urldate = {2024-08-27},
	journal = {Proceedings - 11th International Conference on Digital Audio Effects, DAFx 2008},
	author = {Wallis, Isaac and Ingalls, Todd and Campana, Ellen},
	year = {2008},
	pages = {7--12},
}

@inproceedings{yang_sleepgan_2022,
	title = {{SleepGAN}: {Towards} {Personalized} {Sleep} {Therapy} {Music}},
	shorttitle = {{SleepGAN}},
	doi = {10.1109/ICASSP43922.2022.9747033},
	abstract = {Sleep deficiency and disorders are one of the most unsolved public health challenges of modern times. Music therapy is a promising approach, offering a cheap and non-invasive solution to improve sleep quality. However, the choice of therapeutic sleep music is highly limited for users because such music needs to be specially chosen and made by sleep therapists. It could potentially lead to the inefficiency of music therapy if users get bored after listening to the same set of music repeatedly. In this paper, we take the first step towards generating personalized sleep therapy music. Firstly, through an in-depth feature analysis, we investigate the importance of various musical and acoustic features of therapy music. Grounded on our findings, we design a style transfer framework called SleepGAN which induces therapeutic features into music from different genres. We show that, compared to baselines, the music generated by SleepGAN has a higher similarity to the sleep music designed by experts.},
	urldate = {2024-08-26},
	booktitle = {{ICASSP} 2022 - 2022 {IEEE} {International} {Conference} on {Acoustics}, {Speech} and {Signal} {Processing} ({ICASSP})},
	author = {Yang, Jing and Min, Chulhong and Mathur, Akhil and Kawsar, Fahim},
	month = may,
	year = {2022},
	note = {ISSN: 2379-190X},
	keywords = {Conferences, Medical treatment, Multiple signal classification, Music, Music style transfer, Public healthcare, Signal processing, Sleep therapy, Speech processing},
	pages = {966--970},
}

@misc{masuyama_deep_2019,
	title = {Deep {Griffin}-{Lim} {Iteration}},
	url = {http://arxiv.org/abs/1903.03971},
	doi = {10.48550/arXiv.1903.03971},
	abstract = {This paper presents a novel phase reconstruction method (only from a given amplitude spectrogram) by combining a signal-processing-based approach and a deep neural network (DNN). To retrieve a time-domain signal from its amplitude spectrogram, the corresponding phase is required. One of the popular phase reconstruction methods is the Griffin-Lim algorithm (GLA), which is based on the redundancy of the short-time Fourier transform. However, GLA often involves many iterations and produces low-quality signals owing to the lack of prior knowledge of the target signal. In order to address these issues, in this study, we propose an architecture which stacks a sub-block including two GLA-inspired fixed layers and a DNN. The number of stacked sub-blocks is adjustable, and we can trade the performance and computational load based on requirements of applications. The effectiveness of the proposed method is investigated by reconstructing phases from amplitude spectrograms of speeches.},
	urldate = {2024-08-15},
	publisher = {arXiv},
	author = {Masuyama, Yoshiki and Yatabe, Kohei and Koizumi, Yuma and Oikawa, Yasuhiro and Harada, Noboru},
	month = mar,
	year = {2019},
	note = {arXiv:1903.03971 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@inproceedings{ribeiro_crowdmos_2011,
	address = {Prague, Czech Republic},
	title = {{CROWDMOS}: {An} approach for crowdsourcing mean opinion score studies},
	isbn = {978-1-4577-0538-0},
	shorttitle = {{CROWDMOS}},
	url = {http://ieeexplore.ieee.org/document/5946971/},
	doi = {10.1109/ICASSP.2011.5946971},
	abstract = {MOS (mean opinion score) subjective quality studies are used to evaluate many signal processing methods. Since laboratory quality studies are time consuming and expensive, researchers often run small studies with less statistical signiﬁcance or use objective measures which only approximate human perception. We propose a cost-effective and convenient measure called crowdMOS, obtained by having internet users participate in a MOS-like listening study. Workers listen and rate sentences at their leisure, using their own hardware, in an environment of their choice. Since these individuals cannot be supervised, we propose methods for detecting and discarding inaccurate scores. To automate crowdMOS testing, we offer a set of freely distributable, open-source tools for Amazon Mechanical Turk, a platform designed to facilitate crowdsourcing. These tools implement the MOS testing methodology described in this paper, providing researchers with a user-friendly means of performing subjective quality evaluations without the overhead associated with laboratory studies. Finally, we demonstrate the use of crowdMOS using data from the Blizzard text-to-speech competition, showing that it delivers accurate and repeatable results.},
	language = {en},
	urldate = {2024-07-24},
	booktitle = {2011 {IEEE} {International} {Conference} on {Acoustics}, {Speech} and {Signal} {Processing} ({ICASSP})},
	publisher = {IEEE},
	author = {Ribeiro, Flavio and Florencio, Dinei and Zhang, Cha and Seltzer, Michael},
	month = may,
	year = {2011},
	pages = {2416--2419},
}

@incollection{shahid_pittsburgh_2011,
	address = {New York, NY},
	title = {Pittsburgh {Sleep} {Quality} {Index} ({PSQI})},
	isbn = {978-1-4419-9892-7 978-1-4419-9893-4},
	url = {https://link.springer.com/10.1007/978-1-4419-9893-4_67},
	language = {en},
	urldate = {2024-07-24},
	booktitle = {{STOP}, {THAT} and {One} {Hundred} {Other} {Sleep} {Scales}},
	publisher = {Springer New York},
	author = {Shahid, Azmeh and Wilkinson, Kate and Marcu, Shai and Shapiro, Colin M.},
	editor = {Shahid, Azmeh and Wilkinson, Kate and Marcu, Shai and Shapiro, Colin M},
	year = {2011},
	doi = {10.1007/978-1-4419-9893-4_67},
	pages = {279--283},
}

@misc{noauthor_sleepgan_nodate,
	title = {{SleepGAN}: {Towards} {Personalized} {Sleep} {Therapy} {Music} {\textbar} {IEEE} {Conference} {Publication} {\textbar} {IEEE} {Xplore}},
	url = {https://ieeexplore.ieee.org/document/9747033},
	urldate = {2024-07-10},
}

@article{ding_effectiveness_2021,
	title = {Effectiveness and safety of music therapy for insomnia disorder patients: {A} protocol for systematic review and meta-analysis},
	volume = {100},
	issn = {1536-5964},
	shorttitle = {Effectiveness and safety of music therapy for insomnia disorder patients},
	doi = {10.1097/MD.0000000000026399},
	abstract = {BACKGROUND: Music therapy has been widely used clinically to relieve insomnia disorder patients. However, the efficacy of music therapy in the treatment of insomnia disorder patients is uncertain. The purpose of this study is to determine the effectiveness and safety of music therapy in the treatment of insomnia disorder patients.
METHODS: Search PubMed, Cochrane Library, Embase, China National Knowledge Infrastructure Database, Wanfang Database, China Science and Technology Journal Database, China Biomedical Literature Database, and search-related randomized controlled trials. Two reviewers will independently select studies, collect data, and evaluate methodological quality through the Cochrane Deviation Risk Tool. Revman V.5.3 will be used for meta-analysis.
RESULTS: This study will evaluate the current status of music therapy treatment for insomnia disorder patients, aiming to illustrate the effectiveness and safety of music therapy treatment.
CONCLUSION: This study will provide a basis for judging whether music therapy is effective in treating insomnia disorder patients.
INPLASY REGISTRATION NUMBER: INPLASY202150087.},
	language = {eng},
	number = {26},
	journal = {Medicine},
	author = {Ding, Jie and Huang, Tianqi and Hu, Jinyu and Yuan, Fuqiang},
	month = jul,
	year = {2021},
	pmid = {34190156},
	pmcid = {PMC8257833},
	keywords = {Humans, Meta-Analysis as Topic, Music Therapy, Sleep Initiation and Maintenance Disorders, Systematic Reviews as Topic},
	pages = {e26399},
}

@inproceedings{bittner_medleydb_2014,
	title = {{MedleyDB}: {A} {Multitrack} {Dataset} for {Annotation}-{Intensive} {MIR} {Research}},
	shorttitle = {{MedleyDB}},
	abstract = {We introduce MedleyDB: a dataset of annotated, royalty-free multitrack recordings. The dataset was primarily developed to support research on melody extraction, address-ing important shortcomings of existing collections. For each song we provide melody f 0 annotations as well as instrument activations for evaluating automatic instrument recognition. The dataset is also useful for research on tasks that require access to the individual tracks of a song such as source separation and automatic mixing. In this paper we provide a detailed description of MedleyDB, including curation, annotation, and musical content. To gain insight into the new challenges presented by the dataset, we run a set of experiments using a state-of-the-art melody extraction algorithm and discuss the results. The dataset is shown to be considerably more challenging than the current test sets used in the MIREX evaluation campaign, thus opening new research avenues in melody extraction research.},
	author = {Bittner, Rachel and Salamon, Justin and Tierney, Mike and Mauch, Matthias and Cannam, Chris and Bello, Juan},
	month = oct,
	year = {2014},
}

@misc{zen_libritts_2019,
	title = {{LibriTTS}: {A} {Corpus} {Derived} from {LibriSpeech} for {Text}-to-{Speech}},
	shorttitle = {{LibriTTS}},
	url = {http://arxiv.org/abs/1904.02882},
	doi = {10.48550/arXiv.1904.02882},
	abstract = {This paper introduces a new speech corpus called "LibriTTS" designed for text-to-speech use. It is derived from the original audio and text materials of the LibriSpeech corpus, which has been used for training and evaluating automatic speech recognition systems. The new corpus inherits desired properties of the LibriSpeech corpus while addressing a number of issues which make LibriSpeech less than ideal for text-to-speech work. The released corpus consists of 585 hours of speech data at 24kHz sampling rate from 2,456 speakers and the corresponding texts. Experimental results show that neural end-to-end TTS models trained from the LibriTTS corpus achieved above 4.0 in mean opinion scores in naturalness in five out of six evaluation speakers. The corpus is freely available for download from http://www.openslr.org/60/.},
	urldate = {2024-06-06},
	publisher = {arXiv},
	author = {Zen, Heiga and Dang, Viet and Clark, Rob and Zhang, Yu and Weiss, Ron J. and Jia, Ye and Chen, Zhifeng and Wu, Yonghui},
	month = apr,
	year = {2019},
	note = {arXiv:1904.02882 [cs, eess]},
	keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{zen_libritts_2019-1,
	title = {{LibriTTS}: {A} {Corpus} {Derived} from {LibriSpeech} for {Text}-to-{Speech}},
	shorttitle = {{LibriTTS}},
	url = {http://arxiv.org/abs/1904.02882},
	abstract = {This paper introduces a new speech corpus called “LibriTTS” designed for text-to-speech use. It is derived from the original audio and text materials of the LibriSpeech corpus, which has been used for training and evaluating automatic speech recognition systems. The new corpus inherits desired properties of the LibriSpeech corpus while addressing a number of issues which make LibriSpeech less than ideal for text-to-speech work. The released corpus consists of 585 hours of speech data at 24kHz sampling rate from 2,456 speakers and the corresponding texts. Experimental results show that neural end-to-end TTS models trained from the LibriTTS corpus achieved above 4.0 in mean opinion scores in naturalness in ﬁve out of six evaluation speakers. The corpus is freely available for download from http://www.openslr.org/60/.},
	language = {en},
	urldate = {2024-06-06},
	publisher = {arXiv},
	author = {Zen, Heiga and Dang, Viet and Clark, Rob and Zhang, Yu and Weiss, Ron J. and Jia, Ye and Chen, Zhifeng and Wu, Yonghui},
	month = apr,
	year = {2019},
	note = {arXiv:1904.02882 [cs, eess]},
	keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@inproceedings{piczak_esc_2015,
	address = {Brisbane Australia},
	title = {{ESC}: {Dataset} for {Environmental} {Sound} {Classification}},
	isbn = {978-1-4503-3459-4},
	shorttitle = {{ESC}},
	url = {https://dl.acm.org/doi/10.1145/2733373.2806390},
	doi = {10.1145/2733373.2806390},
	abstract = {One of the obstacles in research activities concentrating on environmental sound classiﬁcation is the scarcity of suitable and publicly available datasets. This paper tries to address that issue by presenting a new annotated collection of 2 000 short clips comprising 50 classes of various common sound events, and an abundant uniﬁed compilation of 250 000 unlabeled auditory excerpts extracted from recordings available through the Freesound project. The paper also provides an evaluation of human accuracy in classifying environmental sounds and compares it to the performance of selected baseline classiﬁers using features derived from mel-frequency cepstral coeﬃcients and zero-crossing rate.},
	language = {en},
	urldate = {2024-06-06},
	booktitle = {Proceedings of the 23rd {ACM} international conference on {Multimedia}},
	publisher = {ACM},
	author = {Piczak, Karol J.},
	month = oct,
	year = {2015},
	pages = {1015--1018},
}

@misc{deshmukh_audio_2022,
	title = {Audio {Retrieval} with {WavText5K} and {CLAP} {Training}},
	url = {http://arxiv.org/abs/2209.14275},
	doi = {10.48550/arXiv.2209.14275},
	abstract = {Audio-Text retrieval takes a natural language query to retrieve relevant audio files in a database. Conversely, Text-Audio retrieval takes an audio file as a query to retrieve relevant natural language descriptions. Most of the literature train retrieval systems with one audio captioning dataset, but evaluating the benefit of training with multiple datasets is underexplored. Moreover, retrieval systems have to learn the alignment between elaborated sentences describing audio content of variable length ranging from a few seconds to several minutes. In this work, we propose a new collection of web audio-text pairs and a new framework for retrieval. First, we provide a new collection of about five thousand web audio-text pairs that we refer to as WavText5K. When used to train our retrieval system, WavText5K improved performance more than other audio captioning datasets. Second, our framework learns to connect language and audio content by using a text encoder, two audio encoders, and a contrastive learning objective. Combining both audio encoders helps to process variable length audio. The two contributions beat state of the art performance for AudioCaps and Clotho on Text-Audio retrieval by a relative 2\% and 16\%, and Audio-Text retrieval by 6\% and 23\%.},
	urldate = {2024-06-06},
	publisher = {arXiv},
	author = {Deshmukh, Soham and Elizalde, Benjamin and Wang, Huaming},
	month = sep,
	year = {2022},
	note = {arXiv:2209.14275 [cs, eess]},
	keywords = {Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{martin-morato_what_2021,
	title = {What is the ground truth? {Reliability} of multi-annotator data for audio tagging},
	shorttitle = {What is the ground truth?},
	url = {http://arxiv.org/abs/2104.04214},
	doi = {10.48550/arXiv.2104.04214},
	abstract = {Crowdsourcing has become a common approach for annotating large amounts of data. It has the advantage of harnessing a large workforce to produce large amounts of data in a short time, but comes with the disadvantage of employing non-expert annotators with different backgrounds. This raises the problem of data reliability, in addition to the general question of how to combine the opinions of multiple annotators in order to estimate the ground truth. This paper presents a study of the annotations and annotators' reliability for audio tagging. We adapt the use of Krippendorf's alpha and multi-annotator competence estimation (MACE) for a multi-labeled data scenario, and present how MACE can be used to estimate a candidate ground truth based on annotations from non-expert users with different levels of expertise and competence.},
	urldate = {2024-06-06},
	publisher = {arXiv},
	author = {Martin-Morato, Irene and Mesaros, Annamaria},
	month = apr,
	year = {2021},
	note = {arXiv:2104.04214 [eess]},
	keywords = {Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@inproceedings{drossos_clotho_2020,
	title = {Clotho: an {Audio} {Captioning} {Dataset}},
	shorttitle = {Clotho},
	doi = {10.1109/ICASSP40776.2020.9052990},
	author = {Drossos, Konstantinos and Lipping, Samuel and Virtanen, Tuomas},
	month = may,
	year = {2020},
	pages = {736--740},
}

@inproceedings{wu_audio_2019,
	title = {Audio {Caption}: {Listen} and {Tell}},
	shorttitle = {Audio {Caption}},
	url = {http://arxiv.org/abs/1902.09254},
	doi = {10.1109/ICASSP.2019.8682377},
	abstract = {Increasing amount of research has shed light on machine perception of audio events, most of which concerns detection and classification tasks. However, human-like perception of audio scenes involves not only detecting and classifying audio sounds, but also summarizing the relationship between different audio events. Comparable research such as image caption has been conducted, yet the audio field is still quite barren. This paper introduces a manually-annotated dataset for audio caption. The purpose is to automatically generate natural sentences for audio scene description and to bridge the gap between machine perception of audio and image. The whole dataset is labelled in Mandarin and we also include translated English annotations. A baseline encoder-decoder model is provided for both English and Mandarin. Similar BLEU scores are derived for both languages: our model can generate understandable and data-related captions based on the dataset.},
	urldate = {2024-06-06},
	booktitle = {{ICASSP} 2019 - 2019 {IEEE} {International} {Conference} on {Acoustics}, {Speech} and {Signal} {Processing} ({ICASSP})},
	author = {Wu, Mengyue and Dinkel, Heinrich and Yu, Kai},
	month = may,
	year = {2019},
	note = {arXiv:1902.09254 [cs, eess]},
	keywords = {Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
	pages = {830--834},
}

@misc{kong_panns_2020,
	title = {{PANNs}: {Large}-{Scale} {Pretrained} {Audio} {Neural} {Networks} for {Audio} {Pattern} {Recognition}},
	shorttitle = {{PANNs}},
	url = {http://arxiv.org/abs/1912.10211},
	doi = {10.48550/arXiv.1912.10211},
	abstract = {Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification, music classification, speech emotion classification and sound event detection. Recently, neural networks have been applied to tackle audio pattern recognition problems. However, previous systems are built on specific datasets with limited durations. Recently, in computer vision and natural language processing, systems pretrained on large-scale datasets have generalized well to several tasks. However, there is limited research on pretraining systems on large-scale datasets for audio pattern recognition. In this paper, we propose pretrained audio neural networks (PANNs) trained on the large-scale AudioSet dataset. These PANNs are transferred to other audio related tasks. We investigate the performance and computational complexity of PANNs modeled by a variety of convolutional neural networks. We propose an architecture called Wavegram-Logmel-CNN using both log-mel spectrogram and waveform as input feature. Our best PANN system achieves a state-of-the-art mean average precision (mAP) of 0.439 on AudioSet tagging, outperforming the best previous system of 0.392. We transfer PANNs to six audio pattern recognition tasks, and demonstrate state-of-the-art performance in several of those tasks. We have released the source code and pretrained models of PANNs: https://github.com/qiuqiangkong/audioset\_tagging\_cnn.},
	urldate = {2024-06-06},
	publisher = {arXiv},
	author = {Kong, Qiuqiang and Cao, Yin and Iqbal, Turab and Wang, Yuxuan and Wang, Wenwu and Plumbley, Mark D.},
	month = aug,
	year = {2020},
	note = {arXiv:1912.10211 [cs, eess]},
	keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@inproceedings{kim_audiocaps_2019,
	address = {Minneapolis, Minnesota},
	title = {{AudioCaps}: {Generating} {Captions} for {Audios} in {The} {Wild}},
	shorttitle = {{AudioCaps}},
	url = {https://aclanthology.org/N19-1011},
	doi = {10.18653/v1/N19-1011},
	abstract = {We explore the problem of Audio Captioning: generating natural language description for any kind of audio in the wild, which has been surprisingly unexplored in previous research. We contribute a large-scale dataset of 46K audio clips with human-written text pairs collected via crowdsourcing on the AudioSet dataset. Our thorough empirical studies not only show that our collected captions are indeed faithful to audio inputs but also discover what forms of audio representation and captioning models are effective for the audio captioning. From extensive experiments, we also propose two novel components that help improve audio captioning performance: the top-down multi-scale encoder and aligned semantic attention.},
	urldate = {2024-06-06},
	booktitle = {Proceedings of the 2019 {Conference} of the {North} {American} {Chapter} of the {Association} for {Computational} {Linguistics}: {Human} {Language} {Technologies}, {Volume} 1 ({Long} and {Short} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Kim, Chris Dongjoo and Kim, Byeongchang and Lee, Hyunmin and Kim, Gunhee},
	editor = {Burstein, Jill and Doran, Christy and Solorio, Thamar},
	month = jun,
	year = {2019},
	pages = {119--132},
}

@misc{lostanlen_medley-solos-db_2018,
	title = {Medley-solos-{DB}: a cross-collection dataset for musical instrument recognition},
	shorttitle = {Medley-solos-{DB}},
	url = {https://zenodo.org/records/1344103},
	doi = {10.5281/zenodo.1344103},
	abstract = {Medley-solos-DB
=============
Version 1.0, February 2019.
 


This version is incomplete.
Please download the latest version: https://doi.org/10.5281/zenodo.1344102},
	urldate = {2024-06-06},
	publisher = {Zenodo},
	author = {Lostanlen, Vincent and Cella, Carmine-Emanuele and Bittner, Rachel and Essid, Slim},
	month = sep,
	year = {2018},
	keywords = {audio, classification, instrument, machine learning, machine listening, music, music information retrieval, timbre},
}

@inproceedings{panayotov_librispeech_2015,
	title = {Librispeech: {An} {ASR} corpus based on public domain audio books},
	shorttitle = {Librispeech},
	url = {https://ieeexplore.ieee.org/document/7178964},
	doi = {10.1109/ICASSP.2015.7178964},
	abstract = {This paper introduces a new corpus of read English speech, suitable for training and evaluating speech recognition systems. The LibriSpeech corpus is derived from audiobooks that are part of the LibriVox project, and contains 1000 hours of speech sampled at 16 kHz. We have made the corpus freely available for download, along with separately prepared language-model training data and pre-built language models. We show that acoustic models trained on LibriSpeech give lower error rate on the Wall Street Journal (WSJ) test sets than models trained on WSJ itself. We are also releasing Kaldi scripts that make it easy to build these systems.},
	urldate = {2024-06-06},
	booktitle = {2015 {IEEE} {International} {Conference} on {Acoustics}, {Speech} and {Signal} {Processing} ({ICASSP})},
	author = {Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
	month = apr,
	year = {2015},
	note = {ISSN: 2379-190X},
	keywords = {Bioinformatics, Blogs, Corpus, Electronic publishing, Genomics, Information services, LibriVox, Resource description framework, Speech Recognition},
	pages = {5206--5210},
}

@inproceedings{salamon_dataset_2014,
	address = {Orlando Florida USA},
	title = {A {Dataset} and {Taxonomy} for {Urban} {Sound} {Research}},
	isbn = {978-1-4503-3063-3},
	url = {https://dl.acm.org/doi/10.1145/2647868.2655045},
	doi = {10.1145/2647868.2655045},
	abstract = {Automatic urban sound classiﬁcation is a growing area of research with applications in multimedia retrieval and urban informatics. In this paper we identify two main barriers to research in this area – the lack of a common taxonomy and the scarceness of large, real-world, annotated data. To address these issues we present a taxonomy of urban sounds and a new dataset, UrbanSound, containing 27 hours of audio with 18.5 hours of annotated sound event occurrences across 10 sound classes. The challenges presented by the new dataset are studied through a series of experiments using a baseline classiﬁcation system.},
	language = {en},
	urldate = {2024-06-06},
	booktitle = {Proceedings of the 22nd {ACM} international conference on {Multimedia}},
	publisher = {ACM},
	author = {Salamon, Justin and Jacoby, Christopher and Bello, Juan Pablo},
	month = nov,
	year = {2014},
	pages = {1041--1044},
}

@misc{fonseca_fsd50k_2020,
	title = {{FSD50K}},
	url = {https://zenodo.org/records/4060432},
	doi = {10.5281/zenodo.4060432},
	abstract = {FSD50K is an open dataset of human-labeled sound events containing 51,197 Freesound clips unequally distributed in 200 classes drawn from the AudioSet Ontology. FSD50K has been created at the Music Technology Group of Universitat Pompeu Fabra.


Citation


If you use the FSD50K dataset, or part of it, please cite our TASLP paper (available from [arXiv] [TASLP]):




@article\{\vphantom{\}}fonseca2022FSD50K,
  title=\{\{FSD50K\}: an open dataset of human-labeled sound events\},
  author=\{Fonseca, Eduardo and Favory, Xavier and Pons, Jordi and Font, Frederic and Serra, Xavier\},
  journal=\{IEEE/ACM Transactions on Audio, Speech, and Language Processing\},
  volume=\{30\},
  pages=\{829--852\},
  year=\{2022\},
  publisher=\{IEEE\}
\vphantom{\{}\}




Paper update: This paper has been published in TASLP at the beginning of 2022. The accepted camera-ready version includes a number of improvements with respect to the initial submission. The main updates include: estimation of the amount of label noise in FSD50K, SNR comparison between FSD50K and AudioSet, improved description of evaluation metrics including equations, clarification of experimental methodology and some results, some content moved to Appendix for readability. The TASLP-accepted camera-ready version is available from arXiv (in particular, it is v2 in arXiv, displayed by default).


Data curators


Eduardo Fonseca, Xavier Favory, Jordi Pons, Mercedes Collado, Ceren Can, Rachit Gupta, Javier Arredondo, Gary Avendano and Sara Fernandez


Contact


You are welcome to contact Eduardo Fonseca should you have any questions, at efonseca@google.com.


 


ABOUT FSD50K


Freesound Dataset 50k (or FSD50K for short) is an open dataset of human-labeled sound events containing 51,197 Freesound clips unequally distributed in 200 classes drawn from the AudioSet Ontology [1]. FSD50K has been created at the Music Technology Group of Universitat Pompeu Fabra.


What follows is a brief summary of FSD50K's most important characteristics. Please have a look at our paper (especially Section 4) to extend the basic information provided here with relevant details for its usage, as well as discussion, limitations, applications and more.


Basic characteristics:



	
FSD50K contains 51,197 audio clips from Freesound, totalling 108.3 hours of multi-labeled audio
	
The dataset encompasses 200 sound classes (144 leaf nodes and 56 intermediate nodes) hierarchically organized with a subset of the AudioSet Ontology.
	
The audio content is composed mainly of sound events produced by physical sound sources and production mechanisms, including human sounds, sounds of things, animals, natural sounds, musical instruments and more. The vocabulary can be inspected in vocabulary.csv (see Files section below).
	
The acoustic material has been manually labeled by humans following a data labeling process using the Freesound Annotator platform [2]. 
	
Clips are of variable length from 0.3 to 30s, due to the diversity of the sound classes and the preferences of Freesound users when recording sounds.
	
All clips are provided as uncompressed PCM 16 bit 44.1 kHz mono audio files.
	
Ground truth labels are provided at the clip-level (i.e., weak labels).
	
The dataset poses mainly a large-vocabulary multi-label sound event classification problem, but also allows development and evaluation of a variety of machine listening approaches (see Sec. 4D in our paper).
	
In addition to audio clips and ground truth, additional metadata is made available (including raw annotations, sound predominance ratings, Freesound metadata, and more), allowing a variety of analyses and sound event research tasks (see Files section below).
	
The audio clips are grouped into a development (dev) set and an evaluation (eval) set such that they do not have clips from the same Freesound uploader.



Dev set:



	
40,966 audio clips totalling 80.4 hours of audio
	
Avg duration/clip: 7.1s
	
114,271 smeared labels (i.e., labels propagated in the upwards direction to the root of the ontology)
	
Labels are correct but could be occasionally incomplete
	
A train/validation split is provided (Sec. 3H). If a different split is used, it should be specified for reproducibility and fair comparability of results (see Sec. 5C of our paper)



Eval set:



	
10,231 audio clips totalling 27.9 hours of audio
	
Avg duration/clip: 9.8s
	
38,596 smeared labels
	
Eval set is labeled exhaustively (labels are correct and complete for the considered vocabulary)



Note: All classes in FSD50K are represented in AudioSet, except Crash cymbal, Human group actions, Human voice, Respiratory sounds, and Domestic sounds, home sounds.


 


LICENSE


All audio clips in FSD50K are released under Creative Commons (CC) licenses. Each clip has its own license as defined by the clip uploader in Freesound, some of them requiring attribution to their original authors and some forbidding further commercial reuse. Specifically:


The development set consists of 40,966 clips with the following licenses:



	
CC0: 14,959
	
CC-BY: 20,017
	
CC-BY-NC: 4616
	
CC Sampling+: 1374



The evaluation set consists of 10,231 clips with the following licenses:



	
CC0: 4914
	
CC-BY: 3489
	
CC-BY-NC: 1425
	
CC Sampling+: 403



For attribution purposes and to facilitate attribution of these files to third parties, we include a mapping from the audio clips to their corresponding licenses. The licenses are specified in the files dev\_clips\_info\_FSD50K.json and eval\_clips\_info\_FSD50K.json.


In addition, FSD50K as a whole is the result of a curation process and it has an additional license: FSD50K is released under CC-BY. This license is specified in the LICENSE-DATASET file downloaded with the FSD50K.doc zip file. We note that the choice of one license for the dataset as a whole is not straightforward as it comprises items with different licenses (such as audio clips, annotations, or data split). The choice of a global license in these cases may warrant further investigation (e.g., by someone with a background in copyright law).


Usage of FSD50K for commercial purposes:


If you'd like to use FSD50K for commercial purposes, please contact Eduardo Fonseca and Frederic Font at efonseca@google.com and frederic.font@upf.edu.


Also, if you are interested in using FSD50K for machine learning competitions, please contact Eduardo Fonseca and Frederic Font at efonseca@google.com and frederic.font@upf.edu.


 


FILES


FSD50K can be downloaded as a series of zip files with the following directory structure:


root
│  
└───FSD50K.dev\_audio/                   Audio clips in the dev set
│  
└───FSD50K.eval\_audio/                  Audio clips in the eval set
│   
└───FSD50K.ground\_truth/                Files for FSD50K's ground truth
│   │   
│   └─── dev.csv                              Ground truth for the dev set
│   │        
│   └─── eval.csv                             Ground truth for the eval set            
│   │            
│   └─── vocabulary.csv                       List of 200 sound classes in FSD50K 
│   
└───FSD50K.metadata/                    Files for additional metadata
│   │            
│   └─── class\_info\_FSD50K.json               Metadata about the sound classes
│   │            
│   └─── dev\_clips\_info\_FSD50K.json           Metadata about the dev clips
│   │            
│   └─── eval\_clips\_info\_FSD50K.json          Metadata about the eval clips
│   │            
│   └─── pp\_pnp\_ratings\_FSD50K.json           PP/PNP ratings    
│   │            
│   └─── collection/                          Files for the *sound collection* format   
│   
└───FSD50K.doc/
    │            
    └───README.md                             The dataset description file that you are reading
    │            
    └───LICENSE-DATASET                       License of the FSD50K dataset as an entity   



Each row (i.e. audio clip) of dev.csv contains the following information:



	
fname: the file name without the .wav extension, e.g., the fname 64760 corresponds to the file 64760.wav in disk. This number is the Freesound id. We always use Freesound ids as filenames.
	
labels: the class labels (i.e., the ground truth). Note these class labels are smeared, i.e., the labels have been propagated in the upwards direction to the root of the ontology. More details about the label smearing process can be found in Appendix D of our paper. 
	
mids: the Freebase identifiers corresponding to the class labels, as defined in the AudioSet Ontology specification
	
split: whether the clip belongs to train or val (see paper for details on the proposed split)



Rows in eval.csv follow the same format, except that there is no split column.


Note: We use a slightly different format than AudioSet for the naming of class labels in order to avoid potential problems with spaces, commas, etc. Example: we use Accelerating\_and\_revving\_and\_vroom instead of the original Accelerating, revving, vroom. You can go back to the original AudioSet naming using the information provided in vocabulary.csv (class label and mid for the 200 classes of FSD50K) and the AudioSet Ontology specification.


Files with additional metadata (FSD50K.metadata/)


To allow a variety of analysis and approaches with FSD50K, we provide the following metadata:



	

	
class\_info\_FSD50K.json: python dictionary where each entry corresponds to one sound class and  contains: FAQs utilized during the annotation of the class, examples (representative audio clips), and verification\_examples (audio clips presented to raters during annotation as a quality control mechanism). Audio clips are described by the Freesound id. Note: It may be that some of these examples are not included in the FSD50K release.
	
	

	
dev\_clips\_info\_FSD50K.json: python dictionary where each entry corresponds to one dev clip and contains: title, description, tags, clip license, and the uploader name. All these metadata are provided by the uploader.
	
	

	
eval\_clips\_info\_FSD50K.json: same as before, but with eval clips.
	
	

	
pp\_pnp\_ratings.json: python dictionary where each entry corresponds to one clip in the dataset and contains the PP/PNP ratings for the labels associated with the clip. More specifically, these ratings are gathered for the labels validated in the validation task (Sec. 3 of paper). This file includes 59,485 labels for the 51,197 clips in FSD50K. Out of these labels:

	

		
56,095 labels have inter-annotator agreement (PP twice, or PNP twice). Each of these combinations can be occasionally accompanied by other (non-positive) ratings.
		
3390 labels feature other rating configurations such as i) only one PP rating and one PNP rating (and nothing else). This can be considered inter-annotator agreement at the ``Present” level; ii) only one PP rating (and nothing else); iii) only one PNP rating (and nothing else).
	

	
Ratings' legend: PP=1; PNP=0.5; U=0; NP=-1.

	
Note: The PP/PNP ratings have been provided in the validation task. Subsequently, a subset of these clips corresponding to the eval set was exhaustively labeled in the refinement task, hence receiving additional labels in many cases. For these eval clips, you might want to check their labels in eval.csv in order to have more info about their audio content (see Sec. 3 for details).
	
	

	
collection/: This folder contains metadata for what we call the sound collection format. This format consists of the raw annotations gathered, featuring all generated class labels without any restriction. 

	
We provide the collection format to make available some annotations that do not appear in the FSD50K ground truth release. This typically happens in the case of classes for which we gathered human-provided annotations, but that were discarded in the FSD50K release due to data scarcity (more specifically, they were merged with their parents). In other words, the main purpose of the collection format is to make available annotations for tiny classes. The format of these files in analogous to that of the files in FSD50K.ground\_truth/. A couple of examples show the differences between collection and ground truth formats:

	
clip:  labels\_in\_collection    --    labels\_in\_ground\_truth

	
51690:  Owl    --    Bird,Wild\_Animal,Animal

	
190579:  Toothbrush,Electric\_toothbrush    --    Domestic\_sounds\_and\_home\_sounds

	
In the first example, raters provided the label Owl. However, due to data scarcity, Owl labels were merged into their parent Bird. Then, labels Wild\_Animal,Animal were added via label propagation (smearing). The second example shows one of the most extreme cases, where raters provided the labels Electric\_toothbrush,Toothbrush, which both had few data. Hence, they were merged into Toothbrush's parent, which unfortunately is Domestic\_sounds\_and\_home\_sounds (a rather vague class containing a variety of children sound classes).

	
Note: Labels in the collection format are not smeared.
	Note: While in FSD50K's ground truth the vocabulary encompasses 200 classes (common for dev and eval), since the collection format is composed of raw annotations, the vocabulary here is much larger (over 350 classes), and it is slightly different in dev and eval.
	



For further questions, please contact efonseca@google.com, or join the freesound-annotator Google Group.


 


DOWNLOAD


The folders FSD50K.ground\_truth/, FSD50K.metadata/ and FSD50K.doc/ are compressed into one zip file each. However, due to their large size, the folders FSD50K.dev\_audio/ and FSD50K.eval\_audio/ are split into several files. Specifically, FSD50K.dev\_audio/ is split into six files (note the last file is not *.z06, but *.zip): 


FSD50K.dev\_audio.z01
FSD50K.dev\_audio.z02
FSD50K.dev\_audio.z03
FSD50K.dev\_audio.z04
FSD50K.dev\_audio.z05
FSD50K.dev\_audio.zip


In this case, you first have to download all the files. Once downloaded, we merge all the files into one zip file called e.g. unsplit.zip in your local machine.


zip -s 0 FSD50K.dev\_audio.zip --out unsplit.zip


Finally, this merged file is unzipped.


unzip unsplit.zip


Similar guidelines must be followed for the FSD50K.eval\_audio/ folder (only two zip files in this case).


 


BASELINE SYSTEM


Several baseline systems for FSD50K are available at https://github.com/edufonseca/FSD50K\_baseline. The experiments are described in Sec 5 of our paper.


 


REFERENCES AND LINKS


[1] Jort F Gemmeke, Daniel PW Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter. "Audio set: An ontology and human-labeled dataset for audio events." In Proceedings of the International Conference on Acoustics, Speech and Signal Processing, 2017. [PDF]


[2] Eduardo Fonseca, Jordi Pons, Xavier Favory, Frederic Font, Dmitry Bogdanov, Andres Ferraro, Sergio Oramas, Alastair Porter, and Xavier Serra. "Freesound Datasets: A Platform for the Creation of Open Audio Datasets." In Proceedings of the International Conference on Music Information Retrieval, 2017. [PDF]


Companion site for FSD50K: https://annotator.freesound.org/fsd/release/FSD50K/
Freesound Annotator: https://annotator.freesound.org/
Freesound: https://freesound.org
Eduardo Fonseca's personal website: http://www.eduardofonseca.net/
More datasets collected by us: http://www.eduardofonseca.net/datasets/


 


ACKNOWLEDGMENTS


The authors would like to thank everyone who contributed to FSD50K with annotations, and especially Mercedes Collado, Ceren Can, Rachit Gupta, Javier Arredondo, Gary Avendano and Sara Fernandez for their commitment and perseverance. The authors would also like to thank Daniel P.W. Ellis and Manoj Plakal from Google Research for valuable discussions. This work is partially supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688382 AudioCommons, and two Google Faculty Research Awards 2017 and 2018, and the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).},
	urldate = {2024-06-06},
	publisher = {Zenodo},
	author = {Fonseca, Eduardo and Favory, Xavier and Pons, Jordi and Font, Frederic and Serra, Xavier},
	month = oct,
	year = {2020},
	keywords = {audio dataset, data collection, environmental sound, everyday sounds, sound event classification, sound event recognition, sound event tagging},
}

@inproceedings{gemmeke_audio_2017,
	address = {New Orleans, LA},
	title = {Audio {Set}: {An} ontology and human-labeled dataset for audio events},
	isbn = {978-1-5090-4117-6},
	shorttitle = {Audio {Set}},
	url = {http://ieeexplore.ieee.org/document/7952261/},
	doi = {10.1109/ICASSP.2017.7952261},
	abstract = {Audio event recognition, the human-like ability to identify and relate sounds from audio, is a nascent problem in machine perception. Comparable problems such as object detection in images have reaped enormous beneﬁts from comprehensive datasets – principally ImageNet. This paper describes the creation of Audio Set, a largescale dataset of manually-annotated audio events that endeavors to bridge the gap in data availability between image and audio research. Using a carefully structured hierarchical ontology of 632 audio classes guided by the literature and manual curation, we collect data from human labelers to probe the presence of speciﬁc audio classes in 10 second segments of YouTube videos. Segments are proposed for labeling using searches based on metadata, context (e.g., links), and content analysis. The result is a dataset of unprecedented breadth and size that will, we hope, substantially stimulate the development of high-performance audio event recognizers.},
	language = {en},
	urldate = {2024-06-06},
	booktitle = {2017 {IEEE} {International} {Conference} on {Acoustics}, {Speech} and {Signal} {Processing} ({ICASSP})},
	publisher = {IEEE},
	author = {Gemmeke, Jort F. and Ellis, Daniel P. W. and Freedman, Dylan and Jansen, Aren and Lawrence, Wade and Moore, R. Channing and Plakal, Manoj and Ritter, Marvin},
	month = mar,
	year = {2017},
	pages = {776--780},
}

@inproceedings{kim_audiocaps_2019-1,
	address = {Minneapolis, Minnesota},
	title = {{AudioCaps}: {Generating} {Captions} for {Audios} in {The} {Wild}},
	shorttitle = {{AudioCaps}},
	url = {https://aclanthology.org/N19-1011},
	doi = {10.18653/v1/N19-1011},
	abstract = {We explore the problem of Audio Captioning: generating natural language description for any kind of audio in the wild, which has been surprisingly unexplored in previous research. We contribute a large-scale dataset of 46K audio clips with human-written text pairs collected via crowdsourcing on the AudioSet dataset. Our thorough empirical studies not only show that our collected captions are indeed faithful to audio inputs but also discover what forms of audio representation and captioning models are effective for the audio captioning. From extensive experiments, we also propose two novel components that help improve audio captioning performance: the top-down multi-scale encoder and aligned semantic attention.},
	urldate = {2024-06-06},
	booktitle = {Proceedings of the 2019 {Conference} of the {North} {American} {Chapter} of the {Association} for {Computational} {Linguistics}: {Human} {Language} {Technologies}, {Volume} 1 ({Long} and {Short} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Kim, Chris Dongjoo and Kim, Byeongchang and Lee, Hyunmin and Kim, Gunhee},
	editor = {Burstein, Jill and Doran, Christy and Solorio, Thamar},
	month = jun,
	year = {2019},
	pages = {119--132},
}

@misc{hershey_cnn_2017,
	title = {{CNN} {Architectures} for {Large}-{Scale} {Audio} {Classification}},
	url = {http://arxiv.org/abs/1609.09430},
	doi = {10.48550/arXiv.1609.09430},
	abstract = {Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task.},
	urldate = {2024-06-06},
	publisher = {arXiv},
	author = {Hershey, Shawn and Chaudhuri, Sourish and Ellis, Daniel P. W. and Gemmeke, Jort F. and Jansen, Aren and Moore, R. Channing and Plakal, Manoj and Platt, Devin and Saurous, Rif A. and Seybold, Bryan and Slaney, Malcolm and Weiss, Ron J. and Wilson, Kevin},
	month = jan,
	year = {2017},
	note = {arXiv:1609.09430 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Statistics - Machine Learning},
}

@misc{szegedy_going_2014,
	title = {Going {Deeper} with {Convolutions}},
	url = {http://arxiv.org/abs/1409.4842},
	doi = {10.48550/arXiv.1409.4842},
	abstract = {We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. This was achieved by a carefully crafted design that allows for increasing the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC 2014 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.},
	urldate = {2024-06-06},
	publisher = {arXiv},
	author = {Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew},
	month = sep,
	year = {2014},
	note = {arXiv:1409.4842 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{kingma_auto-encoding_2022,
	title = {Auto-{Encoding} {Variational} {Bayes}},
	url = {http://arxiv.org/abs/1312.6114},
	doi = {10.48550/arXiv.1312.6114},
	abstract = {How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions are two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.},
	urldate = {2024-06-06},
	publisher = {arXiv},
	author = {Kingma, Diederik P. and Welling, Max},
	month = dec,
	year = {2022},
	note = {arXiv:1312.6114 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{choi_ilvr_2021,
	title = {{ILVR}: {Conditioning} {Method} for {Denoising} {Diffusion} {Probabilistic} {Models}},
	shorttitle = {{ILVR}},
	url = {http://arxiv.org/abs/2108.02938},
	doi = {10.48550/arXiv.2108.02938},
	abstract = {Denoising diffusion probabilistic models (DDPM) have shown remarkable performance in unconditional image generation. However, due to the stochasticity of the generative process in DDPM, it is challenging to generate images with the desired semantics. In this work, we propose Iterative Latent Variable Refinement (ILVR), a method to guide the generative process in DDPM to generate high-quality images based on a given reference image. Here, the refinement of the generative process in DDPM enables a single DDPM to sample images from various sets directed by the reference image. The proposed ILVR method generates high-quality images while controlling the generation. The controllability of our method allows adaptation of a single DDPM without any additional learning in various image generation tasks, such as generation from various downsampling factors, multi-domain image translation, paint-to-image, and editing with scribbles.},
	urldate = {2024-06-06},
	publisher = {arXiv},
	author = {Choi, Jooyoung and Kim, Sungwon and Jeong, Yonghyun and Gwon, Youngjune and Yoon, Sungroh},
	month = sep,
	year = {2021},
	note = {arXiv:2108.02938 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{koizumi_specgrad_2022,
	title = {{SpecGrad}: {Diffusion} {Probabilistic} {Model} based {Neural} {Vocoder} with {Adaptive} {Noise} {Spectral} {Shaping}},
	shorttitle = {{SpecGrad}},
	url = {http://arxiv.org/abs/2203.16749},
	doi = {10.48550/arXiv.2203.16749},
	abstract = {Neural vocoder using denoising diffusion probabilistic model (DDPM) has been improved by adaptation of the diffusion noise distribution to given acoustic features. In this study, we propose SpecGrad that adapts the diffusion noise so that its time-varying spectral envelope becomes close to the conditioning log-mel spectrogram. This adaptation by time-varying filtering improves the sound quality especially in the high-frequency bands. It is processed in the time-frequency domain to keep the computational cost almost the same as the conventional DDPM-based neural vocoders. Experimental results showed that SpecGrad generates higher-fidelity speech waveform than conventional DDPM-based neural vocoders in both analysis-synthesis and speech enhancement scenarios. Audio demos are available at wavegrad.github.io/specgrad/.},
	urldate = {2024-06-06},
	publisher = {arXiv},
	author = {Koizumi, Yuma and Zen, Heiga and Yatabe, Kohei and Chen, Nanxin and Bacchiani, Michiel},
	month = aug,
	year = {2022},
	note = {arXiv:2203.16749 [cs, eess, stat]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning},
}

@misc{lopez-espejo_deep_2021,
	title = {Deep {Spoken} {Keyword} {Spotting}: {An} {Overview}},
	shorttitle = {Deep {Spoken} {Keyword} {Spotting}},
	url = {http://arxiv.org/abs/2111.10592},
	doi = {10.48550/arXiv.2111.10592},
	abstract = {Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams and has become a fast-growing technology thanks to the paradigm shift introduced by deep learning a few years ago. This has allowed the rapid embedding of deep KWS in a myriad of small electronic devices with different purposes like the activation of voice assistants. Prospects suggest a sustained growth in terms of social use of this technology. Thus, it is not surprising that deep KWS has become a hot research topic among speech scientists, who constantly look for KWS performance improvement and computational complexity reduction. This context motivates this paper, in which we conduct a literature review into deep spoken KWS to assist practitioners and researchers who are interested in this technology. Specifically, this overview has a comprehensive nature by covering a thorough analysis of deep KWS systems (which includes speech features, acoustic modeling and posterior handling), robustness methods, applications, datasets, evaluation metrics, performance of deep KWS systems and audio-visual KWS. The analysis performed in this paper allows us to identify a number of directions for future research, including directions adopted from automatic speech recognition research and directions that are unique to the problem of spoken KWS.},
	urldate = {2024-06-06},
	publisher = {arXiv},
	author = {López-Espejo, Iván and Tan, Zheng-Hua and Hansen, John and Jensen, Jesper},
	month = nov,
	year = {2021},
	note = {arXiv:2111.10592 [cs, eess]},
	keywords = {Computer Science - Human-Computer Interaction, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@article{de_souza_multitaper-mel_2022,
	title = {Multitaper-{Mel} {Spectrograms} for {Keyword} {Spotting}},
	volume = {29},
	issn = {1558-2361},
	url = {https://ieeexplore.ieee.org/document/9896956},
	doi = {10.1109/LSP.2022.3208411},
	abstract = {Keyword spotting (KWS) is one of the speech recognition tasks most sensitive to the quality of the feature representation. However, the research on KWS has traditionally focused on new model topologies, putting little emphasis on other aspects like feature extraction. This paper investigates the use of the multitaper technique to create improved features for KWS. The experimental study is carried out for different test scenarios, windows and parameters, datasets, and neural networks commonly used in embedded KWS applications. Experiment results confirm the advantages of using the proposed improved features.},
	urldate = {2024-06-06},
	journal = {IEEE Signal Processing Letters},
	author = {de Souza, Douglas Baptista and Bakri, Khaled Jamal and Ferreira, Fernanda de Souza and Inacio, Juliana},
	year = {2022},
	note = {Conference Name: IEEE Signal Processing Letters},
	keywords = {Computational modeling, Feature extraction, Hidden Markov models, Internet, Keyword spotting, Spectrogram, Speech recognition, Training, feature extraction, mel spectrograms, multitaper spectrograms},
	pages = {2028--2032},
}

@misc{noauthor_ieee_nodate,
	title = {{IEEE} {Xplore} {Full}-{Text} {PDF}:},
	url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9896956},
	urldate = {2024-06-06},
}

@misc{foleiss_random_2019,
	title = {Random {Projections} of {Mel}-{Spectrograms} as {Low}-{Level} {Features} for {Automatic} {Music} {Genre} {Classification}},
	url = {http://arxiv.org/abs/1911.04660},
	doi = {10.48550/arXiv.1911.04660},
	abstract = {In this work, we analyse the random projections of Mel-spectrograms as low-level features for music genre classification. This approach was compared to handcrafted features, features learned using an auto-encoder and features obtained from a transfer learning setting. Tests in five different well-known, publicly available datasets show that random projections leads to results comparable to learned features and outperforms features obtained via transfer learning in a shallow learning scenario. Random projections do not require using extensive specialist knowledge and, simultaneously, requires less computational power for training than other projection-based low-level features. Therefore, they can be are a viable choice for usage in shallow learning content-based music genre classification.},
	urldate = {2024-06-06},
	publisher = {arXiv},
	author = {Foleiss, Juliano Henrique and Tavares, Tiago Fernandes},
	month = nov,
	year = {2019},
	note = {arXiv:1911.04660 [cs, eess]},
	keywords = {Computer Science - Information Retrieval, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@article{deruty_development_2022,
	title = {On the {Development} and {Practice} of {AI} {Technology} for {Contemporary} {Popular} {Music} {Production}},
	volume = {5},
	doi = {10.5334/tismir.100},
	abstract = {Although the use of AI technology for music production is still in its infancy, it has the potential to make a lasting impact on the way we produce music. In this paper we focus on the design and use of AI music tools for the production of contemporary Popular Music, in particular genres involving studio technology as part of the creative process. First we discuss how music production practices associated with those genres can differ significantly from traditional views of how a musical work is created, and how this affects AI music technology. We argue that—given the role of symbolic representations in this context, as well as the integration of composition activities with editing and mixing—audio-based AI tools are better suited to support the artist’s creative workflow than purely piano-roll/MIDI-based tools. Then we give a report of collaborations with professional artists, in which we look at how various AI tools are used in practice to produce music. We identify usage patterns as well as issues and challenges that arise in practical use of the tools. Based on this we formulate some recommendations and validation criteria for the development of AI technology for contemporary Popular Music.},
	journal = {Transactions of the International Society for Music Information Retrieval},
	author = {Deruty, Emmanuel and Grachten, Maarten and Lattner, Stefan and Nistal, Javier and Aouameur, Cyran},
	month = feb,
	year = {2022},
	pages = {35},
}

@misc{bozic_survey_2024,
	title = {A {Survey} of {Deep} {Learning} {Audio} {Generation} {Methods}},
	url = {http://arxiv.org/abs/2406.00146},
	abstract = {This article presents a review of typical techniques used in three distinct aspects of deep learning model development for audio generation. In the first part of the article, we provide an explanation of audio representations, beginning with the fundamental audio waveform. We then progress to the frequency domain, with an emphasis on the attributes of human hearing, and finally introduce a relatively recent development. The main part of the article focuses on explaining basic and extended deep learning architecture variants, along with their practical applications in the field of audio generation. The following architectures are addressed: 1) Autoencoders 2) Generative adversarial networks 3) Normalizing flows 4) Transformer networks 5) Diffusion models. Lastly, we will examine four distinct evaluation metrics that are commonly employed in audio generation. This article aims to offer novice readers and beginners in the field a comprehensive understanding of the current state of the art in audio generation methods as well as relevant studies that can be explored for future research.},
	language = {en},
	urldate = {2024-06-06},
	publisher = {arXiv},
	author = {Božić, Matej and Horvat, Marko},
	month = may,
	year = {2024},
	note = {arXiv:2406.00146 [cs, eess]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@book{zhu_survey_2023,
	title = {A {Survey} of {AI} {Music} {Generation} {Tools} and {Models}},
	abstract = {In this work, we provide a comprehensive survey of AI music generation tools, including both research projects and commercialized applications. To conduct our analysis, we classified music generation approaches into three categories: parameter-based, text-based, and visual-based classes. Our survey highlights the diverse possibilities and functional features of these tools, which cater to a wide range of users, from regular listeners to professional musicians. We observed that each tool has its own set of advantages and limitations. As a result, we have compiled a comprehensive list of these factors that should be considered during the tool selection process. Moreover, our survey offers critical insights into the underlying mechanisms and challenges of AI music generation.},
	author = {Zhu, Yueyue and Baca, Jared and Rekabdar, Banafsheh and Rawassizadeh, Reza},
	month = aug,
	year = {2023},
}

@misc{defferrard_fma_2017,
	title = {{FMA}: {A} {Dataset} {For} {Music} {Analysis}},
	shorttitle = {{FMA}},
	url = {http://arxiv.org/abs/1612.01840},
	doi = {10.48550/arXiv.1612.01840},
	abstract = {We introduce the Free Music Archive (FMA), an open and easily accessible dataset suitable for evaluating several tasks in MIR, a field concerned with browsing, searching, and organizing large music collections. The community's growing interest in feature and end-to-end learning is however restrained by the limited availability of large audio datasets. The FMA aims to overcome this hurdle by providing 917 GiB and 343 days of Creative Commons-licensed audio from 106,574 tracks from 16,341 artists and 14,854 albums, arranged in a hierarchical taxonomy of 161 genres. It provides full-length and high-quality audio, pre-computed features, together with track- and user-level metadata, tags, and free-form text such as biographies. We here describe the dataset and how it was created, propose a train/validation/test split and three subsets, discuss some suitable MIR tasks, and evaluate some baselines for genre recognition. Code, data, and usage examples are available at https://github.com/mdeff/fma},
	urldate = {2024-06-04},
	publisher = {arXiv},
	author = {Defferrard, Michaël and Benzi, Kirell and Vandergheynst, Pierre and Bresson, Xavier},
	month = sep,
	year = {2017},
	note = {arXiv:1612.01840 [cs]},
	keywords = {Computer Science - Information Retrieval, Computer Science - Sound},
}

@misc{defferrard_fma_2017-1,
	title = {{FMA}: {A} {Dataset} {For} {Music} {Analysis}},
	shorttitle = {{FMA}},
	url = {http://arxiv.org/abs/1612.01840},
	abstract = {We introduce the Free Music Archive (FMA), an open and easily accessible dataset suitable for evaluating several tasks in MIR, a ﬁeld concerned with browsing, searching, and organizing large music collections. The community’s growing interest in feature and end-to-end learning is however restrained by the limited availability of large audio datasets. The FMA aims to overcome this hurdle by providing 917 GiB and 343 days of Creative Commonslicensed audio from 106,574 tracks from 16,341 artists and 14,854 albums, arranged in a hierarchical taxonomy of 161 genres. It provides full-length and high-quality audio, pre-computed features, together with track- and user-level metadata, tags, and free-form text such as biographies. We here describe the dataset and how it was created, propose a train/validation/test split and three subsets, discuss some suitable MIR tasks, and evaluate some baselines for genre recognition. Code, data, and usage examples are available at https://github.com/mdeff/fma.},
	language = {en},
	urldate = {2024-06-04},
	publisher = {arXiv},
	author = {Defferrard, Michaël and Benzi, Kirell and Vandergheynst, Pierre and Bresson, Xavier},
	month = sep,
	year = {2017},
	note = {arXiv:1612.01840 [cs]},
	keywords = {Computer Science - Information Retrieval, Computer Science - Sound},
}

@misc{wang_review_2023,
	title = {A {Review} of {Intelligent} {Music} {Generation} {Systems}},
	url = {http://arxiv.org/abs/2211.09124},
	abstract = {With the introduction of ChatGPT, the public’s perception of AI-generated content (AIGC) has begun to reshape. Artificial intelligence has significantly reduced the barrier to entry for non-professionals in creative endeavors, enhancing the efficiency of content creation. Recent advancements have seen significant improvements in the quality of symbolic music generation, which is enabled by the use of modern generative algorithms to extract patterns implicit in a piece of music based on rule constraints or a musical corpus. Nevertheless, existing literature reviews tend to present a conventional and conservative perspective on future development trajectories, with a notable absence of thorough benchmarking of generative models. This paper provides a survey and analysis of recent intelligent music generation techniques, outlining their respective characteristics and discussing existing methods for evaluation. Additionally, the paper compares the different characteristics of music generation techniques in the East and West as well as analysing the field’s development prospects.},
	language = {en},
	urldate = {2024-06-02},
	publisher = {arXiv},
	author = {Wang, Lei and Zhao, Ziyi and Liu, Hanwei and Pang, Junwei and Qin, Yi and Wu, Qidi},
	month = nov,
	year = {2023},
	note = {arXiv:2211.09124 [cs, eess]},
	keywords = {68T01, Computer Science - Artificial Intelligence, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, J.5},
}

@article{cousens_corporeal_2018,
	title = {Corporeal {Vulnerability} and the {Temporality} of {Feminist} {Theory}},
	issn = {2398-3884, 1467-2219},
	url = {https://journals.sussex.ac.uk/index.php/sspt/article/view/71},
	doi = {10.20919/sspt.27.2017.71},
	abstract = {In recent years, Judith Butler has been credited with advancing a ‘theory’ of vulnerability that addresses the concerns of previous articulations of the concept within feminist thought. This article will closely elucidate Butler’s presentation of this term, distinguishing it from other proximately related terms in her work: precarity and precariousness. It will then proceed to level two, related, criticisms at what is a highly important contribution to contemporary feminist thought. First, it will argue that the emerging discourse of vulnerability within feminism tends to present there as being an ‘old’ and ‘new’ formulation of the concept, that correspond to ‘bad’ and ‘good’ respectively. I will argue that such an approach is emblematic of the ‘progress narrative’ within much feminist academia, where authors are compelled to situate themselves against their forbearers. This leads to the second criticism: that this process of dismissing past feminist reflections on vulnerability has led to a lacuna in feminist thought regarding the possibility of theorising sexual violence.},
	language = {en},
	urldate = {2024-05-31},
	journal = {Studies in Social and Political Thought},
	author = {Cousens, Emily},
	month = jan,
	year = {2018},
}

@misc{kumar_high-fidelity_2023,
	title = {High-{Fidelity} {Audio} {Compression} with {Improved} {RVQGAN}},
	url = {http://arxiv.org/abs/2306.06546},
	doi = {10.48550/arXiv.2306.06546},
	abstract = {Language models have been successfully used to model natural signals, such as images, speech, and music. A key component of these models is a high quality neural compression model that can compress high-dimensional natural signals into lower dimensional discrete tokens. To that end, we introduce a high-fidelity universal neural audio compression algorithm that achieves {\textasciitilde}90x compression of 44.1 KHz audio into tokens at just 8kbps bandwidth. We achieve this by combining advances in high-fidelity audio generation with better vector quantization techniques from the image domain, along with improved adversarial and reconstruction losses. We compress all domains (speech, environment, music, etc.) with a single universal model, making it widely applicable to generative modeling of all audio. We compare with competing audio compression algorithms, and find our method outperforms them significantly. We provide thorough ablations for every design choice, as well as open-source code and trained model weights. We hope our work can lay the foundation for the next generation of high-fidelity audio modeling.},
	urldate = {2024-05-28},
	publisher = {arXiv},
	author = {Kumar, Rithesh and Seetharaman, Prem and Luebs, Alejandro and Kumar, Ishaan and Kumar, Kundan},
	month = oct,
	year = {2023},
	note = {arXiv:2306.06546 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{defossez_high_2022,
	title = {High {Fidelity} {Neural} {Audio} {Compression}},
	url = {http://arxiv.org/abs/2210.13438},
	doi = {10.48550/arXiv.2210.13438},
	abstract = {We introduce a state-of-the-art real-time, high-fidelity, audio codec leveraging neural networks. It consists in a streaming encoder-decoder architecture with quantized latent space trained in an end-to-end fashion. We simplify and speed-up the training by using a single multiscale spectrogram adversary that efficiently reduces artifacts and produce high-quality samples. We introduce a novel loss balancer mechanism to stabilize training: the weight of a loss now defines the fraction of the overall gradient it should represent, thus decoupling the choice of this hyper-parameter from the typical scale of the loss. Finally, we study how lightweight Transformer models can be used to further compress the obtained representation by up to 40\%, while staying faster than real time. We provide a detailed description of the key design choices of the proposed model including: training objective, architectural changes and a study of various perceptual loss functions. We present an extensive subjective evaluation (MUSHRA tests) together with an ablation study for a range of bandwidths and audio domains, including speech, noisy-reverberant speech, and music. Our approach is superior to the baselines methods across all evaluated settings, considering both 24 kHz monophonic and 48 kHz stereophonic audio. Code and models are available at github.com/facebookresearch/encodec.},
	urldate = {2024-05-28},
	publisher = {arXiv},
	author = {Défossez, Alexandre and Copet, Jade and Synnaeve, Gabriel and Adi, Yossi},
	month = oct,
	year = {2022},
	note = {arXiv:2210.13438 [cs, eess, stat]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning},
}

@misc{manocha_cdpam_2021,
	title = {{CDPAM}: {Contrastive} learning for perceptual audio similarity},
	shorttitle = {{CDPAM}},
	url = {http://arxiv.org/abs/2102.05109},
	abstract = {Many speech processing methods based on deep learning require an automatic and differentiable audio metric for the loss function. The DPAM approach of Manocha et al. [1] learns a full-reference metric trained directly on human judgments, and thus correlates well with human perception. However, it requires a large number of human annotations and does not generalize well outside the range of perturbations on which it was trained. This paper introduces CDPAM – a metric that builds on and advances DPAM. The primary improvement is to combine contrastive learning and multi-dimensional representations to build robust models from limited data. In addition, we collect human judgments on triplet comparisons to improve generalization to a broader range of audio perturbations. CDPAM correlates well with human responses across nine varied datasets. We also show that adding this metric to existing speech synthesis and enhancement methods yields signiﬁcant improvement, as measured by objective and subjective tests.},
	language = {en},
	urldate = {2024-05-28},
	publisher = {arXiv},
	author = {Manocha, Pranay and Jin, Zeyu and Zhang, Richard and Finkelstein, Adam},
	month = feb,
	year = {2021},
	note = {arXiv:2102.05109 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{noauthor_iclr_nodate,
	title = {{ICLR} {Poster} {MERT}: {Acoustic} {Music} {Understanding} {Model} with {Large}-{Scale} {Self}-supervised {Training}},
	url = {https://iclr.cc/virtual/2024/poster/17510},
	urldate = {2024-05-28},
}

@misc{noauthor_2024_nodate,
	title = {2024 {Conference}},
	url = {https://iclr.cc/Conferences/2024},
	urldate = {2024-05-28},
}

@misc{wu_large-scale_2024,
	title = {Large-scale {Contrastive} {Language}-{Audio} {Pretraining} with {Feature} {Fusion} and {Keyword}-to-{Caption} {Augmentation}},
	url = {http://arxiv.org/abs/2211.06687},
	doi = {10.48550/arXiv.2211.06687},
	abstract = {Contrastive learning has shown remarkable success in the field of multimodal representation learning. In this paper, we propose a pipeline of contrastive language-audio pretraining to develop an audio representation by combining audio data with natural language descriptions. To accomplish this target, we first release LAION-Audio-630K, a large collection of 633,526 audio-text pairs from different data sources. Second, we construct a contrastive language-audio pretraining model by considering different audio encoders and text encoders. We incorporate the feature fusion mechanism and keyword-to-caption augmentation into the model design to further enable the model to process audio inputs of variable lengths and enhance the performance. Third, we perform comprehensive experiments to evaluate our model across three tasks: text-to-audio retrieval, zero-shot audio classification, and supervised audio classification. The results demonstrate that our model achieves superior performance in text-to-audio retrieval task. In audio classification tasks, the model achieves state-of-the-art performance in the zero-shot setting and is able to obtain performance comparable to models' results in the non-zero-shot setting. LAION-Audio-630K and the proposed model are both available to the public.},
	urldate = {2024-05-28},
	publisher = {arXiv},
	author = {Wu, Yusong and Chen, Ke and Zhang, Tianyu and Hui, Yuchen and Nezhurina, Marianna and Berg-Kirkpatrick, Taylor and Dubnov, Shlomo},
	month = mar,
	year = {2024},
	note = {arXiv:2211.06687 [cs, eess]},
	keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@inproceedings{elizalde_natural_2024,
	title = {Natural {Language} {Supervision} {For} {General}-{Purpose} {Audio} {Representations}},
	doi = {10.1109/ICASSP48485.2024.10448504},
	author = {Elizalde, Benjamin and Deshmukh, Soham and Wang, Huaming},
	month = apr,
	year = {2024},
	pages = {336--340},
}

@misc{hershey_cnn_2017-1,
	title = {{CNN} {Architectures} for {Large}-{Scale} {Audio} {Classification}},
	url = {http://arxiv.org/abs/1609.09430},
	doi = {10.48550/arXiv.1609.09430},
	abstract = {Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task.},
	urldate = {2024-05-28},
	publisher = {arXiv},
	author = {Hershey, Shawn and Chaudhuri, Sourish and Ellis, Daniel P. W. and Gemmeke, Jort F. and Jansen, Aren and Moore, R. Channing and Plakal, Manoj and Platt, Devin and Saurous, Rif A. and Seybold, Bryan and Slaney, Malcolm and Weiss, Ron J. and Wilson, Kevin},
	month = jan,
	year = {2017},
	note = {arXiv:1609.09430 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Statistics - Machine Learning},
}

@misc{liu_gla-grad_2024,
	title = {{GLA}-{Grad}: {A} {Griffin}-{Lim} {Extended} {Waveform} {Generation} {Diffusion} {Model}},
	shorttitle = {{GLA}-{Grad}},
	url = {http://arxiv.org/abs/2402.15516},
	doi = {10.48550/arXiv.2402.15516},
	abstract = {Diffusion models are receiving a growing interest for a variety of signal generation tasks such as speech or music synthesis. WaveGrad, for example, is a successful diffusion model that conditionally uses the mel spectrogram to guide a diffusion process for the generation of high-fidelity audio. However, such models face important challenges concerning the noise diffusion process for training and inference, and they have difficulty generating high-quality speech for speakers that were not seen during training. With the aim of minimizing the conditioning error and increasing the efficiency of the noise diffusion process, we propose in this paper a new scheme called GLA-Grad, which consists in introducing a phase recovery algorithm such as the Griffin-Lim algorithm (GLA) at each step of the regular diffusion process. Furthermore, it can be directly applied to an already-trained waveform generation model, without additional training or fine-tuning. We show that our algorithm outperforms state-of-the-art diffusion models for speech generation, especially when generating speech for a previously unseen target speaker.},
	urldate = {2024-05-28},
	publisher = {arXiv},
	author = {Liu, Haocheng and Baoueb, Teysir and Fontaine, Mathieu and Roux, Jonathan Le and Richard, Gael},
	month = feb,
	year = {2024},
	note = {arXiv:2402.15516 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Electrical Engineering and Systems Science - Signal Processing},
}

@misc{gui_adapting_2024,
	title = {Adapting {Frechet} {Audio} {Distance} for {Generative} {Music} {Evaluation}},
	url = {http://arxiv.org/abs/2311.01616},
	doi = {10.48550/arXiv.2311.01616},
	abstract = {The growing popularity of generative music models underlines the need for perceptually relevant, objective music quality metrics. The Frechet Audio Distance (FAD) is commonly used for this purpose even though its correlation with perceptual quality is understudied. We show that FAD performance may be hampered by sample size bias, poor choice of audio embeddings, or the use of biased or low-quality reference sets. We propose reducing sample size bias by extrapolating scores towards an infinite sample size. Through comparisons with MusicCaps labels and a listening test we identify audio embeddings and music reference sets that yield FAD scores well-correlated with acoustic and musical quality. Our results suggest that per-song FAD can be useful to identify outlier samples and predict perceptual quality for a range of music sets and generative models. Finally, we release a toolkit that allows adapting FAD for generative music evaluation.},
	urldate = {2024-05-14},
	author = {Gui, Azalea and Gamper, Hannes and Braun, Sebastian and Emmanouilidou, Dimitra},
	month = mar,
	year = {2024},
	note = {arXiv:2311.01616 [eess]},
	keywords = {Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{engel_gansynth_2019,
	title = {{GANSynth}: {Adversarial} {Neural} {Audio} {Synthesis}},
	shorttitle = {{GANSynth}},
	url = {http://arxiv.org/abs/1902.08710},
	abstract = {Efficient audio synthesis is an inherently difficult machine learning task, as human perception is sensitive to both global structure and fine-scale waveform coherence. Autoregressive models, such as WaveNet, model local structure at the expense of global latent structure and slow iterative sampling, while Generative Adversarial Networks (GANs), have global latent conditioning and efficient parallel sampling, but struggle to generate locally-coherent audio waveforms. Herein, we demonstrate that GANs can in fact generate high-fidelity and locally-coherent audio by modeling log magnitudes and instantaneous frequencies with sufficient frequency resolution in the spectral domain. Through extensive empirical investigations on the NSynth dataset, we demonstrate that GANs are able to outperform strong WaveNet baselines on automated and human evaluation metrics, and efficiently generate audio several orders of magnitude faster than their autoregressive counterparts.},
	language = {en},
	urldate = {2024-05-11},
	publisher = {arXiv},
	author = {Engel, Jesse and Agrawal, Kumar Krishna and Chen, Shuo and Gulrajani, Ishaan and Donahue, Chris and Roberts, Adam},
	month = apr,
	year = {2019},
	note = {arXiv:1902.08710 [cs, eess, stat]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning},
}

@article{koepke_audio_2023,
	title = {Audio {Retrieval} with {Natural} {Language} {Queries}: {A} {Benchmark} {Study}},
	volume = {25},
	issn = {1520-9210, 1941-0077},
	shorttitle = {Audio {Retrieval} with {Natural} {Language} {Queries}},
	url = {http://arxiv.org/abs/2112.09418},
	doi = {10.1109/TMM.2022.3149712},
	abstract = {The objectives of this work are cross-modal text-audio and audio-text retrieval, in which the goal is to retrieve the audio content from a pool of candidates that best matches a given written description and vice versa. Text-audio retrieval enables users to search large databases through an intuitive interface: they simply issue free-form natural language descriptions of the sound they would like to hear. To study the tasks of text-audio and audio-text retrieval, which have received limited attention in the existing literature, we introduce three challenging new benchmarks. We first construct text-audio and audio-text retrieval benchmarks from the AudioCaps and Clotho audio captioning datasets. Additionally, we introduce the SoundDescs benchmark, which consists of paired audio and natural language descriptions for a diverse collection of sounds that are complementary to those found in AudioCaps and Clotho. We employ these three benchmarks to establish baselines for cross-modal text-audio and audio-text retrieval, where we demonstrate the benefits of pre-training on diverse audio tasks. We hope that our benchmarks will inspire further research into audio retrieval with free-form text queries. Code, audio features for all datasets used, and the SoundDescs dataset are publicly available at https://github.com/akoepke/audio-retrieval-benchmark.},
	urldate = {2024-05-08},
	journal = {IEEE Transactions on Multimedia},
	author = {Koepke, A. Sophia and Oncescu, Andreea-Maria and Henriques, João F. and Akata, Zeynep and Albanie, Samuel},
	year = {2023},
	note = {arXiv:2112.09418 [cs, eess]},
	keywords = {Computer Science - Information Retrieval, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
	pages = {2675--2685},
}

@misc{drossos_clotho_2019,
	title = {Clotho: {An} {Audio} {Captioning} {Dataset}},
	shorttitle = {Clotho},
	url = {http://arxiv.org/abs/1910.09387},
	doi = {10.48550/arXiv.1910.09387},
	abstract = {Audio captioning is the novel task of general audio content description using free text. It is an intermodal translation task (not speech-to-text), where a system accepts as an input an audio signal and outputs the textual description (i.e. the caption) of that signal. In this paper we present Clotho, a dataset for audio captioning consisting of 4981 audio samples of 15 to 30 seconds duration and 24 905 captions of eight to 20 words length, and a baseline method to provide initial results. Clotho is built with focus on audio content and caption diversity, and the splits of the data are not hampering the training or evaluation of methods. All sounds are from the Freesound platform, and captions are crowdsourced using Amazon Mechanical Turk and annotators from English speaking countries. Unique words, named entities, and speech transcription are removed with post-processing. Clotho is freely available online (https://zenodo.org/record/3490684).},
	urldate = {2024-05-08},
	publisher = {arXiv},
	author = {Drossos, Konstantinos and Lipping, Samuel and Virtanen, Tuomas},
	month = oct,
	year = {2019},
	note = {arXiv:1910.09387 [cs, eess]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{lemercier_diffusion_2024,
	title = {Diffusion {Models} for {Audio} {Restoration}},
	url = {http://arxiv.org/abs/2402.09821},
	doi = {10.48550/arXiv.2402.09821},
	abstract = {With the development of audio playback devices and fast data transmission, the demand for high sound quality is rising, for both entertainment and communications. In this quest for better sound quality, challenges emerge from distortions and interferences originating at the recording side or caused by an imperfect transmission pipeline. To address this problem, audio restoration methods aim to recover clean sound signals from the corrupted input data. We present here audio restoration algorithms based on diffusion models, with a focus on speech enhancement and music restoration tasks. Traditional approaches, often grounded in handcrafted rules and statistical heuristics, have shaped our understanding of audio signals. In the past decades, there has been a notable shift towards data-driven methods that exploit the modeling capabilities of deep neural networks (DNNs). Deep generative models, and among them diffusion models, have emerged as powerful techniques for learning complex data distributions. However, relying solely on DNN-based learning approaches carries the risk of reducing interpretability, particularly when employing end-to-end models. Nonetheless, data-driven approaches allow more flexibility in comparison to statistical model-based frameworks whose performance depends on distributional and statistical assumptions that can be difficult to guarantee. Here, we aim to show that diffusion models can combine the best of both worlds and offer the opportunity to design audio restoration algorithms with a good degree of interpretability and a remarkable performance in terms of sound quality.},
	urldate = {2024-05-08},
	publisher = {arXiv},
	author = {Lemercier, Jean-Marie and Richter, Julius and Welker, Simon and Moliner, Eloi and Välimäki, Vesa and Gerkmann, Timo},
	month = feb,
	year = {2024},
	note = {arXiv:2402.09821 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{siuzdak_vocos_2023,
	title = {Vocos: {Closing} the gap between time-domain and {Fourier}-based neural vocoders for high-quality audio synthesis},
	shorttitle = {Vocos},
	url = {http://arxiv.org/abs/2306.00814},
	doi = {10.48550/arXiv.2306.00814},
	abstract = {Recent advancements in neural vocoding are predominantly driven by Generative Adversarial Networks (GANs) operating in the time-domain. While effective, this approach neglects the inductive bias offered by time-frequency representations, resulting in reduntant and computionally-intensive upsampling operations. Fourier-based time-frequency representation is an appealing alternative, aligning more accurately with human auditory perception, and benefitting from well-established fast algorithms for its computation. Nevertheless, direct reconstruction of complex-valued spectrograms has been historically problematic, primarily due to phase recovery issues. This study seeks to close this gap by presenting Vocos, a new model that directly generates Fourier spectral coefficients. Vocos not only matches the state-of-the-art in audio quality, as demonstrated in our evaluations, but it also substantially improves computational efficiency, achieving an order of magnitude increase in speed compared to prevailing time-domain neural vocoding approaches. The source code and model weights have been open-sourced at https://github.com/charactr-platform/vocos.},
	urldate = {2024-04-28},
	publisher = {arXiv},
	author = {Siuzdak, Hubert},
	month = oct,
	year = {2023},
	note = {arXiv:2306.00814 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{evans_long-form_2024,
	title = {Long-form music generation with latent diffusion},
	url = {http://arxiv.org/abs/2404.10301},
	doi = {10.48550/arXiv.2404.10301},
	abstract = {Audio-based generative models for music have seen great strides recently, but so far have not managed to produce full-length music tracks with coherent musical structure. We show that by training a generative model on long temporal contexts it is possible to produce long-form music of up to 4m45s. Our model consists of a diffusion-transformer operating on a highly downsampled continuous latent representation (latent rate of 21.5Hz). It obtains state-of-the-art generations according to metrics on audio quality and prompt alignment, and subjective tests reveal that it produces full-length music with coherent structure.},
	urldate = {2024-04-21},
	publisher = {arXiv},
	author = {Evans, Zach and Parker, Julian D. and Carr, C. J. and Zukowski, Zack and Taylor, Josiah and Pons, Jordi},
	month = apr,
	year = {2024},
	note = {arXiv:2404.10301 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{zhu_edmsound_2023,
	title = {{EDMSound}: {Spectrogram} {Based} {Diffusion} {Models} for {Efficient} and {High}-{Quality} {Audio} {Synthesis}},
	shorttitle = {{EDMSound}},
	url = {http://arxiv.org/abs/2311.08667},
	abstract = {Audio diffusion models can synthesize a wide variety of sounds. Existing models often operate on the latent domain with cascaded phase recovery modules to reconstruct waveform. This poses challenges when generating high-fidelity audio. In this paper, we propose EDMSound, a diffusion-based generative model in spectrogram domain under the framework of elucidated diffusion models (EDM). Combining with efficient deterministic sampler, we achieved similar Fr{\textbackslash}'echet audio distance (FAD) score as top-ranked baseline with only 10 steps and reached state-of-the-art performance with 50 steps on the DCASE2023 foley sound generation benchmark. We also revealed a potential concern regarding diffusion based audio generation models that they tend to generate samples with high perceptual similarity to the data from training data. Project page: https://agentcooper2002.github.io/EDMSound/},
	urldate = {2024-04-15},
	publisher = {arXiv},
	author = {Zhu, Ge and Wen, Yutong and Carbonneau, Marc-André and Duan, Zhiyao},
	month = nov,
	year = {2023},
	note = {arXiv:2311.08667 [cs, eess]},
	keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@article{mokry_audio_2020,
	title = {Audio {Inpainting}: {Revisited} and {Reweighted}},
	volume = {28},
	issn = {2329-9290, 2329-9304},
	shorttitle = {Audio {Inpainting}},
	url = {http://arxiv.org/abs/2001.02480},
	doi = {10.1109/TASLP.2020.3030486},
	abstract = {We deal with the problem of sparsity-based audio inpainting, i.e. filling in the missing segments of audio. A consequence of the approaches based on mathematical optimization is the insufficient amplitude of the signal in the filled gaps. Remaining in the framework based on sparsity and convex optimization, we propose improvements to audio inpainting, aiming at compensating for such an energy loss. The new ideas are based on different types of weighting, both in the coefficient and the time domains. We show that our propositions improve the inpainting performance in terms of both the SNR and ODG.},
	urldate = {2024-04-14},
	journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
	author = {Mokrý, Ondřej and Rajmic, Pavel},
	year = {2020},
	note = {arXiv:2001.02480 [cs, eess]},
	keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
	pages = {2906--2918},
}

@misc{huang_make--audio_2023,
	title = {Make-{An}-{Audio}: {Text}-{To}-{Audio} {Generation} with {Prompt}-{Enhanced} {Diffusion} {Models}},
	shorttitle = {Make-{An}-{Audio}},
	url = {http://arxiv.org/abs/2301.12661},
	doi = {10.48550/arXiv.2301.12661},
	abstract = {Large-scale multimodal generative modeling has created milestones in text-to-image and text-to-video generation. Its application to audio still lags behind for two main reasons: the lack of large-scale datasets with high-quality text-audio pairs, and the complexity of modeling long continuous audio data. In this work, we propose Make-An-Audio with a prompt-enhanced diffusion model that addresses these gaps by 1) introducing pseudo prompt enhancement with a distill-then-reprogram approach, it alleviates data scarcity with orders of magnitude concept compositions by using language-free audios; 2) leveraging spectrogram autoencoder to predict the self-supervised audio representation instead of waveforms. Together with robust contrastive language-audio pretraining (CLAP) representations, Make-An-Audio achieves state-of-the-art results in both objective and subjective benchmark evaluation. Moreover, we present its controllability and generalization for X-to-Audio with "No Modality Left Behind", for the first time unlocking the ability to generate high-definition, high-fidelity audios given a user-defined modality input. Audio samples are available at https://Text-to-Audio.github.io},
	urldate = {2024-04-14},
	publisher = {arXiv},
	author = {Huang, Rongjie and Huang, Jiawei and Yang, Dongchao and Ren, Yi and Liu, Luping and Li, Mingze and Ye, Zhenhui and Liu, Jinglin and Yin, Xiang and Zhao, Zhou},
	month = jan,
	year = {2023},
	note = {arXiv:2301.12661 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{zhang_survey_2023,
	title = {A {Survey} of {Diffusion} {Based} {Image} {Generation} {Models}: {Issues} and {Their} {Solutions}},
	shorttitle = {A {Survey} of {Diffusion} {Based} {Image} {Generation} {Models}},
	url = {http://arxiv.org/abs/2308.13142},
	doi = {10.48550/arXiv.2308.13142},
	abstract = {Recently, there has been significant progress in the development of large models. Following the success of ChatGPT, numerous language models have been introduced, demonstrating remarkable performance. Similar advancements have also been observed in image generation models, such as Google's Imagen model, OpenAI's DALL-E 2, and stable diffusion models, which have exhibited impressive capabilities in generating images. However, similar to large language models, these models still encounter unresolved challenges. Fortunately, the availability of open-source stable diffusion models and their underlying mathematical principles has enabled the academic community to extensively analyze the performance of current image generation models and make improvements based on this stable diffusion framework. This survey aims to examine the existing issues and the current solutions pertaining to image generation models.},
	urldate = {2024-04-14},
	publisher = {arXiv},
	author = {Zhang, Tianyi and Wang, Zheng and Huang, Jing and Tasnim, Mohiuddin Muhammad and Shi, Wei},
	month = aug,
	year = {2023},
	note = {arXiv:2308.13142 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition},
}

@misc{yang_uniaudio_2023,
	title = {{UniAudio}: {An} {Audio} {Foundation} {Model} {Toward} {Universal} {Audio} {Generation}},
	shorttitle = {{UniAudio}},
	url = {http://arxiv.org/abs/2310.00704},
	doi = {10.48550/arXiv.2310.00704},
	abstract = {Large Language models (LLM) have demonstrated the capability to handle a variety of generative tasks. This paper presents the UniAudio system, which, unlike prior task-specific approaches, leverages LLM techniques to generate multiple types of audio (including speech, sounds, music, and singing) with given input conditions. UniAudio 1) first tokenizes all types of target audio along with other condition modalities, 2) concatenates source-target pair as a single sequence, and 3) performs next-token prediction using LLM. Also, a multi-scale Transformer model is proposed to handle the overly long sequences caused by the residual vector quantization based neural codec in tokenization. Training of UniAudio is scaled up to 165K hours of audio and 1B parameters, based on all generative tasks, aiming to obtain sufficient prior knowledge not only in the intrinsic properties of audio but also the inter-relationship between audio and other modalities. Therefore, the trained UniAudio model has the potential to become a foundation model for universal audio generation: it shows strong capability in all trained tasks and can seamlessly support new audio generation tasks after simple fine-tuning. Experiments demonstrate that UniAudio achieves state-of-the-art or at least competitive results on most of the 11 tasks. Demo and code are released at https://github.com/yangdongchao/UniAudio},
	urldate = {2024-04-14},
	publisher = {arXiv},
	author = {Yang, Dongchao and Tian, Jinchuan and Tan, Xu and Huang, Rongjie and Liu, Songxiang and Chang, Xuankai and Shi, Jiatong and Zhao, Sheng and Bian, Jiang and Wu, Xixin and Zhao, Zhou and Watanabe, Shinji and Meng, Helen},
	month = dec,
	year = {2023},
	note = {arXiv:2310.00704 [cs, eess]},
	keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{wang_review_2023-1,
	title = {A {Review} of {Intelligent} {Music} {Generation} {Systems}},
	url = {http://arxiv.org/abs/2211.09124},
	abstract = {With the introduction of ChatGPT, the public's perception of AI-generated content (AIGC) has begun to reshape. Artificial intelligence has significantly reduced the barrier to entry for non-professionals in creative endeavors, enhancing the efficiency of content creation. Recent advancements have seen significant improvements in the quality of symbolic music generation, which is enabled by the use of modern generative algorithms to extract patterns implicit in a piece of music based on rule constraints or a musical corpus. Nevertheless, existing literature reviews tend to present a conventional and conservative perspective on future development trajectories, with a notable absence of thorough benchmarking of generative models. This paper provides a survey and analysis of recent intelligent music generation techniques, outlining their respective characteristics and discussing existing methods for evaluation. Additionally, the paper compares the different characteristics of music generation techniques in the East and West as well as analysing the field's development prospects.},
	urldate = {2024-04-14},
	publisher = {arXiv},
	author = {Wang, Lei and Zhao, Ziyi and Liu, Hanwei and Pang, Junwei and Qin, Yi and Wu, Qidi},
	month = nov,
	year = {2023},
	note = {arXiv:2211.09124 [cs, eess]},
	keywords = {68T01, Computer Science - Artificial Intelligence, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, J.5},
}

@misc{ruiz_dreambooth_2023,
	title = {{DreamBooth}: {Fine} {Tuning} {Text}-to-{Image} {Diffusion} {Models} for {Subject}-{Driven} {Generation}},
	shorttitle = {{DreamBooth}},
	url = {http://arxiv.org/abs/2208.12242},
	doi = {10.48550/arXiv.2208.12242},
	abstract = {Large text-to-image models achieved a remarkable leap in the evolution of AI, enabling high-quality and diverse synthesis of images from a given text prompt. However, these models lack the ability to mimic the appearance of subjects in a given reference set and synthesize novel renditions of them in different contexts. In this work, we present a new approach for "personalization" of text-to-image diffusion models. Given as input just a few images of a subject, we fine-tune a pretrained text-to-image model such that it learns to bind a unique identifier with that specific subject. Once the subject is embedded in the output domain of the model, the unique identifier can be used to synthesize novel photorealistic images of the subject contextualized in different scenes. By leveraging the semantic prior embedded in the model with a new autogenous class-specific prior preservation loss, our technique enables synthesizing the subject in diverse scenes, poses, views and lighting conditions that do not appear in the reference images. We apply our technique to several previously-unassailable tasks, including subject recontextualization, text-guided view synthesis, and artistic rendering, all while preserving the subject's key features. We also provide a new dataset and evaluation protocol for this new task of subject-driven generation. Project page: https://dreambooth.github.io/},
	urldate = {2024-04-13},
	publisher = {arXiv},
	author = {Ruiz, Nataniel and Li, Yuanzhen and Jampani, Varun and Pritch, Yael and Rubinstein, Michael and Aberman, Kfir},
	month = mar,
	year = {2023},
	note = {arXiv:2208.12242 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics, Computer Science - Machine Learning},
}

@misc{pasini_bass_2024,
	title = {Bass {Accompaniment} {Generation} via {Latent} {Diffusion}},
	url = {http://arxiv.org/abs/2402.01412},
	doi = {10.48550/arXiv.2402.01412},
	abstract = {The ability to automatically generate music that appropriately matches an arbitrary input track is a challenging task. We present a novel controllable system for generating single stems to accompany musical mixes of arbitrary length. At the core of our method are audio autoencoders that efficiently compress audio waveform samples into invertible latent representations, and a conditional latent diffusion model that takes as input the latent encoding of a mix and generates the latent encoding of a corresponding stem. To provide control over the timbre of generated samples, we introduce a technique to ground the latent space to a user-provided reference style during diffusion sampling. For further improving audio quality, we adapt classifier-free guidance to avoid distortions at high guidance strengths when generating an unbounded latent space. We train our model on a dataset of pairs of mixes and matching bass stems. Quantitative experiments demonstrate that, given an input mix, the proposed system can generate basslines with user-specified timbres. Our controllable conditional audio generation framework represents a significant step forward in creating generative AI tools to assist musicians in music production.},
	urldate = {2024-04-12},
	publisher = {arXiv},
	author = {Pasini, Marco and Grachten, Maarten and Lattner, Stefan},
	month = feb,
	year = {2024},
	note = {arXiv:2402.01412 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@inproceedings{ribeiro_crowdmos_2011-1,
	title = {{CROWDMOS}: {An} approach for crowdsourcing mean opinion score studies},
	shorttitle = {{CROWDMOS}},
	url = {https://ieeexplore.ieee.org/document/5946971},
	doi = {10.1109/ICASSP.2011.5946971},
	abstract = {MOS (mean opinion score) subjective quality studies are used to evaluate many signal processing methods. Since laboratory quality studies are time consuming and expensive, researchers often run small studies with less statistical significance or use objective measures which only approximate human perception. We propose a cost-effective and convenient measure called crowdMOS, obtained by having internet users participate in a MOS-like listening study. Workers listen and rate sentences at their leisure, using their own hardware, in an environment of their choice. Since these individuals cannot be supervised, we propose methods for detecting and discarding inaccurate scores. To automate crowdMOS testing, we offer a set of freely distributable, open-source tools for Amazon Mechanical Turk, a platform designed to facilitate crowdsourcing. These tools implement the MOS testing methodology described in this pa per, providing researchers with a user-friendly means of performing subjective quality evaluations without the overhead associated with laboratory studies. Finally, we demonstrate the use of crowdMOS using data from the Blizzard text-to-speech competition, showing that it delivers accurate and repeatable results.},
	urldate = {2024-04-11},
	booktitle = {2011 {IEEE} {International} {Conference} on {Acoustics}, {Speech} and {Signal} {Processing} ({ICASSP})},
	author = {Ribeiro, Flávio and Florêncio, Dinei and Zhang, Cha and Seltzer, Michael},
	month = may,
	year = {2011},
	note = {ISSN: 2379-190X},
	keywords = {Hardware, Headphones, Laboratories, MOS, MUSHRA, Manganese, Reactive power, Speech, Throughput, crowdMOS, crowdsourcing, mean opinion score, mechanical turk, subjective quality},
	pages = {2416--2419},
}

@article{kong_panns_2020-1,
	title = {{PANNs}: {Large}-{Scale} {Pretrained} {Audio} {Neural} {Networks} for {Audio} {Pattern} {Recognition}},
	volume = {28},
	issn = {2329-9290},
	shorttitle = {{PANNs}},
	url = {https://dl.acm.org/doi/10.1109/TASLP.2020.3030497},
	doi = {10.1109/TASLP.2020.3030497},
	abstract = {Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification, music classification, speech emotion classification and sound event detection. Recently, neural networks have been applied to tackle audio pattern recognition problems. However, previous systems are built on specific datasets with limited durations. Recently, in computer vision and natural language processing, systems pretrained on large-scale datasets have generalized well to several tasks. However, there is limited research on pretraining systems on large-scale datasets for audio pattern recognition. In this paper, we propose pretrained audio neural networks (PANNs) trained on the large-scale AudioSet dataset. These PANNs are transferred to other audio related tasks. We investigate the performance and computational complexity of PANNs modeled by a variety of convolutional neural networks. We propose an architecture called Wavegram-Logmel-CNN using both log-mel spectrogram and waveform as input feature. Our best PANN system achieves a state-of-the-art mean average precision (mAP) of 0.439 on AudioSet tagging, outperforming the best previous system of 0.392. We transfer PANNs to six audio pattern recognition tasks, and demonstrate state-of-the-art performance in several of those tasks. We have released the source code and pretrained models of PANNs: https://github.com/qiuqiangkong/audioset\_tagging\_cnn.},
	urldate = {2024-04-11},
	journal = {IEEE/ACM Transactions on Audio, Speech and Language Processing},
	author = {Kong, Qiuqiang and Cao, Yin and Iqbal, Turab and Wang, Yuxuan and Wang, Wenwu and Plumbley, Mark D.},
	month = nov,
	year = {2020},
	pages = {2880--2894},
}

@inproceedings{gemmeke_audio_2017-1,
	address = {New Orleans, LA},
	title = {Audio {Set}: {An} ontology and human-labeled dataset for audio events},
	isbn = {978-1-5090-4117-6},
	shorttitle = {Audio {Set}},
	url = {http://ieeexplore.ieee.org/document/7952261/},
	doi = {10.1109/ICASSP.2017.7952261},
	abstract = {Audio event recognition, the human-like ability to identify and relate sounds from audio, is a nascent problem in machine perception. Comparable problems such as object detection in images have reaped enormous beneﬁts from comprehensive datasets – principally ImageNet. This paper describes the creation of Audio Set, a largescale dataset of manually-annotated audio events that endeavors to bridge the gap in data availability between image and audio research. Using a carefully structured hierarchical ontology of 632 audio classes guided by the literature and manual curation, we collect data from human labelers to probe the presence of speciﬁc audio classes in 10 second segments of YouTube videos. Segments are proposed for labeling using searches based on metadata, context (e.g., links), and content analysis. The result is a dataset of unprecedented breadth and size that will, we hope, substantially stimulate the development of high-performance audio event recognizers.},
	language = {en},
	urldate = {2024-04-11},
	booktitle = {2017 {IEEE} {International} {Conference} on {Acoustics}, {Speech} and {Signal} {Processing} ({ICASSP})},
	publisher = {IEEE},
	author = {Gemmeke, Jort F. and Ellis, Daniel P. W. and Freedman, Dylan and Jansen, Aren and Lawrence, Wade and Moore, R. Channing and Plakal, Manoj and Ritter, Marvin},
	month = mar,
	year = {2017},
	pages = {776--780},
}

@misc{noauthor_audio_nodate,
	title = {Audio {Set}: {An} ontology and human-labeled dataset for audio events},
	url = {https://research.google/pubs/audio-set-an-ontology-and-human-labeled-dataset-for-audio-events/},
	urldate = {2024-04-11},
}

@misc{simonyan_very_2015,
	title = {Very {Deep} {Convolutional} {Networks} for {Large}-{Scale} {Image} {Recognition}},
	url = {http://arxiv.org/abs/1409.1556},
	doi = {10.48550/arXiv.1409.1556},
	abstract = {In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.},
	urldate = {2024-04-11},
	publisher = {arXiv},
	author = {Simonyan, Karen and Zisserman, Andrew},
	month = apr,
	year = {2015},
	note = {arXiv:1409.1556 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{hershey_cnn_2017-2,
	title = {{CNN} {Architectures} for {Large}-{Scale} {Audio} {Classification}},
	url = {http://arxiv.org/abs/1609.09430},
	doi = {10.48550/arXiv.1609.09430},
	abstract = {Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task.},
	urldate = {2024-04-11},
	publisher = {arXiv},
	author = {Hershey, Shawn and Chaudhuri, Sourish and Ellis, Daniel P. W. and Gemmeke, Jort F. and Jansen, Aren and Moore, R. Channing and Plakal, Manoj and Platt, Devin and Saurous, Rif A. and Seybold, Bryan and Slaney, Malcolm and Weiss, Ron J. and Wilson, Kevin},
	month = jan,
	year = {2017},
	note = {arXiv:1609.09430 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Statistics - Machine Learning},
}

@misc{nistal_comparing_2020,
	title = {Comparing {Representations} for {Audio} {Synthesis} {Using} {Generative} {Adversarial} {Networks}},
	url = {http://arxiv.org/abs/2006.09266},
	doi = {10.48550/arXiv.2006.09266},
	abstract = {In this paper, we compare different audio signal representations, including the raw audio waveform and a variety of time-frequency representations, for the task of audio synthesis with Generative Adversarial Networks (GANs). We conduct the experiments on a subset of the NSynth dataset. The architecture follows the benchmark Progressive Growing Wasserstein GAN. We perform experiments both in a fully non-conditional manner as well as conditioning the network on the pitch information. We quantitatively evaluate the generated material utilizing standard metrics for assessing generative models, and compare training and sampling times. We show that complex-valued as well as the magnitude and Instantaneous Frequency of the Short-Time Fourier Transform achieve the best results, and yield fast generation and inversion times. The code for feature extraction, training and evaluating the model is available online.},
	urldate = {2024-04-10},
	publisher = {arXiv},
	author = {Nistal, Javier and Lattner, Stefan and Richard, Gaël},
	month = jun,
	year = {2020},
	note = {arXiv:2006.09266 [cs, eess]},
	keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{saharia_photorealistic_2022,
	title = {Photorealistic {Text}-to-{Image} {Diffusion} {Models} with {Deep} {Language} {Understanding}},
	url = {http://arxiv.org/abs/2205.11487},
	doi = {10.48550/arXiv.2205.11487},
	abstract = {We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation. Our key discovery is that generic large language models (e.g. T5), pretrained on text-only corpora, are surprisingly effective at encoding text for image synthesis: increasing the size of the language model in Imagen boosts both sample fidelity and image-text alignment much more than increasing the size of the image diffusion model. Imagen achieves a new state-of-the-art FID score of 7.27 on the COCO dataset, without ever training on COCO, and human raters find Imagen samples to be on par with the COCO data itself in image-text alignment. To assess text-to-image models in greater depth, we introduce DrawBench, a comprehensive and challenging benchmark for text-to-image models. With DrawBench, we compare Imagen with recent methods including VQ-GAN+CLIP, Latent Diffusion Models, and DALL-E 2, and find that human raters prefer Imagen over other models in side-by-side comparisons, both in terms of sample quality and image-text alignment. See https://imagen.research.google/ for an overview of the results.},
	urldate = {2024-04-10},
	publisher = {arXiv},
	author = {Saharia, Chitwan and Chan, William and Saxena, Saurabh and Li, Lala and Whang, Jay and Denton, Emily and Ghasemipour, Seyed Kamyar Seyed and Ayan, Burcu Karagol and Mahdavi, S. Sara and Lopes, Rapha Gontijo and Salimans, Tim and Ho, Jonathan and Fleet, David J. and Norouzi, Mohammad},
	month = may,
	year = {2022},
	note = {arXiv:2205.11487 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@article{lee_bigvgan_2023,
	title = {{BIGVGAN}: {A} {UNIVERSAL} {NEURAL} {VOCODER} {WITH} {LARGE}-{SCALE} {TRAINING}},
	abstract = {Despite recent progress in generative adversarial network (GAN)-based vocoders, where the model generates raw waveform conditioned on acoustic features, it is challenging to synthesize high-fidelity audio for numerous speakers across various recording environments. In this work, we present BigVGAN, a universal vocoder that generalizes well for various out-of-distribution scenarios without fine-tuning. We introduce periodic activation function and anti-aliased representation into the GAN generator, which brings the desired inductive bias for audio synthesis and significantly improves audio quality. In addition, we train our GAN vocoder at the largest scale up to 112M parameters, which is unprecedented in the literature. We identify and address the failure modes in large-scale GAN training for audio, while maintaining high-fidelity output without over-regularization. Our BigVGAN, trained only on clean speech (LibriTTS), achieves the state-of-the-art performance for various zero-shot (out-of-distribution) conditions, including unseen speakers, languages, recording environments, singing voices, music, and instrumental audio. 1 We release our code and model at: https://github.com/NVIDIA/BigVGAN.},
	language = {en},
	author = {Lee, Sang-gil and Ping, Wei and Ginsburg, Boris and Catanzaro, Bryan and Yoon, Sungroh},
	year = {2023},
}

@article{morise_world_2016,
	title = {{WORLD}: {A} {Vocoder}-{Based} {High}-{Quality} {Speech} {Synthesis} {System} for {Real}-{Time} {Applications}},
	volume = {E99.D},
	shorttitle = {{WORLD}},
	doi = {10.1587/transinf.2015EDP7457},
	abstract = {A vocoder-based speech synthesis system, named WORLD, was developed in an effort to improve the sound quality of real-time applications using speech. Speech analysis, manipulation, and synthesis on the basis of vocoders are used in various kinds of speech research. Although several high-quality speech synthesis systems have been developed, real-time processing has been difficult with them because of their high computational costs. This new speech synthesis system has not only sound quality but also quick processing. It consists of three analysis algorithms and one synthesis algorithm proposed in our previous research. The effectiveness of the system was evaluated by comparing its output with against natural speech including consonants. Its processing speed was also compared with those of conventional systems. The results showed that WORLD was superior to the other systems in terms of both sound quality and processing speed. In particular, it was over ten times faster than the conventional systems, and the real time factor (RTF) indicated that it was fast enough for real-time processing.},
	number = {7},
	journal = {IEICE Transactions on Information and Systems},
	author = {Morise, Masanori and Yokomori, Fumiya and Ozawa, Kenji},
	year = {2016},
	keywords = {real-time processing, sound quality, speech analysis, speech synthesis, vocoder},
	pages = {1877--1884},
}

@misc{kong_hifi-gan_2020,
	title = {{HiFi}-{GAN}: {Generative} {Adversarial} {Networks} for {Efficient} and {High} {Fidelity} {Speech} {Synthesis}},
	shorttitle = {{HiFi}-{GAN}},
	url = {http://arxiv.org/abs/2010.05646},
	doi = {10.48550/arXiv.2010.05646},
	abstract = {Several recent work on speech synthesis have employed generative adversarial networks (GANs) to produce raw waveforms. Although such methods improve the sampling efficiency and memory usage, their sample quality has not yet reached that of autoregressive and flow-based generative models. In this work, we propose HiFi-GAN, which achieves both efficient and high-fidelity speech synthesis. As speech audio consists of sinusoidal signals with various periods, we demonstrate that modeling periodic patterns of an audio is crucial for enhancing sample quality. A subjective human evaluation (mean opinion score, MOS) of a single speaker dataset indicates that our proposed method demonstrates similarity to human quality while generating 22.05 kHz high-fidelity audio 167.9 times faster than real-time on a single V100 GPU. We further show the generality of HiFi-GAN to the mel-spectrogram inversion of unseen speakers and end-to-end speech synthesis. Finally, a small footprint version of HiFi-GAN generates samples 13.4 times faster than real-time on CPU with comparable quality to an autoregressive counterpart.},
	urldate = {2024-04-09},
	publisher = {arXiv},
	author = {Kong, Jungil and Kim, Jaehyeon and Bae, Jaekyoung},
	month = oct,
	year = {2020},
	note = {arXiv:2010.05646 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{di_giorgi_mel_2022,
	title = {Mel {Spectrogram} {Inversion} with {Stable} {Pitch}},
	url = {http://arxiv.org/abs/2208.12782},
	doi = {10.48550/arXiv.2208.12782},
	abstract = {Vocoders are models capable of transforming a low-dimensional spectral representation of an audio signal, typically the mel spectrogram, to a waveform. Modern speech generation pipelines use a vocoder as their final component. Recent vocoder models developed for speech achieve a high degree of realism, such that it is natural to wonder how they would perform on music signals. Compared to speech, the heterogeneity and structure of the musical sound texture offers new challenges. In this work we focus on one specific artifact that some vocoder models designed for speech tend to exhibit when applied to music: the perceived instability of pitch when synthesizing sustained notes. We argue that the characteristic sound of this artifact is due to the lack of horizontal phase coherence, which is often the result of using a time-domain target space with a model that is invariant to time-shifts, such as a convolutional neural network. We propose a new vocoder model that is specifically designed for music. Key to improving the pitch stability is the choice of a shift-invariant target space that consists of the magnitude spectrum and the phase gradient. We discuss the reasons that inspired us to re-formulate the vocoder task, outline a working example, and evaluate it on musical signals. Our method results in 60\% and 10\% improved reconstruction of sustained notes and chords with respect to existing models, using a novel harmonic error metric.},
	urldate = {2024-04-09},
	publisher = {arXiv},
	author = {Di Giorgi, Bruno and Levy, Mark and Sharp, Richard},
	month = aug,
	year = {2022},
	note = {arXiv:2208.12782 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{kong_diffwave_2021,
	title = {{DiffWave}: {A} {Versatile} {Diffusion} {Model} for {Audio} {Synthesis}},
	shorttitle = {{DiffWave}},
	url = {http://arxiv.org/abs/2009.09761},
	doi = {10.48550/arXiv.2009.09761},
	abstract = {In this work, we propose DiffWave, a versatile diffusion probabilistic model for conditional and unconditional waveform generation. The model is non-autoregressive, and converts the white noise signal into structured waveform through a Markov chain with a constant number of steps at synthesis. It is efficiently trained by optimizing a variant of variational bound on the data likelihood. DiffWave produces high-fidelity audios in different waveform generation tasks, including neural vocoding conditioned on mel spectrogram, class-conditional generation, and unconditional generation. We demonstrate that DiffWave matches a strong WaveNet vocoder in terms of speech quality (MOS: 4.44 versus 4.43), while synthesizing orders of magnitude faster. In particular, it significantly outperforms autoregressive and GAN-based waveform models in the challenging unconditional generation task in terms of audio quality and sample diversity from various automatic and human evaluations.},
	urldate = {2024-04-09},
	publisher = {arXiv},
	author = {Kong, Zhifeng and Ping, Wei and Huang, Jiaji and Zhao, Kexin and Catanzaro, Bryan},
	month = mar,
	year = {2021},
	note = {arXiv:2009.09761 [cs, eess, stat]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning},
}

@misc{morrison_chunked_2022,
	title = {Chunked {Autoregressive} {GAN} for {Conditional} {Waveform} {Synthesis}},
	url = {http://arxiv.org/abs/2110.10139},
	doi = {10.48550/arXiv.2110.10139},
	abstract = {Conditional waveform synthesis models learn a distribution of audio waveforms given conditioning such as text, mel-spectrograms, or MIDI. These systems employ deep generative models that model the waveform via either sequential (autoregressive) or parallel (non-autoregressive) sampling. Generative adversarial networks (GANs) have become a common choice for non-autoregressive waveform synthesis. However, state-of-the-art GAN-based models produce artifacts when performing mel-spectrogram inversion. In this paper, we demonstrate that these artifacts correspond with an inability for the generator to learn accurate pitch and periodicity. We show that simple pitch and periodicity conditioning is insufficient for reducing this error relative to using autoregression. We discuss the inductive bias that autoregression provides for learning the relationship between instantaneous frequency and phase, and show that this inductive bias holds even when autoregressively sampling large chunks of the waveform during each forward pass. Relative to prior state-of-the-art GAN-based models, our proposed model, Chunked Autoregressive GAN (CARGAN) reduces pitch error by 40-60\%, reduces training time by 58\%, maintains a fast generation speed suitable for real-time or interactive applications, and maintains or improves subjective quality.},
	urldate = {2024-04-09},
	publisher = {arXiv},
	author = {Morrison, Max and Kumar, Rithesh and Kumar, Kundan and Seetharaman, Prem and Courville, Aaron and Bengio, Yoshua},
	month = mar,
	year = {2022},
	note = {arXiv:2110.10139 [cs, eess]},
	keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{kumar_melgan_2019,
	title = {{MelGAN}: {Generative} {Adversarial} {Networks} for {Conditional} {Waveform} {Synthesis}},
	shorttitle = {{MelGAN}},
	url = {http://arxiv.org/abs/1910.06711},
	doi = {10.48550/arXiv.1910.06711},
	abstract = {Previous works (Donahue et al., 2018a; Engel et al., 2019a) have found that generating coherent raw audio waveforms with GANs is challenging. In this paper, we show that it is possible to train GANs reliably to generate high quality coherent waveforms by introducing a set of architectural changes and simple training techniques. Subjective evaluation metric (Mean Opinion Score, or MOS) shows the effectiveness of the proposed approach for high quality mel-spectrogram inversion. To establish the generality of the proposed techniques, we show qualitative results of our model in speech synthesis, music domain translation and unconditional music synthesis. We evaluate the various components of the model through ablation studies and suggest a set of guidelines to design general purpose discriminators and generators for conditional sequence synthesis tasks. Our model is non-autoregressive, fully convolutional, with significantly fewer parameters than competing models and generalizes to unseen speakers for mel-spectrogram inversion. Our pytorch implementation runs at more than 100x faster than realtime on GTX 1080Ti GPU and more than 2x faster than real-time on CPU, without any hardware specific optimization tricks.},
	urldate = {2024-04-09},
	publisher = {arXiv},
	author = {Kumar, Kundan and Kumar, Rithesh and de Boissiere, Thibault and Gestin, Lucas and Teoh, Wei Zhen and Sotelo, Jose and de Brebisson, Alexandre and Bengio, Yoshua and Courville, Aaron},
	month = dec,
	year = {2019},
	note = {arXiv:1910.06711 [cs, eess]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{di_giorgi_mel_2022-1,
	title = {Mel {Spectrogram} {Inversion} with {Stable} {Pitch}},
	url = {http://arxiv.org/abs/2208.12782},
	doi = {10.48550/arXiv.2208.12782},
	abstract = {Vocoders are models capable of transforming a low-dimensional spectral representation of an audio signal, typically the mel spectrogram, to a waveform. Modern speech generation pipelines use a vocoder as their final component. Recent vocoder models developed for speech achieve a high degree of realism, such that it is natural to wonder how they would perform on music signals. Compared to speech, the heterogeneity and structure of the musical sound texture offers new challenges. In this work we focus on one specific artifact that some vocoder models designed for speech tend to exhibit when applied to music: the perceived instability of pitch when synthesizing sustained notes. We argue that the characteristic sound of this artifact is due to the lack of horizontal phase coherence, which is often the result of using a time-domain target space with a model that is invariant to time-shifts, such as a convolutional neural network. We propose a new vocoder model that is specifically designed for music. Key to improving the pitch stability is the choice of a shift-invariant target space that consists of the magnitude spectrum and the phase gradient. We discuss the reasons that inspired us to re-formulate the vocoder task, outline a working example, and evaluate it on musical signals. Our method results in 60\% and 10\% improved reconstruction of sustained notes and chords with respect to existing models, using a novel harmonic error metric.},
	urldate = {2024-04-09},
	publisher = {arXiv},
	author = {Di Giorgi, Bruno and Levy, Mark and Sharp, Richard},
	month = aug,
	year = {2022},
	note = {arXiv:2208.12782 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@article{griffin_signal_1984,
	title = {Signal estimation from modified short-time {Fourier} transform},
	volume = {32},
	copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
	issn = {0096-3518},
	url = {http://ieeexplore.ieee.org/document/1164317/},
	doi = {10.1109/TASSP.1984.1164317},
	abstract = {In this paper, we present an algorithm to estimate a signal from its modified short-timeFourier transform (STFT). This algorithm is computationally simple and is obtained by minimizing the mean squared error between the STFT of the estimated signal and the modified STFT. Using this algorithm, we also develop an iterative algorithm to estimate a signal from itsmodifiedSTFT magnitude. The iterative algorithm is shown to decrease, in each iteration, the mean squared error betweentheSTFT magnitude of the estimated signal and the modified STFT magnitude. Themajor computation involved in the iterative algorithm is the discrete Fourier transform (DFT)computation, and the algorithm appears to be real-time implementable with currenthardware technology. The algorithm developed in this paper has been applied to the time-scale modification of speech. The resulting system generates very high-quality speech, and appears to be better in performancc than any existing method.},
	language = {en},
	number = {2},
	urldate = {2024-04-09},
	journal = {IEEE Transactions on Acoustics, Speech, and Signal Processing},
	author = {Griffin, D. and {Jae Lim}},
	month = apr,
	year = {1984},
	pages = {236--243},
}

@misc{noauthor_crowdmos_nodate,
	title = {{CROWDMOS}: {An} approach for crowdsourcing mean opinion score studies {\textbar} {IEEE} {Conference} {Publication} {\textbar} {IEEE} {Xplore}},
	url = {https://ieeexplore.ieee.org/document/5946971},
	urldate = {2024-04-09},
}

@misc{ronneberger_u-net_2015,
	title = {U-{Net}: {Convolutional} {Networks} for {Biomedical} {Image} {Segmentation}},
	shorttitle = {U-{Net}},
	url = {http://arxiv.org/abs/1505.04597},
	doi = {10.48550/arXiv.1505.04597},
	abstract = {There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .},
	urldate = {2024-04-08},
	publisher = {arXiv},
	author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
	month = may,
	year = {2015},
	note = {arXiv:1505.04597 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{oktay_attention_2018,
	title = {Attention {U}-{Net}: {Learning} {Where} to {Look} for the {Pancreas}},
	shorttitle = {Attention {U}-{Net}},
	url = {http://arxiv.org/abs/1804.03999},
	doi = {10.48550/arXiv.1804.03999},
	abstract = {We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs). AGs can be easily integrated into standard CNN architectures such as the U-Net model with minimal computational overhead while increasing the model sensitivity and prediction accuracy. The proposed Attention U-Net architecture is evaluated on two large CT abdominal datasets for multi-class image segmentation. Experimental results show that AGs consistently improve the prediction performance of U-Net across different datasets and training sizes while preserving computational efficiency. The code for the proposed architecture is publicly available.},
	urldate = {2024-04-08},
	publisher = {arXiv},
	author = {Oktay, Ozan and Schlemper, Jo and Folgoc, Loic Le and Lee, Matthew and Heinrich, Mattias and Misawa, Kazunari and Mori, Kensaku and McDonagh, Steven and Hammerla, Nils Y. and Kainz, Bernhard and Glocker, Ben and Rueckert, Daniel},
	month = may,
	year = {2018},
	note = {arXiv:1804.03999 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@article{de_benito_exploring_2019,
	title = {Exploring convolutional, recurrent, and hybrid deep neural networks for speech and music detection in a large audio dataset},
	volume = {2019},
	doi = {10.1186/s13636-019-0152-1},
	abstract = {Abstract Audio signals represent a wide diversity of acoustic events, from background environmental noise to spoken communication. Machine learning models such as neural networks have already been proposed for audio signal modeling, where recurrent structures can take advantage of temporal dependencies. This work aims to study the implementation of several neural network-based systems for speech and music event detection over a collection of 77,937 10-second audio segments (216 h), selected from the Google AudioSet dataset. These segments belong to YouTube videos and have been represented as mel-spectrograms. We propose and compare two approaches. The first one is the training of two different neural networks, one for speech detection and another for music detection. The second approach consists on training a single neural network to tackle both tasks at the same time. The studied architectures include fully connected, convolutional and LSTM (long short-term memory) recurrent networks. Comparative results are provided in terms of classification performance and model complexity. We would like to highlight the performance of convolutional architectures, specially in combination with an LSTM stage. The hybrid convolutional-LSTM models achieve the best overall results (85\% accuracy) in the three proposed tasks. Furthermore, a distractor analysis of the results has been carried out in order to identify which events in the ontology are the most harmful for the performance of the models, showing some difficult scenarios for the detection of music and speech.},
	journal = {EURASIP Journal on Audio, Speech, and Music Processing},
	author = {de Benito, Diego and Lozano-Diez, Alicia and Toledano, Doroteo and Gonzalez-Rodriguez, Joaquin},
	month = jun,
	year = {2019},
}

@misc{noauthor_fig_nodate,
	title = {Fig. 1 {Waveform}, spectrogram, and mel-spectrogram of a 10-s speech...},
	url = {https://www.researchgate.net/figure/Waveform-spectrogram-and-mel-spectrogram-of-a-10-s-speech-segment-obtained-from-Google_fig1_333834541},
	abstract = {Download scientific diagram {\textbar} Waveform, spectrogram, and mel-spectrogram of a 10-s speech segment obtained from Google AudioSet. The mel-spectrogram, based on the auditory-based mel-frequency scale, provides better resolution for lower frequencies than the spectrogram from publication: Exploring convolutional, recurrent, and hybrid deep neural networks for speech and music detection in a large audio dataset {\textbar} Abstract Audio signals represent a wide diversity of acoustic events, from background environmental noise to spoken communication. Machine learning models such as neural networks have already been proposed for audio signal modeling, where recurrent structures can take... {\textbar} Music, Speech and Audio {\textbar} ResearchGate, the professional network for scientists.},
	language = {en},
	urldate = {2024-04-07},
	journal = {ResearchGate},
}

@article{shannont_communication_1949,
	title = {Communication in the {Presence} of {Noise}},
	abstract = {A method is developed for representing any communication system geometrically. Messages and the corresponding signals are points in two "function spaces," and the modulation process is a mapping of one space into the other. Using this representation, a number of results in communication theory are deduced concerning expansion and compression of bandwidth and the threshold effect. Formulas are found for the maxmum rate of transmission of binary digits over a system when the signal is perturbed by various types of noise. Some of the properties of "ideal" systems which transmit at this maxmum rate are discussed. The equivalent number of binary digits per second for certain information sources is calculated.},
	language = {en},
	journal = {PROCEEDINGS OF THE I.R.E.},
	author = {SHANNONt, CLAUDE E},
	year = {1949},
}

@misc{natsiou_audio_2022,
	title = {Audio representations for deep learning in sound synthesis: {A} review},
	shorttitle = {Audio representations for deep learning in sound synthesis},
	url = {http://arxiv.org/abs/2201.02490},
	doi = {10.48550/arXiv.2201.02490},
	abstract = {The rise of deep learning algorithms has led many researchers to withdraw from using classic signal processing methods for sound generation. Deep learning models have achieved expressive voice synthesis, realistic sound textures, and musical notes from virtual instruments. However, the most suitable deep learning architecture is still under investigation. The choice of architecture is tightly coupled to the audio representations. A sound's original waveform can be too dense and rich for deep learning models to deal with efficiently - and complexity increases training time and computational cost. Also, it does not represent sound in the manner in which it is perceived. Therefore, in many cases, the raw audio has been transformed into a compressed and more meaningful form using upsampling, feature-extraction, or even by adopting a higher level illustration of the waveform. Furthermore, conditional on the form chosen, additional conditioning representations, different model architectures, and numerous metrics for evaluating the reconstructed sound have been investigated. This paper provides an overview of audio representations applied to sound synthesis using deep learning. Additionally, it presents the most significant methods for developing and evaluating a sound synthesis architecture using deep learning models, always depending on the audio representation.},
	urldate = {2024-04-07},
	publisher = {arXiv},
	author = {Natsiou, Anastasia and O'Leary, Sean},
	month = jan,
	year = {2022},
	note = {arXiv:2201.02490 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@article{sun_diffusion-lm_nodate,
	title = {Diffusion-{LM} on {Symbolic} {Music} {Generation} with {Controllability}},
	language = {en},
	author = {Sun, Hao and Ouyang, Liwen},
}

@article{mittal_symbolic_2021,
	title = {{SYMBOLIC} {MUSIC} {GENERATION} {WITH} {DIFFUSION} {MODELS}},
	abstract = {Score-based generative models and diffusion probabilistic models have been successful at generating high-quality samples in a variety of continuous domains. However, due to their Langevin-inspired sampling mechanisms, their application to discrete symbolic music data has been limited. In this work, we present a technique for training diffusion models on symbolic music data by parameterizing the discrete domain in the continuous latent space of a pre-trained variational autoencoder. Our method is nonautoregressive and learns to generate sequences of latent embeddings through the reverse process and offers parallel generation with a constant number of iterative reﬁnement steps. We show strong unconditional generation and posthoc conditional inﬁlling results compared to autoregressive language models operating over the same continuous embeddings.},
	language = {en},
	author = {Mittal, Gautam and Engel, Jesse and Hawthorne, Curtis and Simon, Ian},
	year = {2021},
}

@misc{chien_adversarial_2019,
	title = {Adversarial {Analysis} of {Natural} {Language} {Inference} {Systems}},
	url = {http://arxiv.org/abs/1912.03441},
	abstract = {The release of large natural language inference (NLI) datasets like SNLI and MNLI have led to rapid development and improvement of completely neural systems for the task. Most recently, heavily pre-trained, Transformer-based models like BERT and MT-DNN have reached near-human performance on these datasets. However, these standard datasets have been shown to contain many annotation artifacts, allowing models to shortcut understanding using simple fallible heuristics, and still perform well on the test set. So it is no surprise that many adversarial (challenge) datasets have been created that cause models trained on standard datasets to fail dramatically. Although extra training on this data generally improves model performance on just that type of data, transferring that learning to unseen examples is still partial at best. This work evaluates the failures of state-of-the-art models on existing adversarial datasets that test different linguistic phenomena, and find that even though the models perform similarly on MNLI, they differ greatly in their robustness to these attacks. In particular, we find syntax-related attacks to be particularly effective across all models, so we provide a fine-grained analysis and comparison of model performance on those examples. We draw conclusions about the value of model size and multi-task learning (beyond comparing their standard test set performance), and provide suggestions for more effective training data.},
	urldate = {2024-03-31},
	publisher = {arXiv},
	author = {Chien, Tiffany and Kalita, Jugal},
	month = dec,
	year = {2019},
	note = {arXiv:1912.03441 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{jia_adversarial_2017,
	title = {Adversarial {Examples} for {Evaluating} {Reading} {Comprehension} {Systems}},
	url = {http://arxiv.org/abs/1707.07328},
	doi = {10.48550/arXiv.1707.07328},
	abstract = {Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. In this adversarial setting, the accuracy of sixteen published models drops from an average of \$75{\textbackslash}\%\$ F1 score to \$36{\textbackslash}\%\$; when the adversary is allowed to add ungrammatical sequences of words, average accuracy on four models decreases further to \$7{\textbackslash}\%\$. We hope our insights will motivate the development of new models that understand language more precisely.},
	urldate = {2024-03-31},
	publisher = {arXiv},
	author = {Jia, Robin and Liang, Percy},
	month = jul,
	year = {2017},
	note = {arXiv:1707.07328 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{yang_diffusion_2024,
	title = {Diffusion {Models}: {A} {Comprehensive} {Survey} of {Methods} and {Applications}},
	shorttitle = {Diffusion {Models}},
	url = {http://arxiv.org/abs/2209.00796},
	doi = {10.48550/arXiv.2209.00796},
	abstract = {Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation, and molecule design. In this survey, we provide an overview of the rapidly expanding body of work on diffusion models, categorizing the research into three key areas: efficient sampling, improved likelihood estimation, and handling data with special structures. We also discuss the potential for combining diffusion models with other generative models for enhanced results. We further review the wide-ranging applications of diffusion models in fields spanning from computer vision, natural language generation, temporal data modeling, to interdisciplinary applications in other scientific disciplines. This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration. Github: https://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy.},
	urldate = {2024-03-26},
	publisher = {arXiv},
	author = {Yang, Ling and Zhang, Zhilong and Song, Yang and Hong, Shenda and Xu, Runsheng and Zhao, Yue and Zhang, Wentao and Cui, Bin and Yang, Ming-Hsuan},
	month = feb,
	year = {2024},
	note = {arXiv:2209.00796 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@article{hromadka_improving_nodate,
	title = {Improving {Progressive} {Distillation} in {Diffusion} {Models}},
	copyright = {All rights reserved},
	abstract = {Diffusion models have emerged as a class of generative models capable of producing high-quality samples, outperforming GANs and VAEs on numerous benchmarks. However, their primary drawback is their sampling speed, often requiring hundreds of thousands of sampling steps. One such proposal to improve sampling speed is Progressive Distillation. In this project we explore three aspects of this method (skip-factor, training iterations, scheduling), aiming to discover to what extend we can speed up the training and sampling process. Through experiments on MNIST and Cifar10 datasets, we demonstrate results that not only hold their ground against established baselines but also significantly reduce both training and inference times as compared to conventional Progressive Distillation approaches. We analyze trade-offs and provide discussion on future directions to make progressive distillation more viable in real-world use-cases. I release my code for future work 1.},
	language = {en},
	author = {Hromádka, Timo},
}

@misc{salimans_improved_2016,
	title = {Improved {Techniques} for {Training} {GANs}},
	url = {http://arxiv.org/abs/1606.03498},
	abstract = {We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3\%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes.},
	urldate = {2024-03-26},
	publisher = {arXiv},
	author = {Salimans, Tim and Goodfellow, Ian and Zaremba, Wojciech and Cheung, Vicki and Radford, Alec and Chen, Xi},
	month = jun,
	year = {2016},
	note = {arXiv:1606.03498 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing},
}

@article{yang_diffusion_2024-1,
	title = {Diffusion {Models}: {A} {Comprehensive} {Survey} of {Methods} and {Applications}},
	volume = {56},
	issn = {0360-0300, 1557-7341},
	shorttitle = {Diffusion {Models}},
	url = {https://dl.acm.org/doi/10.1145/3626235},
	doi = {10.1145/3626235},
	abstract = {Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation, and molecule design. In this survey, we provide an overview of the rapidly expanding body of work on diffusion models, categorizing the research into three key areas: efficient sampling, improved likelihood estimation, and handling data with special structures. We also discuss the potential for combining diffusion models with other generative models for enhanced results. We further review the wide-ranging applications of diffusion models in fields spanning from computer vision, natural language processing, temporal data modeling, to interdisciplinary applications in other scientific disciplines. This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration. Github:
              https://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy},
	language = {en},
	number = {4},
	urldate = {2024-03-24},
	journal = {ACM Computing Surveys},
	author = {Yang, Ling and Zhang, Zhilong and Song, Yang and Hong, Shenda and Xu, Runsheng and Zhao, Yue and Zhang, Wentao and Cui, Bin and Yang, Ming-Hsuan},
	month = apr,
	year = {2024},
	pages = {1--39},
}

@inproceedings{feffer_preference_2023,
	address = {New York, NY, USA},
	series = {{AIES} '23},
	title = {From {Preference} {Elicitation} to {Participatory} {ML}: {A} {Critical} {Survey} \& {Guidelines} for {Future} {Research}},
	isbn = {9798400702310},
	shorttitle = {From {Preference} {Elicitation} to {Participatory} {ML}},
	url = {https://dl.acm.org/doi/10.1145/3600211.3604661},
	doi = {10.1145/3600211.3604661},
	abstract = {The AI Ethics community faces an imperative to empower stakeholders and impacted community members so that they can scrutinize and influence the design, development, and use of AI systems in high-stakes domains. While a growing chorus of recent papers has kindled interest in so-called “participatory ML” methods, precisely what form participation ought to take and how to operationalize these ambitions are seldom addressed. Our survey of the relevant literature shows that in many papers, participation is reduced to highly structured, computational mechanisms designed to elicit mathematically tractable approximations of narrowly-defined moral values. Of papers that actually engage with real people, these engagements typically consist of one-time interactions with individuals that are often unrepresentative of the relevant stakeholders. Motivated by these clear limitations, we introduce a consolidated set of axes to evaluate and improve participatory approaches. We use these axes to analyze contemporary work in this space and outline future AI research directions that could meaningfully contribute to operationalizing the ideal of participation.},
	urldate = {2024-03-24},
	booktitle = {Proceedings of the 2023 {AAAI}/{ACM} {Conference} on {AI}, {Ethics}, and {Society}},
	publisher = {Association for Computing Machinery},
	author = {Feffer, Michael and Skirpan, Michael and Lipton, Zachary and Heidari, Hoda},
	month = aug,
	year = {2023},
	keywords = {Participation, elicitation, value-alignment},
	pages = {38--48},
}

@article{blackwell_ethnographic_2021,
	title = {Ethnographic artificial intelligence},
	volume = {46},
	issn = {0308-0188},
	url = {https://doi.org/10.1080/03080188.2020.1840226},
	doi = {10.1080/03080188.2020.1840226},
	abstract = {Artificial intelligence research is seldom conducted ethnographically. Nevertheless, the field itself has a distinctive culture – an engineering imaginary within which ‘general intelligence’ is considered to be a phenomenon that can be both defined and tested by reference to some universal (probably mathematical) standard. This paper describes a research agenda that sets out to question those assumptions, through a programme of ethnographic field work, collaborating with computer scientists and educators in several countries of sub-Saharan Africa.},
	number = {1-2},
	urldate = {2024-03-23},
	journal = {Interdisciplinary Science Reviews},
	author = {Blackwell, Alan F.},
	month = apr,
	year = {2021},
	note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/03080188.2020.1840226},
	keywords = {Artificial intelligence, broad learning, cognitive variations, critical technical practice, human-computer interaction},
	pages = {198--211},
}

@article{gregory_scandinavian_nodate,
	title = {Scandinavian {Approaches} to {Participatory} {Design}},
	language = {en},
	author = {Gregory, Judith},
}

@book{costanza-chock_design_2020,
	title = {Design {Justice}: {Community}-{Led} {Practices} to {Build} the {Worlds} {We} {Need}},
	isbn = {978-0-262-35686-2},
	shorttitle = {Design {Justice}},
	url = {https://direct.mit.edu/books/book/4605/Design-JusticeCommunity-Led-Practices-to-Build-the},
	abstract = {An exploration of how design might be led by marginalized communities, dismantle structural inequality, and advance collective liberation and ecological survival.
            What is the relationship between design, power, and social justice? “Design justice” is an approach to design that is led by marginalized communities and that aims expilcitly to challenge, rather than reproduce, structural inequalities. It has emerged from a growing community of designers in various fields who work closely with social movements and community-based organizations around the world.
            This book explores the theory and practice of design justice, demonstrates how universalist design principles and practices erase certain groups of people—specifically, those who are intersectionally disadvantaged or multiply burdened under the matrix of domination (white supremacist heteropatriarchy, ableism, capitalism, and settler colonialism)—and invites readers to “build a better world, a world where many worlds fit; linked worlds of collective liberation and ecological sustainability.” Along the way, the book documents a multitude of real-world community-led design practices, each grounded in a particular social movement. Design Justice goes beyond recent calls for design for good, user-centered design, and employment diversity in the technology and design professions; it connects design to larger struggles for collective liberation and ecological survival.
            The open access edition of this book was made possible by generous funding from Knowledge Unlatched and the MIT Press Frank Urbanowski Memorial Fund.},
	language = {en},
	urldate = {2024-03-22},
	publisher = {The MIT Press},
	author = {Costanza-Chock, Sasha},
	month = mar,
	year = {2020},
	doi = {10.7551/mitpress/12255.001.0001},
}

@book{costanza-chock_design_2020-1,
	title = {Design {Justice}: {Community}-{Led} {Practices} to {Build} the {Worlds} {We} {Need}},
	isbn = {978-0-262-35686-2},
	shorttitle = {Design {Justice}},
	url = {https://direct.mit.edu/books/oa-monograph/4605/Design-JusticeCommunity-Led-Practices-to-Build-the},
	abstract = {An exploration of how design might be led by marginalized communities, dismantle structural inequality, and advance collective liberation and ecological surviva},
	language = {en},
	urldate = {2024-03-22},
	publisher = {The MIT Press},
	author = {Costanza-Chock, Sasha},
	month = mar,
	year = {2020},
	doi = {10.7551/mitpress/12255.001.0001},
}

@incollection{ravulo_indigenous_2023,
	address = {Singapore},
	title = {Indigenous {Peoples}, {Whiteness}, and the {Coloniality} of {Co}-design},
	isbn = {978-981-19161-2-0},
	url = {https://link.springer.com/10.1007/978-981-19-1612-0_28-1},
	language = {en},
	urldate = {2024-03-22},
	booktitle = {Handbook of {Critical} {Whiteness}},
	publisher = {Springer Nature Singapore},
	author = {King, Paula Toko and Cormack, Donna},
	editor = {Ravulo, Jioji and Olcoń, Katarzyna and Dune, Tinashe and Workman, Alex and Liamputtong, Pranee},
	year = {2023},
	doi = {10.1007/978-981-19-1612-0_28-1},
	pages = {1--16},
}

@inproceedings{robertson_expressiveness_2023,
	address = {Hamburg Germany},
	title = {Expressiveness, {Cost}, and {Collectivism}: {How} the {Design} of {Preference} {Languages} {Shapes} {Participation} in {Algorithmic} {Decision}-{Making}},
	isbn = {978-1-4503-9421-5},
	shorttitle = {Expressiveness, {Cost}, and {Collectivism}},
	url = {https://dl.acm.org/doi/10.1145/3544548.3580996},
	doi = {10.1145/3544548.3580996},
	language = {en},
	urldate = {2024-03-22},
	booktitle = {Proceedings of the 2023 {CHI} {Conference} on {Human} {Factors} in {Computing} {Systems}},
	publisher = {ACM},
	author = {Robertson, Samantha and Nguyen, Tonya and Hu, Cathy and Albiston, Catherine and Nikzad, Afshin and Salehi, Niloufar},
	month = apr,
	year = {2023},
	pages = {1--16},
}

@article{robertson_expressiveness_2023-1,
	title = {Expressiveness, {Cost}, and {Collectivism}: {How} the {Design} of {Preference} {Languages} {Shapes} {Participation} in {Algorithmic} {Decision}-{Making}},
	shorttitle = {Expressiveness, {Cost}, and {Collectivism}},
	url = {https://dl.acm.org/doi/10.1145/3544548.3580996},
	doi = {10.1145/3544548.3580996},
	abstract = {Emerging methods for participatory algorithm design have proposed collecting and aggregating individual stakeholders’ preferences to create algorithmic systems that account for those stakeholders’ values. Drawing on two years of research across two public school districts in the United States, we study how families and school districts use students’ preferences for schools to meet their goals in the context of algorithmic student assignment systems. We find that the design of the preference language, i.e. the structure in which participants must express their needs and goals to the decision-maker, shapes the opportunities for meaningful participation. We define three properties of preference languages – expressiveness, cost, and collectivism – and discuss how these factors shape who is able to participate, and the extent to which they are able to effectively communicate their needs to the decision-maker. Reflecting on these findings, we offer implications and paths forward for researchers and practitioners who are considering applying a preference-based model for participation in algorithmic decision making.},
	language = {en},
	urldate = {2024-03-22},
	journal = {Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems},
	author = {Robertson, Samantha and Nguyen, Tonya and Hu, Cathy and Albiston, Catherine and Nikzad, Afshin and Salehi, Niloufar},
	month = apr,
	year = {2023},
	note = {Conference Name: CHI '23: CHI Conference on Human Factors in Computing Systems
ISBN: 9781450394215
Place: Hamburg Germany
Publisher: ACM},
	pages = {1--16},
}

@article{yildirim_how_2022,
	title = {How {Experienced} {Designers} of {Enterprise} {Applications} {Engage} {AI} as a {Design} {Material}},
	url = {https://dl.acm.org/doi/10.1145/3491102.3517491},
	doi = {10.1145/3491102.3517491},
	abstract = {HCI research has explored AI as a design material, suggesting that designers can envision AI’s design opportunities to improve UX. Recent research claimed that enterprise applications offer an opportunity for AI innovation at the user experience level. We conducted design workshops to explore the practices of experienced designers who work on cross-functional AI teams in the enterprise. We discussed how designers successfully work with and struggle with AI. Our findings revealed that designers can innovate at the system and service levels. We also discovered that making a case for an AI feature’s return on investment is a barrier for designers when they propose AI concepts and ideas. Our discussions produced novel insights on designers’ role on AI teams, and the boundary objects they used for collaborating with data scientists. We discuss the implications of these findings as opportunities for future research aiming to empower designers in working with data and AI.},
	language = {en},
	urldate = {2024-03-22},
	journal = {CHI Conference on Human Factors in Computing Systems},
	author = {Yildirim, Nur and Kass, Alex and Tung, Teresa and Upton, Connor and Costello, Donnacha and Giusti, Robert and Lacin, Sinem and Lovic, Sara and O'Neill, James M and Meehan, Rudi O'Reilly and Ó Loideáin, Eoin and Pini, Azzurra and Corcoran, Medb and Hayes, Jeremiah and Cahalane, Diarmuid J and Shivhare, Gaurav and Castoro, Luigi and Caruso, Giovanni and Oh, Changhoon and McCann, James and Forlizzi, Jodi and Zimmerman, John},
	month = apr,
	year = {2022},
	note = {Conference Name: CHI '22: CHI Conference on Human Factors in Computing Systems
ISBN: 9781450391573
Place: New Orleans LA USA
Publisher: ACM},
	pages = {1--13},
}

@article{delgado_stakeholder_2021,
	title = {Stakeholder {Participation} in {AI}: {Beyond} "{Add} {Diverse} {Stakeholders} and {Stir}"},
	shorttitle = {Stakeholder {Participation} in {AI}},
	url = {https://www.semanticscholar.org/paper/Stakeholder-Participation-in-AI%3A-Beyond-%22Add-and-Delgado-Yang/f553bacd2e732bd1029971705ffb595a834b4a86},
	abstract = {There is a growing consensus in HCI and AI research that the design of AI systems needs to engage and empower stakeholders who will be affected by AI. However, the manner in which stakeholders should participate in AI design is unclear. This workshop paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices via a survey of recent published research and a dozen semi-structured interviews with AI researchers and practitioners. Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design and articulates a set of empirical findings that in ensemble detail out the contemporary landscape of participatory practice in AI design. These findings can help bootstrap a more principled discussion on how PD of AI should move forward across AI, HCI, and other research communities.},
	urldate = {2024-03-22},
	journal = {ArXiv},
	author = {Delgado, F. and Yang, Stephen and Madaio, Michael A. and Yang, Qian},
	month = nov,
	year = {2021},
}

@misc{noauthor_paradox_2024,
	title = {The paradox of diffusion distillation},
	url = {https://sander.ai/2024/02/28/paradox.html},
	abstract = {Thoughts on the tension between iterative refinement as the thing that makes diffusion models work, and our continual attempts to make it \_less\_ iterative.},
	language = {en},
	urldate = {2024-03-19},
	journal = {Sander Dieleman},
	month = feb,
	year = {2024},
}

@misc{lyu_accelerating_2022,
	title = {Accelerating {Diffusion} {Models} via {Early} {Stop} of the {Diffusion} {Process}},
	url = {http://arxiv.org/abs/2205.12524},
	doi = {10.48550/arXiv.2205.12524},
	abstract = {Denoising Diffusion Probabilistic Models (DDPMs) have achieved impressive performance on various generation tasks. By modeling the reverse process of gradually diffusing the data distribution into a Gaussian distribution, generating a sample in DDPMs can be regarded as iteratively denoising a randomly sampled Gaussian noise. However, in practice DDPMs often need hundreds even thousands of denoising steps to obtain a high-quality sample from the Gaussian noise, leading to extremely low inference efficiency. In this work, we propose a principled acceleration strategy, referred to as Early-Stopped DDPM (ES-DDPM), for DDPMs. The key idea is to stop the diffusion process early where only the few initial diffusing steps are considered and the reverse denoising process starts from a non-Gaussian distribution. By further adopting a powerful pre-trained generative model, such as GAN and VAE, in ES-DDPM, sampling from the target non-Gaussian distribution can be efficiently achieved by diffusing samples obtained from the pre-trained generative model. In this way, the number of required denoising steps is significantly reduced. In the meantime, the sample quality of ES-DDPM also improves substantially, outperforming both the vanilla DDPM and the adopted pre-trained generative model. On extensive experiments across CIFAR-10, CelebA, ImageNet, LSUN-Bedroom and LSUN-Cat, ES-DDPM obtains promising acceleration effect and performance improvement over representative baseline methods. Moreover, ES-DDPM also demonstrates several attractive properties, including being orthogonal to existing acceleration methods, as well as simultaneously enabling both global semantic and local pixel-level control in image generation.},
	urldate = {2024-03-16},
	publisher = {arXiv},
	author = {Lyu, Zhaoyang and XU, Xudong and Yang, Ceyuan and Lin, Dahua and Dai, Bo},
	month = may,
	year = {2022},
	note = {arXiv:2205.12524 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{noauthor_ieee_nodate,
	title = {{IEEE} {Xplore} {Full}-{Text} {PDF}:},
	url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9747033},
	urldate = {2024-03-13},
}

@misc{noauthor_ieee_nodate-1,
	title = {{IEEE} {Xplore} {Full}-{Text} {PDF}:},
	url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9747033},
	urldate = {2024-03-13},
}

@misc{das_image_2023,
	title = {Image generation with shortest path diffusion},
	url = {http://arxiv.org/abs/2306.00501},
	abstract = {The field of image generation has made significant progress thanks to the introduction of Diffusion Models, which learn to progressively reverse a given image corruption. Recently, a few studies introduced alternative ways of corrupting images in Diffusion Models, with an emphasis on blurring. However, these studies are purely empirical and it remains unclear what is the optimal procedure for corrupting an image. In this work, we hypothesize that the optimal procedure minimizes the length of the path taken when corrupting an image towards a given final state. We propose the Fisher metric for the path length, measured in the space of probability distributions. We compute the shortest path according to this metric, and we show that it corresponds to a combination of image sharpening, rather than blurring, and noise deblurring. While the corruption was chosen arbitrarily in previous work, our Shortest Path Diffusion (SPD) determines uniquely the entire spatiotemporal structure of the corruption. We show that SPD improves on strong baselines without any hyperparameter tuning, and outperforms all previous Diffusion Models based on image blurring. Furthermore, any small deviation from the shortest path leads to worse performance, suggesting that SPD provides the optimal procedure to corrupt images. Our work sheds new light on observations made in recent works and provides a new approach to improve diffusion models on images and other types of data.},
	urldate = {2024-03-11},
	publisher = {arXiv},
	author = {Das, Ayan and Fotiadis, Stathi and Batra, Anil and Nabiei, Farhang and Liao, FengTing and Vakili, Sattar and Shiu, Da-shan and Bernacchia, Alberto},
	month = jun,
	year = {2023},
	note = {arXiv:2306.00501 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@article{blackwell_chapter_2022,
	title = {Chapter 13: {Making} code less {WEIRD}},
	shorttitle = {Chapter 13},
	url = {https://moralcodes.pubpub.org/pub/chapter-11/release/3},
	language = {en},
	urldate = {2024-03-06},
	journal = {Moral Codes},
	author = {Blackwell, Alan},
	month = sep,
	year = {2022},
	note = {Publisher: MIT Press},
}

@article{blackwell_inventing_2021,
	title = {Inventing {Artificial} {Intelligence} in {Ethiopia}},
	volume = {46},
	issn = {0308-0188},
	url = {https://doi.org/10.1080/03080188.2020.1830234},
	doi = {10.1080/03080188.2020.1830234},
	abstract = {Artificial Intelligence (AI) research has always been embedded in complex networks of cultural imagination, corporate business, and sociopolitical power relations. The great majority of AI research around the world, and almost all commentary on that research, assumes that the imagination, business, and political systems of Western culture and the Global North are sufficient to understand how this technology should develop in future. This article investigates the context within which AI research is imagined and conducted in the Amhara region of Ethiopia, with implications for public policy, technology strategy, future research in development contexts, and the principles that might be applied as practical engineering priorities.},
	number = {3},
	urldate = {2024-03-05},
	journal = {Interdisciplinary Science Reviews},
	author = {Blackwell, Alan F. and Damena, Addisu and Tegegne, Tesfa},
	month = jul,
	year = {2021},
	note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/03080188.2020.1830234},
	keywords = {Amhara, Artificial intelligence, Ethiopia, broad learning, design, international development, public policy, science fiction},
	pages = {363--385},
}

@misc{butlin_consciousness_2023,
	title = {Consciousness in {Artificial} {Intelligence}: {Insights} from the {Science} of {Consciousness}},
	shorttitle = {Consciousness in {Artificial} {Intelligence}},
	url = {http://arxiv.org/abs/2308.08708},
	doi = {10.48550/arXiv.2308.08708},
	abstract = {Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argues for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive "indicator properties" of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.},
	urldate = {2024-03-04},
	publisher = {arXiv},
	author = {Butlin, Patrick and Long, Robert and Elmoznino, Eric and Bengio, Yoshua and Birch, Jonathan and Constant, Axel and Deane, George and Fleming, Stephen M. and Frith, Chris and Ji, Xu and Kanai, Ryota and Klein, Colin and Lindsay, Grace and Michel, Matthias and Mudrik, Liad and Peters, Megan A. K. and Schwitzgebel, Eric and Simon, Jonathan and VanRullen, Rufin},
	month = aug,
	year = {2023},
	note = {arXiv:2308.08708 [cs, q-bio]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition},
}

@misc{ruoss_grandmaster-level_2024,
	title = {Grandmaster-{Level} {Chess} {Without} {Search}},
	url = {http://arxiv.org/abs/2402.04494},
	doi = {10.48550/arXiv.2402.04494},
	abstract = {The recent breakthrough successes in machine learning are mainly attributed to scale: namely large-scale attention-based architectures and datasets of unprecedented scale. This paper investigates the impact of training at scale for chess. Unlike traditional chess engines that rely on complex heuristics, explicit search, or a combination of both, we train a 270M parameter transformer model with supervised learning on a dataset of 10 million chess games. We annotate each board in the dataset with action-values provided by the powerful Stockfish 16 engine, leading to roughly 15 billion data points. Our largest model reaches a Lichess blitz Elo of 2895 against humans, and successfully solves a series of challenging chess puzzles, without any domain-specific tweaks or explicit search algorithms. We also show that our model outperforms AlphaZero's policy and value networks (without MCTS) and GPT-3.5-turbo-instruct. A systematic investigation of model and dataset size shows that strong chess performance only arises at sufficient scale. To validate our results, we perform an extensive series of ablations of design choices and hyperparameters.},
	urldate = {2024-03-04},
	publisher = {arXiv},
	author = {Ruoss, Anian and Delétang, Grégoire and Medapati, Sourabh and Grau-Moya, Jordi and Wenliang, Li Kevin and Catt, Elliot and Reid, John and Genewein, Tim},
	month = feb,
	year = {2024},
	note = {arXiv:2402.04494 [cs, stat]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{kumar_high-fidelity_2023,
	title = {High-{Fidelity} {Audio} {Compression} with {Improved} {RVQGAN}},
	url = {https://arxiv.org/abs/2306.06546v2},
	abstract = {Language models have been successfully used to model natural signals, such as images, speech, and music. A key component of these models is a high quality neural compression model that can compress high-dimensional natural signals into lower dimensional discrete tokens. To that end, we introduce a high-fidelity universal neural audio compression algorithm that achieves {\textasciitilde}90x compression of 44.1 KHz audio into tokens at just 8kbps bandwidth. We achieve this by combining advances in high-fidelity audio generation with better vector quantization techniques from the image domain, along with improved adversarial and reconstruction losses. We compress all domains (speech, environment, music, etc.) with a single universal model, making it widely applicable to generative modeling of all audio. We compare with competing audio compression algorithms, and find our method outperforms them significantly. We provide thorough ablations for every design choice, as well as open-source code and trained model weights. We hope our work can lay the foundation for the next generation of high-fidelity audio modeling.},
	language = {en},
	urldate = {2024-03-03},
	journal = {arXiv.org},
	author = {Kumar, Rithesh and Seetharaman, Prem and Luebs, Alejandro and Kumar, Ishaan and Kumar, Kundan},
	month = jun,
	year = {2023},
}

@misc{gui_adapting_2023,
	title = {Adapting {Frechet} {Audio} {Distance} for {Generative} {Music} {Evaluation}},
	url = {http://arxiv.org/abs/2311.01616},
	doi = {10.48550/arXiv.2311.01616},
	abstract = {The growing popularity of generative music models underlines the need for perceptually relevant, objective music quality metrics. The Frechet Audio Distance (FAD) is commonly used for this purpose even though its correlation with perceptual quality is understudied. We show that FAD performance may be hampered by sample size bias, poor choice of audio embeddings, or the use of biased or low-quality reference sets. We propose reducing sample size bias by extrapolating scores towards an infinite sample size. Through comparisons with MusicCaps labels and a listening test we identify audio embeddings and music reference sets that yield FAD scores well-correlated with acoustic and musical quality. Our results suggest that per-song FAD can be useful to identify outlier samples and predict perceptual quality for a range of music sets and generative models. Finally, we release a toolkit that allows adapting FAD for generative music evaluation.},
	urldate = {2024-03-03},
	publisher = {arXiv},
	author = {Gui, Azalea and Gamper, Hannes and Braun, Sebastian and Emmanouilidou, Dimitra},
	month = nov,
	year = {2023},
	note = {arXiv:2311.01616 [eess]},
	keywords = {Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{noauthor_white_nodate,
	title = {White {Paper} on {Generative} {AI} and {Disinformation}: {Recent} {Advances}, {Challenges}, and {Opportunities}},
	shorttitle = {White {Paper} on {Generative} {AI} and {Disinformation}},
	url = {https://www.veraai.eu},
	abstract = {VERification Assisted by AI. R\&D \& innovation co-funded by the HorizonEU. Continuing WeVerify work. And much more!},
	urldate = {2024-03-01},
}

@misc{noauthor_claude_nodate,
	title = {Claude},
	url = {https://claude.ai/login?returnTo=%2Fonboarding},
	abstract = {Talk to Claude, an AI assistant from Anthropic},
	language = {en},
	urldate = {2024-02-28},
}

@misc{noauthor_eu_2023,
	title = {{EU} {AI} {Act}: first regulation on artificial intelligence},
	shorttitle = {{EU} {AI} {Act}},
	url = {https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence},
	abstract = {The use of artificial intelligence in the EU will be regulated by the AI Act, the world’s first comprehensive AI law. Find out how it will protect you.},
	language = {en},
	urldate = {2024-02-28},
	journal = {Topics {\textbar} European Parliament},
	month = jun,
	year = {2023},
}

@misc{woods_stimulating_2021,
	title = {Stimulating music supports attention in listeners with attentional difficulties},
	copyright = {© 2021, Posted by Cold Spring Harbor Laboratory. The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission.},
	url = {https://www.biorxiv.org/content/10.1101/2021.10.01.462777v1},
	doi = {10.1101/2021.10.01.462777},
	abstract = {Background music is widely used to sustain attention, but little is known about what musical properties aid attention. This may be due to inter-individual variability in neural responses to music. We test the hypothesis that music can sustain attention by affecting oscillations via acoustic amplitude modulation, differentially for those with varying levels of attentional difficulty. We first show that heavily-modulated music improves sustained attention for participants with more ADHD symptoms. FMRI showed this music elicited greater activity in attentional networks in this group only, and EEG showed greater stimulus-brain coupling for this group in response to the heavily-modulated music. Finally, we parametrically manipulated the depth and rate of amplitude modulations inserted in otherwise-identical music, and found that beta-range modulations helped more than other frequency ranges for participants with more ADHD symptoms. Results suggest the possibility of an oscillation-based neural mechanism for targeted music to support improved cognitive performance.},
	language = {en},
	urldate = {2024-02-28},
	publisher = {bioRxiv},
	author = {Woods, Kevin JP and Sampaio, Gonçalo and James, Tedra and Przysinda, Emily and Hewett, Adam and Spencer, Andrea E. and Morillon, Benjamin and Loui, Psyche},
	month = oct,
	year = {2021},
	note = {Pages: 2021.10.01.462777
Section: New Results},
}

@article{gao_echo_2023,
	title = {Echo chamber effects on short video platforms},
	volume = {13},
	copyright = {2023 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-023-33370-1},
	doi = {10.1038/s41598-023-33370-1},
	abstract = {In recent years, short videos have become an increasingly vital source of information. To compete for users’ attention, short video platforms have been overusing algorithmic technology, making the group polarization intensify, which is likely to push users into the homogeneous “echo chamber”. However, echo chambers can contribute to the spread of misleading information, false news, or rumors, which have negative social impacts. Therefore, it is necessary to explore echo chamber effects in short video platforms. Moreover, the communication paradigms between users and feed algorithms greatly vary across short video platforms. This paper investigated echo chamber effects of three popular short video platforms (Douyin, TikTok, and Bilibili) using social network analysis and explored how user features influenced the generation of echo chambers. We quantified echo chamber effects through two primary ingredients: selective exposure and homophily, in both platform and topic dimensions. Our analyses indicate that the gathering of users into homogeneous groups dominates online interactions on Douyin and Bilibili. We performed performance comparison of echo chamber effects and found that echo chamber members tend to display themselves to attract the attention of their peers and that cultural differences can prevent the development of echo chambers. Our findings are of great value in designing targeted management strategies to prevent the spread of misleading information, false news, or rumors.},
	language = {en},
	number = {1},
	urldate = {2024-02-28},
	journal = {Scientific Reports},
	author = {Gao, Yichang and Liu, Fengming and Gao, Lei},
	month = apr,
	year = {2023},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Information technology, Scientific data},
	pages = {6282},
}

@article{ye_effects_2022,
	title = {Effects of {Short} {Video} {Addiction} on the {Motivation} and {Well}-{Being} of {Chinese} {Vocational} {College} {Students}},
	volume = {10},
	issn = {2296-2565},
	url = {https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.847672},
	abstract = {While media use can be beneficial in some ways, excessive use of media has led to growing concerns about its potential negative consequences. With the popularity of Chinese video applications (apps) such as DouYin, TikTok, Kwai, and other short video apps sweeping through schools around the world. Due to the diversity and immersion principle of short videos, their popularity continues to grow, and the phenomenon of students being addicted to short videos also brings many hidden dangers to the learning effect. Among other things, the problem of excessive use of the Internet among Chinese youth has led the government to propose a series of control policies to strengthen the monitoring of harmful habits of youth in the use of online applications. In addition, the problem of youth addiction to short videos has become a major concern for education experts and the general public, thus demonstrating that short video addiction is indeed an ongoing research issue. Therefore, this study aimed to understand the causes of short-form video addiction and its impact on the psychology of learning, and to investigate the relationship between short-form video flow experience, short-form video addiction, intrinsic and extrinsic learning motivation, and learning well-being from the perspectives of flow experience theory and micro ecological systems. The questionnaire was sent via instant messaging software such as QQ and WeChat, and university students from vocational colleges in China were invited to complete the questionnaire. A total of 517 valid data were collected, including 222 (42.9\%) were male students and 295 (57.1\%) were female students. The collected questionnaires were analyzed for reliability and validity after removing incomplete data, followed by structural equation modeling for model verification. The findings showed that: (1) short video flow experience had a positive effect on short video addiction; (2) short video addiction had a negative effect on intrinsic and extrinsic learning motivation; (3) intrinsic and extrinsic learning motivation had a positive effect on learning well-being; (4) short video flow experience had an indirect negative effect on intrinsic and extrinsic learning motivation; (5) short video flow and short video addiction had indirect negative effects on learning well-being. According to the results, it is clear that addiction to short videos has a negative impact on learners' learning motivation and positive psychology of learning, so parents and teachers should effectively guide students to use short video apps in a self-controlled way.},
	urldate = {2024-02-28},
	journal = {Frontiers in Public Health},
	author = {Ye, Jian-Hong and Wu, Yu-Tai and Wu, Yu-Feng and Chen, Mei-Yen and Ye, Jhen-Ni},
	year = {2022},
}

@misc{caillon_rave_2021,
	title = {{RAVE}: {A} variational autoencoder for fast and high-quality neural audio synthesis},
	shorttitle = {{RAVE}},
	url = {http://arxiv.org/abs/2111.05011},
	doi = {10.48550/arXiv.2111.05011},
	abstract = {Deep generative models applied to audio have improved by a large margin the state-of-the-art in many speech and music related tasks. However, as raw waveform modelling remains an inherently difficult task, audio generative models are either computationally intensive, rely on low sampling rates, are complicated to control or restrict the nature of possible signals. Among those models, Variational AutoEncoders (VAE) give control over the generation by exposing latent variables, although they usually suffer from low synthesis quality. In this paper, we introduce a Realtime Audio Variational autoEncoder (RAVE) allowing both fast and high-quality audio waveform synthesis. We introduce a novel two-stage training procedure, namely representation learning and adversarial fine-tuning. We show that using a post-training analysis of the latent space allows a direct control between the reconstruction fidelity and the representation compactness. By leveraging a multi-band decomposition of the raw waveform, we show that our model is the first able to generate 48kHz audio signals, while simultaneously running 20 times faster than real-time on a standard laptop CPU. We evaluate synthesis quality using both quantitative and qualitative subjective experiments and show the superiority of our approach compared to existing models. Finally, we present applications of our model for timbre transfer and signal compression. All of our source code and audio examples are publicly available.},
	urldate = {2024-02-27},
	publisher = {arXiv},
	author = {Caillon, Antoine and Esling, Philippe},
	month = dec,
	year = {2021},
	note = {arXiv:2111.05011 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{zhang_unreasonable_2018,
	title = {The {Unreasonable} {Effectiveness} of {Deep} {Features} as a {Perceptual} {Metric}},
	url = {http://arxiv.org/abs/1801.03924},
	doi = {10.48550/arXiv.1801.03924},
	abstract = {While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.},
	urldate = {2024-02-26},
	publisher = {arXiv},
	author = {Zhang, Richard and Isola, Phillip and Efros, Alexei A. and Shechtman, Eli and Wang, Oliver},
	month = apr,
	year = {2018},
	note = {arXiv:1801.03924 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics},
}

@misc{song_denoising_2022,
	title = {Denoising {Diffusion} {Implicit} {Models}},
	url = {http://arxiv.org/abs/2010.02502},
	doi = {10.48550/arXiv.2010.02502},
	abstract = {Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process. We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from. We empirically demonstrate that DDIMs can produce high quality samples \$10 {\textbackslash}times\$ to \$50 {\textbackslash}times\$ faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space.},
	urldate = {2024-02-25},
	publisher = {arXiv},
	author = {Song, Jiaming and Meng, Chenlin and Ermon, Stefano},
	month = oct,
	year = {2022},
	note = {arXiv:2010.02502 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@misc{salimans_progressive_2022,
	title = {Progressive {Distillation} for {Fast} {Sampling} of {Diffusion} {Models}},
	url = {http://arxiv.org/abs/2202.00512},
	doi = {10.48550/arXiv.2202.00512},
	abstract = {Diffusion models have recently shown great promise for generative modeling, outperforming GANs on perceptual quality and autoregressive models at density estimation. A remaining downside is their slow sampling time: generating high quality samples takes many hundreds or thousands of model evaluations. Here we make two contributions to help eliminate this downside: First, we present new parameterizations of diffusion models that provide increased stability when using few sampling steps. Second, we present a method to distill a trained deterministic diffusion sampler, using many steps, into a new diffusion model that takes half as many sampling steps. We then keep progressively applying this distillation procedure to our model, halving the number of required sampling steps each time. On standard image generation benchmarks like CIFAR-10, ImageNet, and LSUN, we start out with state-of-the-art samplers taking as many as 8192 steps, and are able to distill down to models taking as few as 4 steps without losing much perceptual quality; achieving, for example, a FID of 3.0 on CIFAR-10 in 4 steps. Finally, we show that the full progressive distillation procedure does not take more time than it takes to train the original model, thus representing an efficient solution for generative modeling using diffusion at both train and test time.},
	urldate = {2024-02-25},
	publisher = {arXiv},
	author = {Salimans, Tim and Ho, Jonathan},
	month = jun,
	year = {2022},
	note = {arXiv:2202.00512 [cs, stat]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{luhman_knowledge_2021,
	title = {Knowledge {Distillation} in {Iterative} {Generative} {Models} for {Improved} {Sampling} {Speed}},
	url = {http://arxiv.org/abs/2101.02388},
	abstract = {Iterative generative models, such as noise conditional score networks and denoising diffusion probabilistic models, produce high quality samples by gradually denoising an initial noise vector. However, their denoising process has many steps, making them 2-3 orders of magnitude slower than other generative models such as GANs and VAEs. In this paper, we establish a novel connection between knowledge distillation and image generation with a technique that distills a multi-step denoising process into a single step, resulting in a sampling speed similar to other single-step generative models. Our Denoising Student generates high quality samples comparable to GANs on the CIFAR-10 and CelebA datasets, without adversarial training. We demonstrate that our method scales to higher resolutions through experiments on 256 x 256 LSUN. Code and checkpoints are available at https://github.com/tcl9876/Denoising\_Student},
	urldate = {2024-02-25},
	publisher = {arXiv},
	author = {Luhman, Eric and Luhman, Troy},
	month = jan,
	year = {2021},
	note = {arXiv:2101.02388 [cs]},
	keywords = {Computer Science - Machine Learning},
}

@misc{pasini_melgan-vc_2019,
	title = {{MelGAN}-{VC}: {Voice} {Conversion} and {Audio} {Style} {Transfer} on arbitrarily long samples using {Spectrograms}},
	shorttitle = {{MelGAN}-{VC}},
	url = {http://arxiv.org/abs/1910.03713},
	abstract = {Traditional voice conversion methods rely on parallel recordings of multiple speakers pronouncing the same sentences. For real-world applications however, parallel data is rarely available. We propose MelGAN-VC, a voice conversion method that relies on non-parallel speech data and is able to convert audio signals of arbitrary length from a source voice to a target voice. We firstly compute spectrograms from waveform data and then perform a domain translation using a Generative Adversarial Network (GAN) architecture. An additional siamese network helps preserving speech information in the translation process, without sacrificing the ability to flexibly model the style of the target speaker. We test our framework with a dataset of clean speech recordings, as well as with a collection of noisy real-world speech examples. Finally, we apply the same method to perform music style transfer, translating arbitrarily long music samples from one genre to another, and showing that our framework is flexible and can be used for audio manipulation applications different from voice conversion.},
	urldate = {2024-02-23},
	publisher = {arXiv},
	author = {Pasini, Marco},
	month = dec,
	year = {2019},
	note = {arXiv:1910.03713 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{kaneko_istftnet_2022,
	title = {{iSTFTNet}: {Fast} and {Lightweight} {Mel}-{Spectrogram} {Vocoder} {Incorporating} {Inverse} {Short}-{Time} {Fourier} {Transform}},
	shorttitle = {{iSTFTNet}},
	url = {http://arxiv.org/abs/2203.02395},
	doi = {10.48550/arXiv.2203.02395},
	abstract = {In recent text-to-speech synthesis and voice conversion systems, a mel-spectrogram is commonly applied as an intermediate representation, and the necessity for a mel-spectrogram vocoder is increasing. A mel-spectrogram vocoder must solve three inverse problems: recovery of the original-scale magnitude spectrogram, phase reconstruction, and frequency-to-time conversion. A typical convolutional mel-spectrogram vocoder solves these problems jointly and implicitly using a convolutional neural network, including temporal upsampling layers, when directly calculating a raw waveform. Such an approach allows skipping redundant processes during waveform synthesis (e.g., the direct reconstruction of high-dimensional original-scale spectrograms). By contrast, the approach solves all problems in a black box and cannot effectively employ the time-frequency structures existing in a mel-spectrogram. We thus propose iSTFTNet, which replaces some output-side layers of the mel-spectrogram vocoder with the inverse short-time Fourier transform (iSTFT) after sufficiently reducing the frequency dimension using upsampling layers, reducing the computational cost from black-box modeling and avoiding redundant estimations of high-dimensional spectrograms. During our experiments, we applied our ideas to three HiFi-GAN variants and made the models faster and more lightweight with a reasonable speech quality. Audio samples are available at https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/istftnet/.},
	urldate = {2024-02-23},
	publisher = {arXiv},
	author = {Kaneko, Takuhiro and Tanaka, Kou and Kameoka, Hirokazu and Seki, Shogo},
	month = mar,
	year = {2022},
	note = {arXiv:2203.02395 [cs, eess, stat]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning},
}

@misc{lin_infinitygan_2022,
	title = {{InfinityGAN}: {Towards} {Infinite}-{Pixel} {Image} {Synthesis}},
	shorttitle = {{InfinityGAN}},
	url = {http://arxiv.org/abs/2104.03963},
	abstract = {We present a novel framework, InfinityGAN, for arbitrary-sized image generation. The task is associated with several key challenges. First, scaling existing models to an arbitrarily large image size is resource-constrained, in terms of both computation and availability of large-field-of-view training data. InfinityGAN trains and infers in a seamless patch-by-patch manner with low computational resources. Second, large images should be locally and globally consistent, avoid repetitive patterns, and look realistic. To address these, InfinityGAN disentangles global appearances, local structures, and textures. With this formulation, we can generate images with spatial size and level of details not attainable before. Experimental evaluation validates that InfinityGAN generates images with superior realism compared to baselines and features parallelizable inference. Finally, we show several applications unlocked by our approach, such as spatial style fusion, multi-modal outpainting, and image inbetweening. All applications can be operated with arbitrary input and output sizes. Please find the full version of the paper at https://openreview.net/forum?id=ufGMqIM0a4b .},
	urldate = {2024-02-22},
	publisher = {arXiv},
	author = {Lin, Chieh Hubert and Lee, Hsin-Ying and Cheng, Yen-Chi and Tulyakov, Sergey and Yang, Ming-Hsuan},
	month = mar,
	year = {2022},
	note = {arXiv:2104.03963 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{skorokhodov_aligning_2021,
	title = {Aligning {Latent} and {Image} {Spaces} to {Connect} the {Unconnectable}},
	url = {http://arxiv.org/abs/2104.06954},
	doi = {10.48550/arXiv.2104.06954},
	abstract = {In this work, we develop a method to generate infinite high-resolution images with diverse and complex content. It is based on a perfectly equivariant generator with synchronous interpolations in the image and latent spaces. Latent codes, when sampled, are positioned on the coordinate grid, and each pixel is computed from an interpolation of the nearby style codes. We modify the AdaIN mechanism to work in such a setup and train the generator in an adversarial setting to produce images positioned between any two latent vectors. At test time, this allows for generating complex and diverse infinite images and connecting any two unrelated scenes into a single arbitrarily large panorama. Apart from that, we introduce LHQ: a new dataset of {\textbackslash}lhqsize high-resolution nature landscapes. We test the approach on LHQ, LSUN Tower and LSUN Bridge and outperform the baselines by at least 4 times in terms of quality and diversity of the produced infinite images. The project page is located at https://universome.github.io/alis.},
	urldate = {2024-02-22},
	publisher = {arXiv},
	author = {Skorokhodov, Ivan and Sotnikov, Grigorii and Elhoseiny, Mohamed},
	month = apr,
	year = {2021},
	note = {arXiv:2104.06954 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition},
}

@misc{lin_infinitygan_2022-1,
	title = {{InfinityGAN}: {Towards} {Infinite}-{Pixel} {Image} {Synthesis}},
	shorttitle = {{InfinityGAN}},
	url = {http://arxiv.org/abs/2104.03963},
	doi = {10.48550/arXiv.2104.03963},
	abstract = {We present a novel framework, InfinityGAN, for arbitrary-sized image generation. The task is associated with several key challenges. First, scaling existing models to an arbitrarily large image size is resource-constrained, in terms of both computation and availability of large-field-of-view training data. InfinityGAN trains and infers in a seamless patch-by-patch manner with low computational resources. Second, large images should be locally and globally consistent, avoid repetitive patterns, and look realistic. To address these, InfinityGAN disentangles global appearances, local structures, and textures. With this formulation, we can generate images with spatial size and level of details not attainable before. Experimental evaluation validates that InfinityGAN generates images with superior realism compared to baselines and features parallelizable inference. Finally, we show several applications unlocked by our approach, such as spatial style fusion, multi-modal outpainting, and image inbetweening. All applications can be operated with arbitrary input and output sizes. Please find the full version of the paper at https://openreview.net/forum?id=ufGMqIM0a4b .},
	urldate = {2024-02-22},
	publisher = {arXiv},
	author = {Lin, Chieh Hubert and Lee, Hsin-Ying and Cheng, Yen-Chi and Tulyakov, Sergey and Yang, Ming-Hsuan},
	month = mar,
	year = {2022},
	note = {arXiv:2104.03963 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@article{pauker_role_2018,
	title = {The {Role} of {Diversity} {Exposure} in {Whites}’ {Reduction} in {Race} {Essentialism} {Over} {Time}},
	volume = {9},
	issn = {1948-5506},
	url = {https://doi.org/10.1177/1948550617731496},
	doi = {10.1177/1948550617731496},
	abstract = {Despite multidisciplinary theorizing on the consequences of the changing racial demographics in the United States, few studies have systematically examined how exposure to racial diversity may impact White individuals’ lay beliefs about race. In a longitudinal study, we explored whether living in a racially diverse environment with a high multiracial population was related to White individuals’ endorsement of race essentialism and its downstream consequences. Endorsement of race essentialism decreased over time, and greater diversity of acquaintances over time was associated with this decrease. Race essentialism reduction also corresponded with a decrease in modern racism and social dominance orientation, and an increase in cognitive flexibility, over time. These findings are consistent with the idea that a racially diverse social context can shape endorsement of race essentialism and lead to social and cognitive benefits for White individuals.},
	language = {en},
	number = {8},
	urldate = {2024-02-21},
	journal = {Social Psychological and Personality Science},
	author = {Pauker, Kristin and Carpinella, Colleen and Meyers, Chanel and Young, Danielle M. and Sanchez, Diana T.},
	month = nov,
	year = {2018},
	note = {Publisher: SAGE Publications Inc},
	pages = {944--952},
}

@article{gibbs_choosing_2002,
	title = {On {Choosing} and {Bounding} {Probability} {Metrics}},
	volume = {70},
	issn = {0306-7734},
	url = {https://www.jstor.org/stable/1403865},
	doi = {10.2307/1403865},
	abstract = {When studying convergence of measures, an important issue is the choice of probability metric. We provide a summary and some new results concerning bounds among some important probability metrics/distances that are used by statisticians and probabilists. Knowledge of other metrics can provide a means of deriving bounds for another one in an applied problem. Considering other metrics can also provide alternate insights. We also give examples that show that rates of convergence can strongly depend on the metric chosen. Careful consideration is necessary when choosing a metric. /// Le choix de métrique de probabilité est une décision très importante lorsqu'on étudie la convergence des mesures. Nous vous fournissons avec un sommaire de plusieurs métriques/distances de probabilité couramment utilisées par des statisticiens(nes) at par des probabilistes, ainsi que certains nouveaux résultats qui se rapportent à leurs bornes. Avoir connaissance d'autres métriques peut vous fournir avec un moyen de dériver des bornes pour une autre métrique dans un problème appliqué. Le fait de prendre en considération plusieurs métriques vous permettra d'approcher des problèmes d'une manière différente. Ainsi, nous vous démontrons que les taux de convergence peuvent dépendre de façon importante sur votre choix de métrique. Il est donc important de tout considérer lorsqu'on doit choisir une métrique.},
	number = {3},
	urldate = {2024-02-20},
	journal = {International Statistical Review / Revue Internationale de Statistique},
	author = {Gibbs, Alison L. and Su, Francis Edward},
	year = {2002},
	note = {Publisher: [Wiley, International Statistical Institute (ISI)]},
	pages = {419--435},
}

@misc{salimans_improved_2016-1,
	title = {Improved {Techniques} for {Training} {GANs}},
	url = {http://arxiv.org/abs/1606.03498},
	doi = {10.48550/arXiv.1606.03498},
	abstract = {We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3\%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes.},
	urldate = {2024-02-20},
	publisher = {arXiv},
	author = {Salimans, Tim and Goodfellow, Ian and Zaremba, Wojciech and Cheung, Vicki and Radford, Alec and Chen, Xi},
	month = jun,
	year = {2016},
	note = {arXiv:1606.03498 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing},
}

@misc{heusel_gans_2018,
	title = {{GANs} {Trained} by a {Two} {Time}-{Scale} {Update} {Rule} {Converge} to a {Local} {Nash} {Equilibrium}},
	url = {http://arxiv.org/abs/1706.08500},
	doi = {10.48550/arXiv.1706.08500},
	abstract = {Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the "Fr{\textbackslash}'echet Inception Distance" (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark.},
	urldate = {2024-02-20},
	publisher = {arXiv},
	author = {Heusel, Martin and Ramsauer, Hubert and Unterthiner, Thomas and Nessler, Bernhard and Hochreiter, Sepp},
	month = jan,
	year = {2018},
	note = {arXiv:1706.08500 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{chong_effectively_2020,
	title = {Effectively {Unbiased} {FID} and {Inception} {Score} and where to find them},
	url = {http://arxiv.org/abs/1911.07023},
	doi = {10.48550/arXiv.1911.07023},
	abstract = {This paper shows that two commonly used evaluation metrics for generative models, the Fr{\textbackslash}'echet Inception Distance (FID) and the Inception Score (IS), are biased -- the expected value of the score computed for a finite sample set is not the true value of the score. Worse, the paper shows that the bias term depends on the particular model being evaluated, so model A may get a better score than model B simply because model A's bias term is smaller. This effect cannot be fixed by evaluating at a fixed number of samples. This means all comparisons using FID or IS as currently computed are unreliable. We then show how to extrapolate the score to obtain an effectively bias-free estimate of scores computed with an infinite number of samples, which we term \${\textbackslash}overline\{{\textbackslash}textrm\{FID\}\}\_{\textbackslash}infty\$ and \${\textbackslash}overline\{{\textbackslash}textrm\{IS\}\}\_{\textbackslash}infty\$. In turn, this effectively bias-free estimate requires good estimates of scores with a finite number of samples. We show that using Quasi-Monte Carlo integration notably improves estimates of FID and IS for finite sample sets. Our extrapolated scores are simple, drop-in replacements for the finite sample scores. Additionally, we show that using low discrepancy sequence in GAN training offers small improvements in the resulting generator.},
	urldate = {2024-02-19},
	publisher = {arXiv},
	author = {Chong, Min Jin and Forsyth, David},
	month = jun,
	year = {2020},
	note = {arXiv:1911.07023 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@article{thesis_archisound_nodate,
	title = {{ARCHISOUND}: {AUDIO} {GENERATION} {WITH} {DIFFUSION}},
	language = {en},
	author = {Thesis, Master’s and Jin, Supervised Zhijing and Schölkopf, Bernhard},
}

@misc{devroye_total_2023,
	title = {The total variation distance between high-dimensional {Gaussians} with the same mean},
	url = {http://arxiv.org/abs/1810.08693},
	abstract = {Given two high-dimensional Gaussians with the same mean, we prove a lower and an upper bound for their total variation distance, which are within a constant factor of one another.},
	urldate = {2024-02-16},
	publisher = {arXiv},
	author = {Devroye, Luc and Mehrabian, Abbas and Reddad, Tommy},
	month = oct,
	year = {2023},
	note = {arXiv:1810.08693 [math, stat]},
	keywords = {Mathematics - Probability, Mathematics - Statistics Theory},
}

@misc{liu_pseudo_2022,
	title = {Pseudo {Numerical} {Methods} for {Diffusion} {Models} on {Manifolds}},
	url = {http://arxiv.org/abs/2202.09778},
	doi = {10.48550/arXiv.2202.09778},
	abstract = {Denoising Diffusion Probabilistic Models (DDPMs) can generate high-quality samples such as image and audio samples. However, DDPMs require hundreds to thousands of iterations to produce final samples. Several prior works have successfully accelerated DDPMs through adjusting the variance schedule (e.g., Improved Denoising Diffusion Probabilistic Models) or the denoising equation (e.g., Denoising Diffusion Implicit Models (DDIMs)). However, these acceleration methods cannot maintain the quality of samples and even introduce new noise at a high speedup rate, which limit their practicability. To accelerate the inference process while keeping the sample quality, we provide a fresh perspective that DDPMs should be treated as solving differential equations on manifolds. Under such a perspective, we propose pseudo numerical methods for diffusion models (PNDMs). Specifically, we figure out how to solve differential equations on manifolds and show that DDIMs are simple cases of pseudo numerical methods. We change several classical numerical methods to corresponding pseudo numerical methods and find that the pseudo linear multi-step method is the best in most situations. According to our experiments, by directly using pre-trained models on Cifar10, CelebA and LSUN, PNDMs can generate higher quality synthetic images with only 50 steps compared with 1000-step DDIMs (20x speedup), significantly outperform DDIMs with 250 steps (by around 0.4 in FID) and have good generalization on different variance schedules. Our implementation is available at https://github.com/luping-liu/PNDM.},
	urldate = {2024-02-15},
	author = {Liu, Luping and Ren, Yi and Lin, Zhijie and Zhao, Zhou},
	month = oct,
	year = {2022},
	note = {arXiv:2202.09778 [cs, math, stat]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Mathematics - Numerical Analysis, Statistics - Machine Learning},
}

@misc{ma_deepcache_2023,
	title = {{DeepCache}: {Accelerating} {Diffusion} {Models} for {Free}},
	shorttitle = {{DeepCache}},
	url = {http://arxiv.org/abs/2312.00858},
	doi = {10.48550/arXiv.2312.00858},
	abstract = {Diffusion models have recently gained unprecedented attention in the field of image synthesis due to their remarkable generative capabilities. Notwithstanding their prowess, these models often incur substantial computational costs, primarily attributed to the sequential denoising process and cumbersome model size. Traditional methods for compressing diffusion models typically involve extensive retraining, presenting cost and feasibility challenges. In this paper, we introduce DeepCache, a novel training-free paradigm that accelerates diffusion models from the perspective of model architecture. DeepCache capitalizes on the inherent temporal redundancy observed in the sequential denoising steps of diffusion models, which caches and retrieves features across adjacent denoising stages, thereby curtailing redundant computations. Utilizing the property of the U-Net, we reuse the high-level features while updating the low-level features in a very cheap way. This innovative strategy, in turn, enables a speedup factor of 2.3\${\textbackslash}times\$ for Stable Diffusion v1.5 with only a 0.05 decline in CLIP Score, and 4.1\${\textbackslash}times\$ for LDM-4-G with a slight decrease of 0.22 in FID on ImageNet. Our experiments also demonstrate DeepCache's superiority over existing pruning and distillation methods that necessitate retraining and its compatibility with current sampling techniques. Furthermore, we find that under the same throughput, DeepCache effectively achieves comparable or even marginally improved results with DDIM or PLMS. The code is available at https://github.com/horseee/DeepCache},
	urldate = {2024-02-15},
	author = {Ma, Xinyin and Fang, Gongfan and Wang, Xinchao},
	month = dec,
	year = {2023},
	note = {arXiv:2312.00858 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition},
}

@misc{karras_elucidating_2022,
	title = {Elucidating the {Design} {Space} of {Diffusion}-{Based} {Generative} {Models}},
	url = {http://arxiv.org/abs/2206.00364},
	doi = {10.48550/arXiv.2206.00364},
	abstract = {We argue that the theory and practice of diffusion-based generative models are currently unnecessarily convoluted and seek to remedy the situation by presenting a design space that clearly separates the concrete design choices. This lets us identify several changes to both the sampling and training processes, as well as preconditioning of the score networks. Together, our improvements yield new state-of-the-art FID of 1.79 for CIFAR-10 in a class-conditional setting and 1.97 in an unconditional setting, with much faster sampling (35 network evaluations per image) than prior designs. To further demonstrate their modular nature, we show that our design changes dramatically improve both the efficiency and quality obtainable with pre-trained score networks from previous work, including improving the FID of a previously trained ImageNet-64 model from 2.07 to near-SOTA 1.55, and after re-training with our proposed improvements to a new SOTA of 1.36.},
	urldate = {2024-02-15},
	author = {Karras, Tero and Aittala, Miika and Aila, Timo and Laine, Samuli},
	month = oct,
	year = {2022},
	note = {arXiv:2206.00364 [cs, stat]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Statistics - Machine Learning},
}

@misc{zhu_diffusion_2024,
	title = {Diffusion {Models} for {Reinforcement} {Learning}: {A} {Survey}},
	shorttitle = {Diffusion {Models} for {Reinforcement} {Learning}},
	url = {http://arxiv.org/abs/2311.01223},
	doi = {10.48550/arXiv.2311.01223},
	abstract = {Diffusion models surpass previous generative models in sample quality and training stability. Recent works have shown the advantages of diffusion models in improving reinforcement learning (RL) solutions. This survey aims to provide an overview of this emerging field and hopes to inspire new avenues of research. First, we examine several challenges encountered by RL algorithms. Then, we present a taxonomy of existing methods based on the roles of diffusion models in RL and explore how the preceding challenges are addressed. We further outline successful applications of diffusion models in various RL-related tasks. Finally, we conclude the survey and offer insights into future research directions. We are actively maintaining a GitHub repository for papers and other related resources in utilizing diffusion models in RL: https://github.com/apexrl/Diff4RLSurvey.},
	urldate = {2024-02-14},
	publisher = {arXiv},
	author = {Zhu, Zhengbang and Zhao, Hanye and He, Haoran and Zhong, Yichao and Zhang, Shenyu and Guo, Haoquan and Chen, Tingting and Zhang, Weinan},
	month = feb,
	year = {2024},
	note = {arXiv:2311.01223 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
}

@article{noauthor_text-and-audio_nodate,
	title = {Text-and-audio  methods - {Catalina} {Cangeae} {Guest} {Lecture}},
	language = {en},
}

@article{go_combinatorial_2023,
	title = {Combinatorial music generation model with song structure graph analysis},
	copyright = {Creative Commons Attribution 4.0 International},
	url = {https://arxiv.org/abs/2312.15400},
	doi = {10.48550/ARXIV.2312.15400},
	abstract = {In this work, we propose a symbolic music generation model with the song structure graph analysis network. We construct a graph that uses information such as note sequence and instrument as node features, while the correlation between note sequences acts as the edge feature. We trained a Graph Neural Network to obtain node representation in the graph, then we use node representation as input of Unet to generate CONLON pianoroll image latent. The outcomes of our experimental results show that the proposed model can generate a comprehensive form of music. Our approach represents a promising and innovative method for symbolic music generation and holds potential applications in various fields in Music Information Retreival, including music composition, music classification, and music inpainting systems.},
	urldate = {2024-02-13},
	author = {Go, Seonghyeon and Lee, Kyogu},
	year = {2023},
	note = {Publisher: arXiv
Version Number: 1},
	keywords = {Artificial Intelligence (cs.AI), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, Sound (cs.SD)},
}

@misc{go_combinatorial_2023-1,
	title = {Combinatorial music generation model with song structure graph analysis},
	url = {https://arxiv.org/abs/2312.15400v1},
	abstract = {In this work, we propose a symbolic music generation model with the song structure graph analysis network. We construct a graph that uses information such as note sequence and instrument as node features, while the correlation between note sequences acts as the edge feature. We trained a Graph Neural Network to obtain node representation in the graph, then we use node representation as input of Unet to generate CONLON pianoroll image latent. The outcomes of our experimental results show that the proposed model can generate a comprehensive form of music. Our approach represents a promising and innovative method for symbolic music generation and holds potential applications in various fields in Music Information Retreival, including music composition, music classification, and music inpainting systems.},
	language = {en},
	urldate = {2024-02-13},
	journal = {arXiv.org},
	author = {Go, Seonghyeon and Lee, Kyogu},
	month = dec,
	year = {2023},
}

@misc{rombach_high-resolution_2022,
	title = {High-{Resolution} {Image} {Synthesis} with {Latent} {Diffusion} {Models}},
	url = {http://arxiv.org/abs/2112.10752},
	doi = {10.48550/arXiv.2112.10752},
	abstract = {By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs. Code is available at https://github.com/CompVis/latent-diffusion .},
	urldate = {2024-02-13},
	publisher = {arXiv},
	author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Björn},
	month = apr,
	year = {2022},
	note = {arXiv:2112.10752 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{mallasto_entropy-regularized_2020,
	title = {Entropy-{Regularized} \$2\$-{Wasserstein} {Distance} between {Gaussian} {Measures}},
	url = {http://arxiv.org/abs/2006.03416},
	abstract = {Gaussian distributions are plentiful in applications dealing in uncertainty quantification and diffusivity. They furthermore stand as important special cases for frameworks providing geometries for probability measures, as the resulting geometry on Gaussians is often expressible in closed-form under the frameworks. In this work, we study the Gaussian geometry under the entropy-regularized 2-Wasserstein distance, by providing closed-form solutions for the distance and interpolations between elements. Furthermore, we provide a fixed-point characterization of a population barycenter when restricted to the manifold of Gaussians, which allows computations through the fixed-point iteration algorithm. As a consequence, the results yield closed-form expressions for the 2-Sinkhorn divergence. As the geometries change by varying the regularization magnitude, we study the limiting cases of vanishing and infinite magnitudes, reconfirming well-known results on the limits of the Sinkhorn divergence. Finally, we illustrate the resulting geometries with a numerical study.},
	urldate = {2024-02-12},
	publisher = {arXiv},
	author = {Mallasto, Anton and Gerolin, Augusto and Minh, Hà Quang},
	month = jun,
	year = {2020},
	note = {arXiv:2006.03416 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{sohl-dickstein_deep_2015,
	title = {Deep {Unsupervised} {Learning} using {Nonequilibrium} {Thermodynamics}},
	url = {http://arxiv.org/abs/1503.03585},
	doi = {10.48550/arXiv.1503.03585},
	abstract = {A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm.},
	urldate = {2024-02-11},
	publisher = {arXiv},
	author = {Sohl-Dickstein, Jascha and Weiss, Eric A. and Maheswaranathan, Niru and Ganguli, Surya},
	month = nov,
	year = {2015},
	note = {arXiv:1503.03585 [cond-mat, q-bio, stat]},
	keywords = {Computer Science - Machine Learning, Condensed Matter - Disordered Systems and Neural Networks, Quantitative Biology - Neurons and Cognition, Statistics - Machine Learning},
}

@article{lovato_effects_2010,
	title = {The effects of napping on cognitive functioning},
	volume = {185},
	issn = {1875-7855},
	doi = {10.1016/B978-0-444-53702-7.00009-9},
	abstract = {Naps (brief sleeps) are a global and highly prevalent phenomenon, thus warranting consideration for their effects on cognitive functioning. Naps can reduce sleepiness and improve cognitive performance. The benefits of brief (5-15 min) naps are almost immediate after the nap and last a limited period (1-3h). Longer naps ({\textgreater} 30 min) can produce impairment from sleep inertia for a short period after waking but then produce improved cognitive performance for a longer period (up to many hours). Other factors that affect the benefits from the nap are the circadian timing of the nap with early afternoon being the most favourable time. Longer periods of prior wakefulness favour longer naps over brief naps. Those who regularly nap seem to show greater benefits than those who rarely nap. These conclusions, however, need to be accepted cautiously until more comprehensive research programmes are conducted in which all these parameters are varied. Research is also needed to test the benefits of brief naps taken more naturalistically at the time when sleepiness becomes intrusive. The significant benefits of a brief nap, containing virtually no slow wave EEG activity, are not predicted by the present theory of homeostatic sleep drive (Process S). A new biological process (Process O) suggests that sleep onset followed by only 7-10 min of sleep can result in a substantial increase of alertness because it allows the rapid dissipation of inhibition in the 'wake-active' cells associated with the 'sleep-switch' mechanism rather than the dissipation of Process S.},
	language = {eng},
	journal = {Progress in Brain Research},
	author = {Lovato, Nicole and Lack, Leon},
	year = {2010},
	pmid = {21075238},
	keywords = {Brain, Cognition, Homeostasis, Humans, Sleep, Time Factors, Wakefulness},
	pages = {155--166},
}

@inproceedings{bittner_lightweight_2022,
	title = {A {Lightweight} {Instrument}-{Agnostic} {Model} for {Polyphonic} {Note} {Transcription} and {Multipitch} {Estimation}},
	url = {https://ieeexplore.ieee.org/document/9746549},
	doi = {10.1109/ICASSP43922.2022.9746549},
	abstract = {Automatic Music Transcription (AMT) has been recognized as a key enabling technology with a wide range of applications. Given the task’s complexity, best results have typically been reported for systems focusing on specific settings, e.g. instrument-specific systems tend to yield improved results over instrument-agnostic methods. Similarly, higher accuracy can be obtained when only estimating frame-wise f0 values and neglecting the harder note event detection. Despite their high accuracy, such specialized systems often cannot be deployed in the real-world. Storage and network constraints prohibit the use of multiple specialized models, while memory and run-time constraints limit their complexity. In this paper, we propose a lightweight neural network for musical instrument transcription, which supports polyphonic outputs and generalizes to a wide variety of instruments (including vocals). Our model is trained to jointly predict frame-wise onsets, multipitch and note activations, and we experimentally show that this multi-output structure improves the resulting frame-level note accuracy. Despite its simplicity, benchmark results show our system’s note estimation to be substantially better than a comparable baseline, and its frame-level accuracy to be only marginally below those of specialized state-of-the-art AMT systems. With this work we hope to encourage the community to further investigate low-resource, instrument-agnostic AMT systems.},
	urldate = {2024-02-08},
	booktitle = {{ICASSP} 2022 - 2022 {IEEE} {International} {Conference} on {Acoustics}, {Speech} and {Signal} {Processing} ({ICASSP})},
	author = {Bittner, Rachel M. and Bosch, Juan José and Rubinstein, David and Meseguer-Brocal, Gabriel and Ewert, Sebastian},
	month = may,
	year = {2022},
	note = {ISSN: 2379-190X},
	keywords = {Computational modeling, Estimation, Instruments, Music, Predictive models, Signal processing, Stacking, automatic music transcription, low-resource, multi-pitch estimation, note estimation, polyphonic},
	pages = {781--785},
}

@misc{meng_sdedit_2022,
	title = {{SDEdit}: {Guided} {Image} {Synthesis} and {Editing} with {Stochastic} {Differential} {Equations}},
	shorttitle = {{SDEdit}},
	url = {http://arxiv.org/abs/2108.01073},
	doi = {10.48550/arXiv.2108.01073},
	abstract = {Guided image synthesis enables everyday users to create and edit photo-realistic images with minimum effort. The key challenge is balancing faithfulness to the user input (e.g., hand-drawn colored strokes) and realism of the synthesized image. Existing GAN-based methods attempt to achieve such balance using either conditional GANs or GAN inversions, which are challenging and often require additional training data or loss functions for individual applications. To address these issues, we introduce a new image synthesis and editing method, Stochastic Differential Editing (SDEdit), based on a diffusion model generative prior, which synthesizes realistic images by iteratively denoising through a stochastic differential equation (SDE). Given an input image with user guide of any type, SDEdit first adds noise to the input, then subsequently denoises the resulting image through the SDE prior to increase its realism. SDEdit does not require task-specific training or inversions and can naturally achieve the balance between realism and faithfulness. SDEdit significantly outperforms state-of-the-art GAN-based methods by up to 98.09\% on realism and 91.72\% on overall satisfaction scores, according to a human perception study, on multiple tasks, including stroke-based image synthesis and editing as well as image compositing.},
	urldate = {2024-02-06},
	publisher = {arXiv},
	author = {Meng, Chenlin and He, Yutong and Song, Yang and Song, Jiaming and Wu, Jiajun and Zhu, Jun-Yan and Ermon, Stefano},
	month = jan,
	year = {2022},
	note = {arXiv:2108.01073 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition},
}

@misc{wang_audit_2023,
	title = {{AUDIT}: {Audio} {Editing} by {Following} {Instructions} with {Latent} {Diffusion} {Models}},
	shorttitle = {{AUDIT}},
	url = {http://arxiv.org/abs/2304.00830},
	doi = {10.48550/arXiv.2304.00830},
	abstract = {Audio editing is applicable for various purposes, such as adding background sound effects, replacing a musical instrument, and repairing damaged audio. Recently, some diffusion-based methods achieved zero-shot audio editing by using a diffusion and denoising process conditioned on the text description of the output audio. However, these methods still have some problems: 1) they have not been trained on editing tasks and cannot ensure good editing effects; 2) they can erroneously modify audio segments that do not require editing; 3) they need a complete description of the output audio, which is not always available or necessary in practical scenarios. In this work, we propose AUDIT, an instruction-guided audio editing model based on latent diffusion models. Specifically, AUDIT has three main design features: 1) we construct triplet training data (instruction, input audio, output audio) for different audio editing tasks and train a diffusion model using instruction and input (to be edited) audio as conditions and generating output (edited) audio; 2) it can automatically learn to only modify segments that need to be edited by comparing the difference between the input and output audio; 3) it only needs edit instructions instead of full target audio descriptions as text input. AUDIT achieves state-of-the-art results in both objective and subjective metrics for several audio editing tasks (e.g., adding, dropping, replacement, inpainting, super-resolution). Demo samples are available at https://audit-demo.github.io/.},
	urldate = {2024-02-06},
	publisher = {arXiv},
	author = {Wang, Yuancheng and Ju, Zeqian and Tan, Xu and He, Lei and Wu, Zhizheng and Bian, Jiang and Zhao, Sheng},
	month = apr,
	year = {2023},
	note = {arXiv:2304.00830 [cs, eess]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{wang_audit_2023-1,
	title = {{AUDIT}: {Audio} {Editing} by {Following} {Instructions} with {Latent} {Diffusion} {Models}},
	shorttitle = {{AUDIT}},
	url = {http://arxiv.org/abs/2304.00830},
	abstract = {Audio editing is applicable for various purposes, such as adding background sound effects, replacing a musical instrument, and repairing damaged audio. Recently, some diffusion-based methods achieved zero-shot audio editing by using a diffusion and denoising process conditioned on the text description of the output audio. However, these methods still have some problems: 1) they have not been trained on editing tasks and cannot ensure good editing effects; 2) they can erroneously modify audio segments that do not require editing; 3) they need a complete description of the output audio, which is not always available or necessary in practical scenarios. In this work, we propose AUDIT, an instruction-guided audio editing model based on latent diffusion models. Specifically, AUDIT has three main design features: 1) we construct triplet training data (instruction, input audio, output audio) for different audio editing tasks and train a diffusion model using instruction and input (to be edited) audio as conditions and generating output (edited) audio; 2) it can automatically learn to only modify segments that need to be edited by comparing the difference between the input and output audio; 3) it only needs edit instructions instead of full target audio descriptions as text input. AUDIT achieves state-of-the-art results in both objective and subjective metrics for several audio editing tasks (e.g., adding, dropping, replacement, inpainting, super-resolution). Demo samples are available at https://audit-demo.github.io/.},
	urldate = {2024-02-06},
	publisher = {arXiv},
	author = {Wang, Yuancheng and Ju, Zeqian and Tan, Xu and He, Lei and Wu, Zhizheng and Bian, Jiang and Zhao, Sheng},
	month = apr,
	year = {2023},
	note = {arXiv:2304.00830 [cs, eess]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{hoogeboom_autoregressive_2022,
	title = {Autoregressive {Diffusion} {Models}},
	url = {http://arxiv.org/abs/2110.02037},
	doi = {10.48550/arXiv.2110.02037},
	abstract = {We introduce Autoregressive Diffusion Models (ARDMs), a model class encompassing and generalizing order-agnostic autoregressive models (Uria et al., 2014) and absorbing discrete diffusion (Austin et al., 2021), which we show are special cases of ARDMs under mild assumptions. ARDMs are simple to implement and easy to train. Unlike standard ARMs, they do not require causal masking of model representations, and can be trained using an efficient objective similar to modern probabilistic diffusion models that scales favourably to highly-dimensional data. At test time, ARDMs support parallel generation which can be adapted to fit any given generation budget. We find that ARDMs require significantly fewer steps than discrete diffusion models to attain the same performance. Finally, we apply ARDMs to lossless compression, and show that they are uniquely suited to this task. Contrary to existing approaches based on bits-back coding, ARDMs obtain compelling results not only on complete datasets, but also on compressing single data points. Moreover, this can be done using a modest number of network calls for (de)compression due to the model's adaptable parallel generation.},
	urldate = {2024-02-06},
	publisher = {arXiv},
	author = {Hoogeboom, Emiel and Gritsenko, Alexey A. and Bastings, Jasmijn and Poole, Ben and Berg, Rianne van den and Salimans, Tim},
	month = feb,
	year = {2022},
	note = {arXiv:2110.02037 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{anderson_bottom-up_2018,
	title = {Bottom-{Up} and {Top}-{Down} {Attention} for {Image} {Captioning} and {Visual} {Question} {Answering}},
	url = {http://arxiv.org/abs/1707.07998},
	doi = {10.48550/arXiv.1707.07998},
	abstract = {Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions. This is the natural basis for attention to be considered. Within our approach, the bottom-up mechanism (based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state-of-the-art for the task, achieving CIDEr / SPICE / BLEU-4 scores of 117.9, 21.5 and 36.9, respectively. Demonstrating the broad applicability of the method, applying the same approach to VQA we obtain first place in the 2017 VQA Challenge.},
	urldate = {2024-02-05},
	publisher = {arXiv},
	author = {Anderson, Peter and He, Xiaodong and Buehler, Chris and Teney, Damien and Johnson, Mark and Gould, Stephen and Zhang, Lei},
	month = mar,
	year = {2018},
	note = {arXiv:1707.07998 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@inproceedings{han_multimodal_2022,
	address = {New Orleans, LA, USA},
	title = {Multimodal {Dynamics}: {Dynamical} {Fusion} for {Trustworthy} {Multimodal} {Classification}},
	isbn = {978-1-66546-946-3},
	shorttitle = {Multimodal {Dynamics}},
	url = {https://ieeexplore.ieee.org/document/9878603/},
	doi = {10.1109/CVPR52688.2022.02005},
	abstract = {Integration of heterogeneous and high-dimensional data (e.g., multiomics) is becoming increasingly important. Existing multimodal classification algorithms mainly focus on improving performance by exploiting the complementarity from different modalities. However, conventional approaches are basically weak in providing trustworthy multimodal fusion, especially for safety-critical applications (e.g., medical diagnosis). For this issue, we propose a novel trustworthy multimodal classification algorithm termed Multimodal Dynamics, which dynamically evaluates both the feature-level and modality-level informativeness for different samples and thus trustworthily integrates multiple modalities. Specifically, a sparse gating is introduced to capture the information variation of each within-modality feature and the true class probability is employed to assess the classification confidence of each modality. Then a transparent fusion algorithm based on the dynamical informativeness estimation strategy is induced. To the best of our knowledge, this is the first work to jointly model both feature and modality variation for different samples to provide trustworthy fusion in multi-modal classification. Extensive experiments are conducted on multimodal medical classification datasets. In these experiments, superior performance and trustworthiness of our algorithm are clearly validated compared to the state-of-the-art methods.},
	language = {en},
	urldate = {2024-02-05},
	booktitle = {2022 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	publisher = {IEEE},
	author = {Han, Zongbo and Yang, Fan and Huang, Junzhou and Zhang, Changqing and Yao, Jianhua},
	month = jun,
	year = {2022},
	pages = {20675--20685},
}

@misc{gao_reconet_2018,
	title = {{ReCoNet}: {Real}-time {Coherent} {Video} {Style} {Transfer} {Network}},
	shorttitle = {{ReCoNet}},
	url = {http://arxiv.org/abs/1807.01197},
	doi = {10.48550/arXiv.1807.01197},
	abstract = {Image style transfer models based on convolutional neural networks usually suffer from high temporal inconsistency when applied to videos. Some video style transfer models have been proposed to improve temporal consistency, yet they fail to guarantee fast processing speed, nice perceptual style quality and high temporal consistency at the same time. In this paper, we propose a novel real-time video style transfer model, ReCoNet, which can generate temporally coherent style transfer videos while maintaining favorable perceptual styles. A novel luminance warping constraint is added to the temporal loss at the output level to capture luminance changes between consecutive frames and increase stylization stability under illumination effects. We also propose a novel feature-map-level temporal loss to further enhance temporal consistency on traceable objects. Experimental results indicate that our model exhibits outstanding performance both qualitatively and quantitatively.},
	urldate = {2024-02-05},
	publisher = {arXiv},
	author = {Gao, Chang and Gu, Derun and Zhang, Fangjun and Yu, Yizhou},
	month = nov,
	year = {2018},
	note = {arXiv:1807.01197 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{trosten_reconsidering_2021,
	title = {Reconsidering {Representation} {Alignment} for {Multi}-view {Clustering}},
	url = {http://arxiv.org/abs/2103.07738},
	doi = {10.48550/arXiv.2103.07738},
	abstract = {Aligning distributions of view representations is a core component of today's state of the art models for deep multi-view clustering. However, we identify several drawbacks with na{\textbackslash}"ively aligning representation distributions. We demonstrate that these drawbacks both lead to less separable clusters in the representation space, and inhibit the model's ability to prioritize views. Based on these observations, we develop a simple baseline model for deep multi-view clustering. Our baseline model avoids representation alignment altogether, while performing similar to, or better than, the current state of the art. We also expand our baseline model by adding a contrastive learning component. This introduces a selective alignment procedure that preserves the model's ability to prioritize views. Our experiments show that the contrastive learning component enhances the baseline model, improving on the current state of the art by a large margin on several datasets.},
	urldate = {2024-02-05},
	publisher = {arXiv},
	author = {Trosten, Daniel J. and Løkse, Sigurd and Jenssen, Robert and Kampffmeyer, Michael},
	month = mar,
	year = {2021},
	note = {arXiv:2103.07738 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@misc{nagrani_attention_2022,
	title = {Attention {Bottlenecks} for {Multimodal} {Fusion}},
	url = {http://arxiv.org/abs/2107.00135},
	doi = {10.48550/arXiv.2107.00135},
	abstract = {Humans perceive the world by concurrently processing and fusing high-dimensional inputs from multiple modalities such as vision and audio. Machine perception models, in stark contrast, are typically modality-specific and optimised for unimodal benchmarks, and hence late-stage fusion of final representations or predictions from each modality (`late-fusion') is still a dominant paradigm for multimodal video classification. Instead, we introduce a novel transformer based architecture that uses `fusion bottlenecks' for modality fusion at multiple layers. Compared to traditional pairwise self-attention, our model forces information between different modalities to pass through a small number of bottleneck latents, requiring the model to collate and condense the most relevant information in each modality and only share what is necessary. We find that such a strategy improves fusion performance, at the same time reducing computational cost. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple audio-visual classification benchmarks including Audioset, Epic-Kitchens and VGGSound. All code and models will be released.},
	urldate = {2024-02-05},
	publisher = {arXiv},
	author = {Nagrani, Arsha and Yang, Shan and Arnab, Anurag and Jansen, Aren and Schmid, Cordelia and Sun, Chen},
	month = nov,
	year = {2022},
	note = {arXiv:2107.00135 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@article{vargas_r255_nodate,
	title = {R255 {TOPIC}: {DIFFUSION} {MODELS} {AND} {STOCHASTIC} {CALCULUS}, {COURSEWORK}},
	abstract = {Coursework for R255, I recommend this course only for students with a strong interest in probability and calculus. I’ve made sure that the mathematics stays at the level taught in an engineering / computer science undergrad, however further mathematical maturity such as courses in advanced probability or randomised algorithms, familiarity with MCMC and knowledge of basic analysis can make reading some of the relevant papers easier.},
	language = {en},
	author = {Vargas, Francisco},
}

@inproceedings{meng_distillation_2023,
	address = {Vancouver, BC, Canada},
	title = {On {Distillation} of {Guided} {Diffusion} {Models}},
	isbn = {9798350301298},
	url = {https://ieeexplore.ieee.org/document/10204696/},
	doi = {10.1109/CVPR52729.2023.01374},
	language = {en},
	urldate = {2024-02-02},
	booktitle = {2023 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	publisher = {IEEE},
	author = {Meng, Chenlin and Rombach, Robin and Gao, Ruiqi and Kingma, Diederik and Ermon, Stefano and Ho, Jonathan and Salimans, Tim},
	month = jun,
	year = {2023},
	pages = {14297--14306},
}

@misc{gu_boot_2023,
	title = {{BOOT}: {Data}-free {Distillation} of {Denoising} {Diffusion} {Models} with {Bootstrapping}},
	shorttitle = {{BOOT}},
	url = {http://arxiv.org/abs/2306.05544},
	doi = {10.48550/arXiv.2306.05544},
	abstract = {Diffusion models have demonstrated excellent potential for generating diverse images. However, their performance often suffers from slow generation due to iterative denoising. Knowledge distillation has been recently proposed as a remedy that can reduce the number of inference steps to one or a few without significant quality degradation. However, existing distillation methods either require significant amounts of offline computation for generating synthetic training data from the teacher model or need to perform expensive online learning with the help of real data. In this work, we present a novel technique called BOOT, that overcomes these limitations with an efficient data-free distillation algorithm. The core idea is to learn a time-conditioned model that predicts the output of a pre-trained diffusion model teacher given any time step. Such a model can be efficiently trained based on bootstrapping from two consecutive sampled steps. Furthermore, our method can be easily adapted to large-scale text-to-image diffusion models, which are challenging for conventional methods given the fact that the training sets are often large and difficult to access. We demonstrate the effectiveness of our approach on several benchmark datasets in the DDIM setting, achieving comparable generation quality while being orders of magnitude faster than the diffusion teacher. The text-to-image results show that the proposed approach is able to handle highly complex distributions, shedding light on more efficient generative modeling.},
	urldate = {2024-02-02},
	publisher = {arXiv},
	author = {Gu, Jiatao and Zhai, Shuangfei and Zhang, Yizhe and Liu, Lingjie and Susskind, Josh},
	month = jun,
	year = {2023},
	note = {arXiv:2306.05544 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@misc{li_is_2023,
	title = {Is {Synthetic} {Data} {From} {Diffusion} {Models} {Ready} for {Knowledge} {Distillation}?},
	url = {http://arxiv.org/abs/2305.12954},
	doi = {10.48550/arXiv.2305.12954},
	abstract = {Diffusion models have recently achieved astonishing performance in generating high-fidelity photo-realistic images. Given their huge success, it is still unclear whether synthetic images are applicable for knowledge distillation when real images are unavailable. In this paper, we extensively study whether and how synthetic images produced from state-of-the-art diffusion models can be used for knowledge distillation without access to real images, and obtain three key conclusions: (1) synthetic data from diffusion models can easily lead to state-of-the-art performance among existing synthesis-based distillation methods, (2) low-fidelity synthetic images are better teaching materials, and (3) relatively weak classifiers are better teachers. Code is available at https://github.com/zhengli97/DM-KD.},
	urldate = {2024-02-02},
	publisher = {arXiv},
	author = {Li, Zheng and Li, Yuxuan and Zhao, Penghai and Song, Renjie and Li, Xiang and Yang, Jian},
	month = may,
	year = {2023},
	note = {arXiv:2305.12954 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{luo_comprehensive_2023,
	title = {A {Comprehensive} {Survey} on {Knowledge} {Distillation} of {Diffusion} {Models}},
	url = {http://arxiv.org/abs/2304.04262},
	doi = {10.48550/arXiv.2304.04262},
	abstract = {Diffusion Models (DMs), also referred to as score-based diffusion models, utilize neural networks to specify score functions. Unlike most other probabilistic models, DMs directly model the score functions, which makes them more flexible to parametrize and potentially highly expressive for probabilistic modeling. DMs can learn fine-grained knowledge, i.e., marginal score functions, of the underlying distribution. Therefore, a crucial research direction is to explore how to distill the knowledge of DMs and fully utilize their potential. Our objective is to provide a comprehensible overview of the modern approaches for distilling DMs, starting with an introduction to DMs and a discussion of the challenges involved in distilling them into neural vector fields. We also provide an overview of the existing works on distilling DMs into both stochastic and deterministic implicit generators. Finally, we review the accelerated diffusion sampling algorithms as a training-free method for distillation. Our tutorial is intended for individuals with a basic understanding of generative models who wish to apply DM's distillation or embark on a research project in this field.},
	urldate = {2024-02-02},
	publisher = {arXiv},
	author = {Luo, Weijian},
	month = apr,
	year = {2023},
	note = {arXiv:2304.04262 [cs]},
	keywords = {Computer Science - Machine Learning},
}

@misc{huang_knowledge_2023,
	title = {Knowledge {Diffusion} for {Distillation}},
	url = {http://arxiv.org/abs/2305.15712},
	doi = {10.48550/arXiv.2305.15712},
	abstract = {The representation gap between teacher and student is an emerging topic in knowledge distillation (KD). To reduce the gap and improve the performance, current methods often resort to complicated training schemes, loss functions, and feature alignments, which are task-specific and feature-specific. In this paper, we state that the essence of these methods is to discard the noisy information and distill the valuable information in the feature, and propose a novel KD method dubbed DiffKD, to explicitly denoise and match features using diffusion models. Our approach is based on the observation that student features typically contain more noises than teacher features due to the smaller capacity of student model. To address this, we propose to denoise student features using a diffusion model trained by teacher features. This allows us to perform better distillation between the refined clean feature and teacher feature. Additionally, we introduce a light-weight diffusion model with a linear autoencoder to reduce the computation cost and an adaptive noise matching module to improve the denoising performance. Extensive experiments demonstrate that DiffKD is effective across various types of features and achieves state-of-the-art performance consistently on image classification, object detection, and semantic segmentation tasks. Code is available at https://github.com/hunto/DiffKD.},
	urldate = {2024-02-02},
	publisher = {arXiv},
	author = {Huang, Tao and Zhang, Yuan and Zheng, Mingkai and You, Shan and Wang, Fei and Qian, Chen and Xu, Chang},
	month = dec,
	year = {2023},
	note = {arXiv:2305.15712 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition},
}

@misc{noauthor_legally_nodate,
	title = {Legally {Scrape} {Spotify} {Data} to {Excel} \& {CSV} via {API}},
	url = {https://stevesie.com/apps/spotify-api},
	urldate = {2024-01-30},
}

@article{scarratt_audio_2023,
	title = {The audio features of sleep music: {Universal} and subgroup characteristics},
	volume = {18},
	issn = {1932-6203},
	shorttitle = {The audio features of sleep music},
	doi = {10.1371/journal.pone.0278813},
	abstract = {Throughout history, lullabies have been used to help children sleep, and today, with the increasing accessibility of recorded music, many people report listening to music as a tool to improve sleep. Nevertheless, we know very little about this common human habit. In this study, we elucidated the characteristics of music associated with sleep by extracting audio features from a large number of tracks (N = 225,626) retrieved from sleep playlists at the global streaming platform Spotify. Compared to music in general, we found that sleep music was softer and slower; it was more often instrumental (i.e. without lyrics) and played on acoustic instruments. Yet, a large amount of variation was present in sleep music, which clustered into six distinct subgroups. Strikingly, three of the subgroups included popular tracks that were faster, louder, and more energetic than average sleep music. The findings reveal previously unknown aspects of the audio features of sleep music and highlight the individual variation in the choice of music used for sleep. By using digital traces, we were able to determine the universal and subgroup characteristics of sleep music in a unique, global dataset, advancing our understanding of how humans use music to regulate their behaviour in everyday life.},
	language = {en},
	number = {1},
	urldate = {2024-01-30},
	journal = {PLOS ONE},
	author = {Scarratt, Rebecca Jane and Heggli, Ole Adrian and Vuust, Peter and Jespersen, Kira Vibe},
	month = jan,
	year = {2023},
	note = {Publisher: Public Library of Science},
	keywords = {Acoustics, Bioacoustics, Linear discriminant analysis, Music cognition, Music therapy, Relaxation (psychology), Sleep, Statistical data},
	pages = {e0278813},
}

@article{trahan_music_2018,
	title = {The music that helps people sleep and the reasons they believe it works: {A} mixed methods analysis of online survey reports},
	volume = {13},
	issn = {1932-6203},
	shorttitle = {The music that helps people sleep and the reasons they believe it works},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6235300/},
	doi = {10.1371/journal.pone.0206531},
	abstract = {Sleep loss is a widespread problem with serious physical and economic consequences. Music can impact upon physical, psychological and emotional states, which may explain anecdotal reports of its success as an everyday sleep aid. However, there is a lack of systematic data on how widely it is used, why people opt for music as a sleep aid, or what music works; hence the underlying drivers to music-sleep effects remain unclear. We investigated music as a sleep aid within the general public via a mixed methods data online survey (n = 651) that scored musicality, sleep habits, and open text responses on what music helps sleep and why. In total, 62\% of respondents stated that they used music to help them sleep. They reported fourteen musical genres comprising 545 artists. Linear modelling found stress, age, and music use as significant predictors of sleep quality (PSQI) scores. Regression tree modelling revealed that younger people with higher musical engagement were significantly more likely to use music to aid sleep. Thematic analysis of the open text responses generated four themes that described why people believe music helps sleep: music offers unique properties that stimulate sleep (Provide), music is part of a normal sleep routine (Habit), music induces a physical or mental state conducive to sleep (State), and music blocks an internal or external stimulus that would otherwise disrupt sleep (Distract). This survey provides new evidence into the relationship between music and sleep in a population that ranged widely in age, musicality, sleep habits and stress levels. In particular, the results highlight the varied pathways of effect between music and sleep. Diversity was observed both in music choices, which reflected idiosyncratic preferences rather than any consistent musical structure, and in the reasons why music supports good sleep, which went far beyond simple physical/mental relaxation.},
	number = {11},
	urldate = {2024-01-30},
	journal = {PLoS ONE},
	author = {Trahan, Tabitha and Durrant, Simon J. and Müllensiefen, Daniel and Williamson, Victoria J.},
	month = nov,
	year = {2018},
	pmid = {30427881},
	pmcid = {PMC6235300},
	pages = {e0206531},
}

@misc{sohl-dickstein_deep_2015-1,
	title = {Deep {Unsupervised} {Learning} using {Nonequilibrium} {Thermodynamics}},
	url = {http://arxiv.org/abs/1503.03585},
	doi = {10.48550/arXiv.1503.03585},
	abstract = {A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm.},
	urldate = {2024-01-29},
	publisher = {arXiv},
	author = {Sohl-Dickstein, Jascha and Weiss, Eric A. and Maheswaranathan, Niru and Ganguli, Surya},
	month = nov,
	year = {2015},
	note = {arXiv:1503.03585 [cond-mat, q-bio, stat]},
	keywords = {Computer Science - Machine Learning, Condensed Matter - Disordered Systems and Neural Networks, Quantitative Biology - Neurons and Cognition, Statistics - Machine Learning},
}

@misc{noauthor_gramerror_nodate,
	title = {{GramError}: {A} {Quality} {Metric} for {Machine} {Generated} {Songs} {\textbar} {SpringerLink}},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-04191-5_16},
	urldate = {2024-01-29},
}

@misc{radford_learning_2021,
	title = {Learning {Transferable} {Visual} {Models} {From} {Natural} {Language} {Supervision}},
	url = {http://arxiv.org/abs/2103.00020},
	doi = {10.48550/arXiv.2103.00020},
	abstract = {State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP.},
	urldate = {2024-01-28},
	publisher = {arXiv},
	author = {Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and Krueger, Gretchen and Sutskever, Ilya},
	month = feb,
	year = {2021},
	note = {arXiv:2103.00020 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@article{dockhorn_distilling_nodate,
	title = {Distilling the {Knowledge} in {Diffusion} {Models}},
	abstract = {Large-scale diffusion models have achieved unprecedented results in (conditional) image synthesis, however, they generally require a large amount of GPU memory and are slow at inference time. To overcome this limitation, we propose to distill the knowledge of pre-trained (teacher) diffusion models into smaller student diffusion models via an approximate score matching objective. For classifierfree guided generation on CIFAR-10, our student model achieves a FID-5K of 8.03 using 273G flops. In comparison, the larger teacher model only achieves a FID-5K of 294 using 424G flops. We present initial experiments on distilling the knowledge of Stable Diffusion, a large scale text-to-image diffusion model, and discuss several promising future directions.},
	language = {en},
	author = {Dockhorn, Tim and Rombach, Robin and Blatmann, Andreas and Yu, Yaoliang},
}

@misc{chamberlain_grand_2021,
	title = {{GRAND}: {Graph} {Neural} {Diffusion}},
	shorttitle = {{GRAND}},
	url = {http://arxiv.org/abs/2106.10934},
	doi = {10.48550/arXiv.2106.10934},
	abstract = {We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an underlying PDE. In our model, the layer structure and topology correspond to the discretisation choices of temporal and spatial operators. Our approach allows a principled development of a broad new class of GNNs that are able to address the common plights of graph learning models such as depth, oversmoothing, and bottlenecks. Key to the success of our models are stability with respect to perturbations in the data and this is addressed for both implicit and explicit discretisation schemes. We develop linear and nonlinear versions of GRAND, which achieve competitive results on many standard graph benchmarks.},
	urldate = {2024-01-28},
	publisher = {arXiv},
	author = {Chamberlain, Benjamin Paul and Rowbottom, James and Gorinova, Maria and Webb, Stefan and Rossi, Emanuele and Bronstein, Michael M.},
	month = sep,
	year = {2021},
	note = {arXiv:2106.10934 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{gao_e3_2023,
	title = {E3 {TTS}: {Easy} {End}-to-{End} {Diffusion}-based {Text} to {Speech}},
	shorttitle = {E3 {TTS}},
	url = {http://arxiv.org/abs/2311.00945},
	doi = {10.48550/arXiv.2311.00945},
	abstract = {We propose Easy End-to-End Diffusion-based Text to Speech, a simple and efficient end-to-end text-to-speech model based on diffusion. E3 TTS directly takes plain text as input and generates an audio waveform through an iterative refinement process. Unlike many prior work, E3 TTS does not rely on any intermediate representations like spectrogram features or alignment information. Instead, E3 TTS models the temporal structure of the waveform through the diffusion process. Without relying on additional conditioning information, E3 TTS could support flexible latent structure within the given audio. This enables E3 TTS to be easily adapted for zero-shot tasks such as editing without any additional training. Experiments show that E3 TTS can generate high-fidelity audio, approaching the performance of a state-of-the-art neural TTS system. Audio samples are available at https://e3tts.github.io.},
	urldate = {2024-01-28},
	publisher = {arXiv},
	author = {Gao, Yuan and Morioka, Nobuyuki and Zhang, Yu and Chen, Nanxin},
	month = nov,
	year = {2023},
	note = {arXiv:2311.00945 [cs, eess]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{noauthor_how_nodate,
	title = {How to get additional storage space for your {Zotero} library? - {Save} {Time}, {Ask} a {Librarian}},
	url = {https://graduateinstitute.libanswers.com/faq/267439},
	urldate = {2024-01-27},
}

@article{costanza-chock_design_2018,
	title = {Design {Justice}, {A}.{I}., and {Escape} from the {Matrix} of {Domination}},
	url = {https://jods.mitpress.mit.edu/pub/costanza-chock/release/4},
	doi = {10.21428/96c8d426},
	abstract = {Essay Competition Winner},
	language = {en},
	urldate = {2024-01-27},
	journal = {Journal of Design and Science},
	author = {Costanza-Chock, Sasha},
	month = jul,
	year = {2018},
}

@article{costanza-chock_design_2018,
	title = {Design {Justice}, {A}.{I}., and {Escape} from the {Matrix} of {Domination}},
	url = {https://jods.mitpress.mit.edu/pub/costanza-chock/release/4},
	doi = {10.21428/96c8d426},
	abstract = {Essay Competition Winner},
	urldate = {2024-01-26},
	journal = {Journal of Design and Science},
	author = {Costanza-Chock, Sasha},
	month = jul,
	year = {2018},
}

@article{kettunen_energizing_2022,
	title = {Energizing collaborative industry-academia learning: a present case and future visions},
	volume = {10},
	issn = {2195-2248},
	shorttitle = {Energizing collaborative industry-academia learning},
	url = {https://doi.org/10.1186/s40309-022-00196-5},
	doi = {10.1186/s40309-022-00196-5},
	abstract = {In Industry-Academia Collaborations (IAC) both academic, scientific research results and industrial practitioner findings and experiences are produced. Both types of knowledge should be gathered, codified, and disseminated efficiently and effectively. This paper investigates a recent (2014–2017) large-scale IAC R\&D\&I program case (Need for Speed, N4S) from a learning perspective. It was one of the programs in the Finnish SHOK (Strategic Centres of Science, Technology, and Innovation) system. The theoretical bases are in innovation management, knowledge management, and higher education (university) pedagogy. In the future, IAC projects should be more and more commonplace since major innovations are hardly ever done in isolation, not even by the largest companies. Both intra-organizational and inter-organizational learning networks are increasingly critical success factors. Collaborative learning capabilities will thus be required more often from all the participating parties. Efficient and effective knowledge creation and sharing are underpinning future core competencies. In this paper, we present and evaluate a collaboratively created and publicly shared digital knowledge repository called “Treasure Chest” produced during our case program. The starting point was a jointly created Strategic Research and Innovation Agenda (SRIA), which defined the main research themes and listed motivating research questions to begin with—i.e., intended learning outcomes (ILO). During the 4-year program, our collaborative industry-academia (I-A) learning process produced a range of theoretical and empirical results, which were iteratively collected and packaged into the Treasure Chest repository. Outstandingly, it contained, in addition to traditional research documents, narratives of the industrial learning experiences and more than 100 actionable knowledge items. In conclusion, our vision of the future is that such transparently shared, ambitious, and versatile outcome goals with a continuous integrative collection of the results are keys to effective networked I-A collaboration and learning. In that way, the N4S largely avoided the general problem of often conflicting motives between industrial firms seeking answers and applied solutions to their immediate practical problems and academic researchers aiming at more generalizable knowledge creation and high-quality scientific publications.},
	number = {1},
	urldate = {2024-01-26},
	journal = {European Journal of Futures Research},
	author = {Kettunen, Petri and Järvinen, Janne and Mikkonen, Tommi and Männistö, Tomi},
	month = apr,
	year = {2022},
	keywords = {Industry-Academia Collaboration, Innovation ecosystems, Learning networks},
	pages = {8},
}

@misc{noauthor_difference_nodate,
	title = {The difference in scientific research in academia vs. industry},
	url = {https://www.colabra.ai/blog/difference-in-scientific-research-in-academia-vs-industry/},
	abstract = {This piece discusses why researchers might want to transition from academia to industry and how research in industry differs from academia.},
	language = {en},
	urldate = {2024-01-26},
}

@article{vargas_solving_2021,
	title = {Solving {Schr}{\textbackslash}"odinger {Bridges} via {Maximum} {Likelihood}},
	volume = {23},
	issn = {1099-4300},
	url = {http://arxiv.org/abs/2106.02081},
	doi = {10.3390/e23091134},
	abstract = {The Schr{\textbackslash}"odinger bridge problem (SBP) finds the most likely stochastic evolution between two probability distributions given a prior stochastic evolution. As well as applications in the natural sciences, problems of this kind have important applications in machine learning such as dataset alignment and hypothesis testing. Whilst the theory behind this problem is relatively mature, scalable numerical recipes to estimate the Schr{\textbackslash}"odinger bridge remain an active area of research. We prove an equivalence between the SBP and maximum likelihood estimation enabling direct application of successful machine learning techniques. We propose a numerical procedure to estimate SBPs using Gaussian process and demonstrate the practical usage of our approach in numerical simulations and experiments.},
	number = {9},
	urldate = {2024-01-25},
	journal = {Entropy},
	author = {Vargas, Francisco and Thodoroff, Pierre and Lawrence, Neil D. and Lamacraft, Austen},
	month = aug,
	year = {2021},
	note = {arXiv:2106.02081 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	pages = {1134},
}

@inproceedings{vargas_denoising_2022,
	title = {Denoising {Diffusion} {Samplers}},
	url = {https://openreview.net/forum?id=8pvnfTAbu1f},
	abstract = {Denoising diffusion models are a popular class of generative models providing state-of-the-art results in many domains. One adds gradually noise to data using a diffusion to transform the data distribution into a Gaussian distribution. Samples from the generative model are then obtained by simulating an approximation of the time-reversal of this diffusion initialized by Gaussian samples. Practically, the intractable score terms appearing in the time-reversed process are approximated using score matching techniques. We explore here a similar idea to sample approximately from unnormalized probability density functions and estimate their normalizing constants. We consider a process where the target density diffuses towards a Gaussian. Denoising Diffusion Samplers (DDS) are obtained by approximating the corresponding time-reversal. While score matching is not applicable in this context, we can leverage many of the ideas introduced in generative modeling for Monte Carlo sampling. Existing theoretical results from denoising diffusion models also provide theoretical guarantees for DDS. We discuss the connections between DDS, optimal control and Schr{\textbackslash}"odinger bridges and finally demonstrate DDS experimentally on a variety of challenging sampling tasks.},
	language = {en},
	urldate = {2024-01-25},
	author = {Vargas, Francisco and Grathwohl, Will Sussman and Doucet, Arnaud},
	month = sep,
	year = {2022},
}

@misc{ye_first_2022,
	title = {First {Hitting} {Diffusion} {Models} for {Generating} {Manifold}, {Graph} and {Categorical} {Data}},
	url = {http://arxiv.org/abs/2209.01170},
	doi = {10.48550/arXiv.2209.01170},
	abstract = {We propose a family of First Hitting Diffusion Models (FHDM), deep generative models that generate data with a diffusion process that terminates at a random first hitting time. This yields an extension of the standard fixed-time diffusion models that terminate at a pre-specified deterministic time. Although standard diffusion models are designed for continuous unconstrained data, FHDM is naturally designed to learn distributions on continuous as well as a range of discrete and structure domains. Moreover, FHDM enables instance-dependent terminate time and accelerates the diffusion process to sample higher quality data with fewer diffusion steps. Technically, we train FHDM by maximum likelihood estimation on diffusion trajectories augmented from observed data with conditional first hitting processes (i.e., bridge) derived based on Doob's \$h\$-transform, deviating from the commonly used time-reversal mechanism. We apply FHDM to generate data in various domains such as point cloud (general continuous distribution), climate and geographical events on earth (continuous distribution on the sphere), unweighted graphs (distribution of binary matrices), and segmentation maps of 2D images (high-dimensional categorical distribution). We observe considerable improvement compared with the state-of-the-art approaches in both quality and speed.},
	urldate = {2024-01-25},
	publisher = {arXiv},
	author = {Ye, Mao and Wu, Lemeng and Liu, Qiang},
	month = oct,
	year = {2022},
	note = {arXiv:2209.01170 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@misc{tong_improving_2023,
	title = {Improving and generalizing flow-based generative models with minibatch optimal transport},
	url = {http://arxiv.org/abs/2302.00482},
	doi = {10.48550/arXiv.2302.00482},
	abstract = {Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but they have been held back by limitations in their simulation-based maximum likelihood training. We introduce the generalized conditional flow matching (CFM) technique, a family of simulation-free training objectives for CNFs. CFM features a stable regression objective like that used to train the stochastic flow in diffusion models but enjoys the efficient inference of deterministic flow models. In contrast to both diffusion models and prior CNF training algorithms, CFM does not require the source distribution to be Gaussian or require evaluation of its density. A variant of our objective is optimal transport CFM (OT-CFM), which creates simpler flows that are more stable to train and lead to faster inference, as evaluated in our experiments. Furthermore, OT-CFM is the first method to compute dynamic OT in a simulation-free way. Training CNFs with CFM improves results on a variety of conditional and unconditional generation tasks, such as inferring single cell dynamics, unsupervised image translation, and Schr{\textbackslash}"odinger bridge inference.},
	urldate = {2024-01-25},
	publisher = {arXiv},
	author = {Tong, Alexander and Malkin, Nikolay and Huguet, Guillaume and Zhang, Yanlei and Rector-Brooks, Jarrid and Fatras, Kilian and Wolf, Guy and Bengio, Yoshua},
	month = oct,
	year = {2023},
	note = {arXiv:2302.00482 [cs]},
	keywords = {Computer Science - Machine Learning},
}

@misc{didi_framework_2023,
	title = {A framework for conditional diffusion modelling with applications in motif scaffolding for protein design},
	url = {http://arxiv.org/abs/2312.09236},
	doi = {10.48550/arXiv.2312.09236},
	abstract = {Many protein design applications, such as binder or enzyme design, require scaffolding a structural motif with high precision. Generative modelling paradigms based on denoising diffusion processes emerged as a leading candidate to address this motif scaffolding problem and have shown early experimental success in some cases. In the diffusion paradigm, motif scaffolding is treated as a conditional generation task, and several conditional generation protocols were proposed or imported from the Computer Vision literature. However, most of these protocols are motivated heuristically, e.g. via analogies to Langevin dynamics, and lack a unifying framework, obscuring connections between the different approaches. In this work, we unify conditional training and conditional sampling procedures under one common framework based on the mathematically well-understood Doob's h-transform. This new perspective allows us to draw connections between existing methods and propose a new variation on existing conditional training protocols. We illustrate the effectiveness of this new protocol in both, image outpainting and motif scaffolding and find that it outperforms standard methods.},
	urldate = {2024-01-25},
	publisher = {arXiv},
	author = {Didi, Kieran and Vargas, Francisco and Mathis, Simon V. and Dutordoir, Vincent and Mathieu, Emile and Komorowska, Urszula J. and Lio, Pietro},
	month = dec,
	year = {2023},
	note = {arXiv:2312.09236 [cs, q-bio]},
	keywords = {Computer Science - Machine Learning, Quantitative Biology - Biomolecules},
}

@misc{heng_simulating_2022,
	title = {Simulating {Diffusion} {Bridges} with {Score} {Matching}},
	url = {http://arxiv.org/abs/2111.07243},
	doi = {10.48550/arXiv.2111.07243},
	abstract = {We consider the problem of simulating diffusion bridges, which are diffusion processes that are conditioned to initialize and terminate at two given states. The simulation of diffusion bridges has applications in diverse scientific fields and plays a crucial role in the statistical inference of discretely-observed diffusions. This is known to be a challenging problem that has received much attention in the last two decades. This article contributes to this rich body of literature by presenting a new avenue to obtain diffusion bridge approximations. Our approach is based on a backward time representation of a diffusion bridge, which may be simulated if one can time-reverse the unconditioned diffusion. We introduce a variational formulation to learn this time-reversal with function approximation and rely on a score matching method to circumvent intractability. Another iteration of our proposed methodology approximates the Doob's \$h\$-transform defining the forward time representation of a diffusion bridge. We discuss algorithmic considerations and extensions, and present numerical results on an Ornstein--Uhlenbeck process, a model from financial econometrics for interest rates, and a model from genetics for cell differentiation and development to illustrate the effectiveness of our approach.},
	urldate = {2024-01-25},
	publisher = {arXiv},
	author = {Heng, Jeremy and De Bortoli, Valentin and Doucet, Arnaud and Thornton, James},
	month = oct,
	year = {2022},
	note = {arXiv:2111.07243 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Computation, Statistics - Machine Learning},
}

@misc{song_score-based_2021,
	title = {Score-{Based} {Generative} {Modeling} through {Stochastic} {Differential} {Equations}},
	url = {http://arxiv.org/abs/2011.13456},
	doi = {10.48550/arXiv.2011.13456},
	abstract = {Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field ({\textbackslash}aka, score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.},
	urldate = {2024-01-25},
	publisher = {arXiv},
	author = {Song, Yang and Sohl-Dickstein, Jascha and Kingma, Diederik P. and Kumar, Abhishek and Ermon, Stefano and Poole, Ben},
	month = feb,
	year = {2021},
	note = {arXiv:2011.13456 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{ho_denoising_2020,
	title = {Denoising {Diffusion} {Probabilistic} {Models}},
	url = {http://arxiv.org/abs/2006.11239},
	abstract = {We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at https://github.com/hojonathanho/diffusion},
	urldate = {2024-01-25},
	publisher = {arXiv},
	author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
	month = dec,
	year = {2020},
	note = {arXiv:2006.11239 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@article{ngiam_multimodal_nodate,
	title = {Multimodal {Deep} {Learning}},
	abstract = {Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train deep networks that learn features to address these tasks. In particular, we demonstrate cross modality feature learning, where better features for one modality (e.g., video) can be learned if multiple modalities (e.g., audio and video) are present at feature learning time. Furthermore, we show how to learn a shared representation between modalities and evaluate it on a unique task, where the classiﬁer is trained with audio-only data but tested with video-only data and vice-versa. Our models are validated on the CUAVE and AVLetters datasets on audio-visual speech classiﬁcation, demonstrating best published visual speech classiﬁcation on AVLetters and eﬀective shared representation learning.},
	language = {en},
	author = {Ngiam, Jiquan and Khosla, Aditya and Kim, Mingyu and Nam, Juhan and Lee, Honglak and Ng, Andrew Y},
}

@misc{aleksa_gordic_-_the_ai_epiphany_openai_2021,
	title = {{OpenAI} {GLIDE}: {Towards} {Photorealistic} {Image} {Generation} and {Editing} with {Text}-{Guided} {Diffusion} {Models}},
	shorttitle = {{OpenAI} {GLIDE}},
	url = {https://www.youtube.com/watch?v=lvv4N2nf-HU},
	abstract = {❤️ Become The AI Epiphany Patreon ❤️
  / theaiepiphany  

��‍��‍��‍�� Join our Discord community ��‍��‍��‍��
  / discord  

In this video I cover a new paper from OpenAI - "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models" where they combine diffusion models with transformers to outperform their older DALL-E model.

▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 
✅ GLIDE paper: https://arxiv.org/abs/2112.10741
✅ GLIDE code: https://github.com/openai/glide-text2im

Learning about diffusion models
Papers:
✅ Seminal (2015): https://arxiv.org/pdf/1503.03585.pdf
✅ DDPM (2020): https://arxiv.org/pdf/2006.11239.pdf
✅ OpenAI (1): https://arxiv.org/pdf/2102.09672.pdf
✅ OpenAI (2): https://arxiv.org/pdf/2105.05233.pdf

Blogs:
✅ Score-based models: https://yang-song.github.io/blog/2021...
✅ Diffusion models: https://lilianweng.github.io/lil-log/...
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 

⌚️ Timetable: 
00:00 Intro to GLIDE - results 
04:00 Intro to diffusion models
07:10 Inpainting and other awesome results
11:05 Diffusion models in depth
20:45 VAE inspired loss
31:30 GLIDE pipeline (diffusion + transformers)
34:15 Guided diffusion
38:00 Classifier-free guidance
42:25 CLIP guidance
45:25 Comparison with other models
48:30 Safety considerations
49:25 Failure cases
51:40 Outro

▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 
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\#glide \#openai \#diffusionmodels},
	urldate = {2024-01-22},
	author = {{Aleksa Gordić - The AI Epiphany}},
	month = dec,
	year = {2021},
}

@misc{machine_learning_at_berkeley_cs_2022,
	title = {{CS} 198-126: {Lecture} 12 - {Diffusion} {Models}},
	shorttitle = {{CS} 198-126},
	url = {https://www.youtube.com/watch?v=687zEGODmHA},
	abstract = {Lecture 12 - Diffusion Models
CS 198-126: Modern Computer Vision and Deep Learning
University of California, Berkeley

Please visit https://ml.berkeley.edu/decal/modern-cv to see more information about the course, including slides, assignments, lectures, and course information/background!

Instructors: Jake Austin, Arvind Rajaraman, Aryan Jain, Rohan Viswanathan, Ryan Alameddine, \& Verona Teo
Faculty Sponsor: Stuart Russell
Semester: Fall 2022},
	urldate = {2024-01-22},
	author = {{Machine Learning at Berkeley}},
	month = dec,
	year = {2022},
}

@misc{yannic_kilcher_ddpm_2021,
	title = {{DDPM} - {Diffusion} {Models} {Beat} {GANs} on {Image} {Synthesis} ({Machine} {Learning} {Research} {Paper} {Explained})},
	url = {https://www.youtube.com/watch?v=W-O7AZNzbzQ},
	abstract = {\#ddpm \#diffusionmodels \#openai

GANs have dominated the image generation space for the majority of the last decade. This paper shows for the first time, how a non-GAN model, a DDPM, can be improved to overtake GANs at standard evaluation metrics for image generation. The produced samples look amazing and other than GANs, the new model has a formal probabilistic foundation. Is there a future for GANs or are Diffusion Models going to overtake them for good?

OUTLINE:
0:00 - Intro \& Overview
4:10 - Denoising Diffusion Probabilistic Models
11:30 - Formal derivation of the training loss
23:00 - Training in practice
27:55 - Learning the covariance
31:25 - Improving the noise schedule
33:35 - Reducing the loss gradient noise
40:35 - Classifier guidance
52:50 - Experimental Results

Paper (this): https://arxiv.org/abs/2105.05233
Paper (previous): https://arxiv.org/abs/2102.09672
Code: https://github.com/openai/guided-diff...

Abstract:
We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for sample quality using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128×128, 4.59 on ImageNet 256×256, and 7.72 on ImageNet 512×512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.85 on ImageNet 512×512. We release our code at this https URL

Authors: Alex Nichol, Prafulla Dhariwal

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	urldate = {2024-01-22},
	author = {{Yannic Kilcher}},
	month = may,
	year = {2021},
}

@misc{song_score-based_2021-1,
	title = {Score-{Based} {Generative} {Modeling} through {Stochastic} {Differential} {Equations}},
	url = {http://arxiv.org/abs/2011.13456},
	doi = {10.48550/arXiv.2011.13456},
	abstract = {Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field ({\textbackslash}aka, score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.},
	urldate = {2024-01-22},
	publisher = {arXiv},
	author = {Song, Yang and Sohl-Dickstein, Jascha and Kingma, Diederik P. and Kumar, Abhishek and Ermon, Stefano and Poole, Ben},
	month = feb,
	year = {2021},
	note = {arXiv:2011.13456 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{nichol_glide_2022,
	title = {{GLIDE}: {Towards} {Photorealistic} {Image} {Generation} and {Editing} with {Text}-{Guided} {Diffusion} {Models}},
	shorttitle = {{GLIDE}},
	url = {http://arxiv.org/abs/2112.10741},
	doi = {10.48550/arXiv.2112.10741},
	abstract = {Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing. We train a smaller model on a filtered dataset and release the code and weights at https://github.com/openai/glide-text2im.},
	urldate = {2024-01-22},
	publisher = {arXiv},
	author = {Nichol, Alex and Dhariwal, Prafulla and Ramesh, Aditya and Shyam, Pranav and Mishkin, Pamela and McGrew, Bob and Sutskever, Ilya and Chen, Mark},
	month = mar,
	year = {2022},
	note = {arXiv:2112.10741 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics, Computer Science - Machine Learning},
}

@misc{luo_understanding_2022,
	title = {Understanding {Diffusion} {Models}: {A} {Unified} {Perspective}},
	shorttitle = {Understanding {Diffusion} {Models}},
	url = {http://arxiv.org/abs/2208.11970},
	doi = {10.48550/arXiv.2208.11970},
	abstract = {Diffusion models have shown incredible capabilities as generative models; indeed, they power the current state-of-the-art models on text-conditioned image generation such as Imagen and DALL-E 2. In this work we review, demystify, and unify the understanding of diffusion models across both variational and score-based perspectives. We first derive Variational Diffusion Models (VDM) as a special case of a Markovian Hierarchical Variational Autoencoder, where three key assumptions enable tractable computation and scalable optimization of the ELBO. We then prove that optimizing a VDM boils down to learning a neural network to predict one of three potential objectives: the original source input from any arbitrary noisification of it, the original source noise from any arbitrarily noisified input, or the score function of a noisified input at any arbitrary noise level. We then dive deeper into what it means to learn the score function, and connect the variational perspective of a diffusion model explicitly with the Score-based Generative Modeling perspective through Tweedie's Formula. Lastly, we cover how to learn a conditional distribution using diffusion models via guidance.},
	urldate = {2024-01-22},
	publisher = {arXiv},
	author = {Luo, Calvin},
	month = aug,
	year = {2022},
	note = {arXiv:2208.11970 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@misc{ho_classifier-free_2022,
	title = {Classifier-{Free} {Diffusion} {Guidance}},
	url = {http://arxiv.org/abs/2207.12598},
	doi = {10.48550/arXiv.2207.12598},
	abstract = {Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models. Classifier guidance combines the score estimate of a diffusion model with the gradient of an image classifier and thereby requires training an image classifier separate from the diffusion model. It also raises the question of whether guidance can be performed without a classifier. We show that guidance can be indeed performed by a pure generative model without such a classifier: in what we call classifier-free guidance, we jointly train a conditional and an unconditional diffusion model, and we combine the resulting conditional and unconditional score estimates to attain a trade-off between sample quality and diversity similar to that obtained using classifier guidance.},
	urldate = {2024-01-22},
	publisher = {arXiv},
	author = {Ho, Jonathan and Salimans, Tim},
	month = jul,
	year = {2022},
	note = {arXiv:2207.12598 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
}

@inproceedings{song_loss-guided_2023,
	title = {Loss-{Guided} {Diffusion} {Models} for {Plug}-and-{Play} {Controllable} {Generation}},
	url = {https://proceedings.mlr.press/v202/song23k.html},
	abstract = {We consider guiding denoising diffusion models with general differentiable loss functions in a plug-and-play fashion, enabling controllable generation without additional training. This paradigm, termed Loss-Guided Diffusion (LGD), can easily be integrated into all diffusion models and leverage various efficient samplers. Despite the benefits, the resulting guidance term is, unfortunately, an intractable integral and needs to be approximated. Existing methods compute the guidance term based on a point estimate. However, we show that such approaches have significant errors over the scale of the approximations. To address this issue, we propose a Monte Carlo method that uses multiple samples from a suitable distribution to reduce bias. Our method is effective in various synthetic and real-world settings, including image super-resolution, text or label-conditional image generation, and controllable motion synthesis. Notably, we show how our method can be applied to control a pretrained motion diffusion model to follow certain paths and avoid obstacles that are proven challenging to prior methods.},
	language = {en},
	urldate = {2024-01-22},
	booktitle = {Proceedings of the 40th {International} {Conference} on {Machine} {Learning}},
	publisher = {PMLR},
	author = {Song, Jiaming and Zhang, Qinsheng and Yin, Hongxu and Mardani, Morteza and Liu, Ming-Yu and Kautz, Jan and Chen, Yongxin and Vahdat, Arash},
	month = jul,
	year = {2023},
	note = {ISSN: 2640-3498},
	pages = {32483--32498},
}

@misc{dhariwal_diffusion_2021,
	title = {Diffusion {Models} {Beat} {GANs} on {Image} {Synthesis}},
	url = {http://arxiv.org/abs/2105.05233},
	doi = {10.48550/arXiv.2105.05233},
	abstract = {We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for fidelity using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128\${\textbackslash}times\$128, 4.59 on ImageNet 256\${\textbackslash}times\$256, and 7.72 on ImageNet 512\${\textbackslash}times\$512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.94 on ImageNet 256\${\textbackslash}times\$256 and 3.85 on ImageNet 512\${\textbackslash}times\$512. We release our code at https://github.com/openai/guided-diffusion},
	urldate = {2024-01-22},
	publisher = {arXiv},
	author = {Dhariwal, Prafulla and Nichol, Alex},
	month = jun,
	year = {2021},
	note = {arXiv:2105.05233 [cs, stat]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{nichol_improved_2021,
	title = {Improved {Denoising} {Diffusion} {Probabilistic} {Models}},
	url = {http://arxiv.org/abs/2102.09672},
	doi = {10.48550/arXiv.2102.09672},
	abstract = {Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality, which is important for the practical deployment of these models. We additionally use precision and recall to compare how well DDPMs and GANs cover the target distribution. Finally, we show that the sample quality and likelihood of these models scale smoothly with model capacity and training compute, making them easily scalable. We release our code at https://github.com/openai/improved-diffusion},
	urldate = {2024-01-22},
	publisher = {arXiv},
	author = {Nichol, Alex and Dhariwal, Prafulla},
	month = feb,
	year = {2021},
	note = {arXiv:2102.09672 [cs, stat]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
}

@inproceedings{sarkar_should_2023,
	address = {Hamburg Germany},
	title = {Should {Computers} {Be} {Easy} {To} {Use}? {Questioning} the {Doctrine} of {Simplicity} in {User} {Interface} {Design}},
	isbn = {978-1-4503-9422-2},
	shorttitle = {Should {Computers} {Be} {Easy} {To} {Use}?},
	url = {https://dl.acm.org/doi/10.1145/3544549.3582741},
	doi = {10.1145/3544549.3582741},
	abstract = {That computers should be easy to learn and use is a rarely-questioned tenet of user interface design. But what do we gain from prioritising usability and learnability, and what do we lose? I explore how simplicity is not an inevitable truth of user interface design, but rather contingent on a series of events in the evolution of software. Not only does a rigid adherence to this doctrine place an artificial ceiling on the power and flexibility of software, but it is also culturally relative, privileging certain information cultures over others. I propose that for feature-rich software, negotiated complexity is a better target than simplicity, and we must revisit the ill-regarded relationship between learning, documentation, and software.},
	language = {en},
	urldate = {2024-01-22},
	booktitle = {Extended {Abstracts} of the 2023 {CHI} {Conference} on {Human} {Factors} in {Computing} {Systems}},
	publisher = {ACM},
	author = {Sarkar, Advait},
	month = apr,
	year = {2023},
	pages = {1--10},
}

@misc{noauthor_human-computer_nodate,
	title = {Human-{Computer} {Insurrection} {\textbar} {Proceedings} of the 2019 {CHI} {Conference} on {Human} {Factors} in {Computing} {Systems}},
	url = {https://dl.acm.org/doi/10.1145/3290605.3300569},
	urldate = {2024-01-22},
}

@article{rossi_can_2020,
	title = {Can {Visual} {Design} {Provide} {Legal} {Transparency}? {The} {Challenges} for {Successful} {Implementation} of {Icons} for {Data} {Protection}},
	volume = {36},
	issn = {0747-9360},
	shorttitle = {Can {Visual} {Design} {Provide} {Legal} {Transparency}?},
	url = {https://doi.org/10.1162/desi_a_00605},
	doi = {10.1162/desi_a_00605},
	abstract = {Design is a key player in the future of data privacy and data protection. The General Data Protection Regulation (GDPR) established by the European Union aims to rebalance the information asymmetry between the organizations that process personal data and the individuals to which that data refers. Machine-readable, standardized icons that present a “meaningful overview of the intended processing” are suggested by the law as a tool to enhance the transparency of information addressed to data subjects. However, no specific guidelines have been provided, and studies on privacy iconography are very few. This article describes research conducted on the creation and evaluation of icons representing data protection concepts. First, we introduce the methodology used to design the Data Protection Icon Set (DaPIS): participatory design methods combined with legal ontologies and machine-readable representations. Second, we discuss some of the challenges that have been faced in the development and evaluation of DaPIS and similar icon sets. Third, we provide some tentative responses and indicate a way forward for evaluation of the effectiveness of privacy icons and their widespread adoption.},
	number = {3},
	urldate = {2024-01-22},
	journal = {Design Issues},
	author = {Rossi, Arianna and Palmirani, Monica},
	month = jun,
	year = {2020},
	pages = {82--96},
}

@article{andersen_002_nodate,
	title = {002: {Aesthetics} of {New} {AI} {Interfaces}},
	language = {en},
	author = {Andersen, Christian Ulrik and Pold, Søren and Cameron, Agnes},
}

@inproceedings{schnabel_impact_2020,
	address = {New York, NY, USA},
	series = {{SIGIR} '20},
	title = {The {Impact} of {More} {Transparent} {Interfaces} on {Behavior} in {Personalized} {Recommendation}},
	isbn = {978-1-4503-8016-4},
	url = {https://dl.acm.org/doi/10.1145/3397271.3401117},
	doi = {10.1145/3397271.3401117},
	abstract = {Many interactive online systems, such as social media platforms or news sites, provide personalized experiences through recommendations or news feed customization based on people's feedback and engagement on individual items (e.g., liking items). In this paper, we investigate how we can support a greater degree of user control in such systems by changing the way the system allows people to gauge the consequences of their feedback actions. To this end, we consider two important aspects of how the system responds to feedback actions: (i) immediacy, i.e., how quickly the system responds with an update, and (ii) visibility, i.e., whether or not changes will get highlighted. We used both an in-lab qualitative study and a large-scale crowd-sourced study to examine the impact of these factors on people's reported preferences and observed behavioral metrics. We demonstrate that UX design which enables people to preview the impact of their actions and highlights changes results in a higher reported transparency, an overall preference for this design, and a greater selectivity in which items are liked.},
	urldate = {2024-01-22},
	booktitle = {Proceedings of the 43rd {International} {ACM} {SIGIR} {Conference} on {Research} and {Development} in {Information} {Retrieval}},
	publisher = {Association for Computing Machinery},
	author = {Schnabel, Tobias and Amershi, Saleema and Bennett, Paul N. and Bailey, Peter and Joachims, Thorsten},
	month = jul,
	year = {2020},
	keywords = {control settings, human-in-the-loop systems, machine learning, personalization, user engagement},
	pages = {991--1000},
}

@article{seaver_care_2021,
	title = {Care and {Scale}: {Decorrelative} {Ethics} in {Algorithmic} {Recommendation}},
	volume = {36},
	copyright = {Copyright (c) 2021 American Anthropological Association},
	issn = {1548-1360},
	shorttitle = {Care and {Scale}},
	url = {https://journal.culanth.org/index.php/ca/article/view/4807},
	doi = {10.14506/ca36.3.11},
	abstract = {The people who make algorithmic recommender systems want apparently incompatible things: they pride themselves on the scale at which their software works, but they also want to treat their materials and users with care. Care and scale are commonly understood as contradictory goals: to be careful is to work at small scale, while working at large scale requires abandoning the small concerns of care. Drawing together anthropological work on care and scale, this article analyzes how people who make music recommender systems try to reconcile these values, reimagining what care and scale mean and how they relate to each other in the process. It describes decorrelation, an ethical technique that metaphorically borrows from the mathematics of machine learning, which practitioners use to reimagine how values might relate with each other. This “decorrelative ethics” facilitates new arrangements of care and scale, which challenge conventional anthropological theorizing.},
	language = {en},
	number = {3},
	urldate = {2024-01-22},
	journal = {Cultural Anthropology},
	author = {Seaver, Nick},
	month = aug,
	year = {2021},
	note = {Number: 3},
	keywords = {music},
	pages = {509--537},
}

@misc{noauthor_-depth_nodate,
	title = {In-depth {EU} {AI} {Toolkit}},
	url = {http://lcfi.ac.uk/projects/ai-innovation-praxis/eu-ai-act-toolkit/},
	urldate = {2024-01-22},
}

@misc{floridi_capai_2022,
	address = {Rochester, NY},
	type = {{SSRN} {Scholarly} {Paper}},
	title = {{capAI} - {A} {Procedure} for {Conducting} {Conformity} {Assessment} of {AI} {Systems} in {Line} with the {EU} {Artificial} {Intelligence} {Act}},
	url = {https://papers.ssrn.com/abstract=4064091},
	doi = {10.2139/ssrn.4064091},
	abstract = {We have developed capAI, a conformity assessment procedure for AI systems, to provide an independent, comparable, quantifiable, and accountable assessment of AI systems that conforms with the proposed AIA regulation. By building on the AIA, capAI provides organisations with practical guidance on how high-level ethics principles can be translated into verifiable criteria that help shape the design, development, deployment and use of ethical AI. The main purpose of capAI is to serve as a governance tool that ensures and demonstrates that the development and operation of an AI system are trustworthy – i.e., legally compliant, ethically sound, and technically robust – and thus conform to the AIA.},
	language = {en},
	urldate = {2024-01-22},
	author = {Floridi, Luciano and Holweg, Matthias and Taddeo, Mariarosaria and Amaya Silva, Javier and Mökander, Jakob and Wen, Yuni},
	month = mar,
	year = {2022},
	keywords = {AI, AI Act, AI Auditing, AI legislation, Conformity Assessment, EU, Trustworthy AI},
}

@misc{noauthor_regulatory_2023,
	title = {Regulatory framework proposal on artificial intelligence {\textbar} {Shaping} {Europe}’s digital future},
	url = {https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai},
	language = {en},
	urldate = {2024-01-22},
	month = dec,
	year = {2023},
}

@misc{noauthor_mulching_nodate,
	title = {A {Mulching} {Proposal} {\textbar} {Extended} {Abstracts} of the 2019 {CHI} {Conference} on {Human} {Factors} in {Computing} {Systems}},
	url = {https://dl.acm.org/doi/10.1145/3290607.3310433},
	urldate = {2024-01-22},
}

@article{greene_better_nodate,
	title = {Better, {Nicer}, {Clearer}, {Fairer}: {A} {Critical} {Assessment} of the {Movement} for {Ethical} {Artificial} {Intelligence} and {Machine} {Learning}},
	abstract = {This paper uses frame analysis to examine recent high-profile values statements endorsing ethical design for artificial intelligence and machine learning (AI/ML). Guided by insights from values in design and the sociology of business ethics, we uncover the grounding assumptions and terms of debate that make some conversations about ethical design possible while forestalling alternative visions. Vision statements for ethical AI/ML co-opt the language of some critics, folding them into a limited, technologically deterministic, expert-driven view of what ethical AI/ML means and how it might work.},
	language = {en},
	author = {Greene, Daniel and Hoffmann, Anna Lauren and Stark, Luke},
}

@misc{noauthor_catalogue_nodate,
	title = {Catalogue of {Tools} \& {Metrics} for {Trustworthy} {AI}},
	url = {https://oecd.ai/en/catalogue},
	abstract = {An interactive collection of the latest tools and resources to help AI actors be accountable and ensure that AI systems and applications respect human rights and are fair, transparent, explainable, robust, secure and safe.},
	language = {en},
	urldate = {2024-01-22},
}

@article{wong_seeing_2023,
	title = {Seeing {Like} a {Toolkit}: {How} {Toolkits} {Envision} the {Work} of {AI} {Ethics}},
	volume = {7},
	shorttitle = {Seeing {Like} a {Toolkit}},
	url = {https://dl.acm.org/doi/10.1145/3579621},
	doi = {10.1145/3579621},
	abstract = {Numerous toolkits have been developed to support ethical AI development. However, toolkits, like all tools, encode assumptions in their design about what work should be done and how. In this paper, we conduct a qualitative analysis of 27 AI ethics toolkits to critically examine how the work of ethics is imagined and how it is supported by these toolkits. Specifically, we examine the discourses toolkits rely on when talking about ethical issues, who they imagine should do the work of ethics, and how they envision the work practices involved in addressing ethics. Among the toolkits, we identify a mismatch between the imagined work of ethics and the support the toolkits provide for doing that work. In particular, we identify a lack of guidance around how to navigate labor, organizational, and institutional power dynamics as they relate to performing ethical work. We use these omissions to chart future work for researchers and designers of AI ethics toolkits.},
	number = {CSCW1},
	urldate = {2024-01-22},
	journal = {Proceedings of the ACM on Human-Computer Interaction},
	author = {Wong, Richmond Y. and Madaio, Michael A. and Merrill, Nick},
	month = apr,
	year = {2023},
	keywords = {ethics, fairness, labor, toolkits, work},
	pages = {145:1--145:27},
}

@inproceedings{madaio_co-designing_2020,
	address = {New York, NY, USA},
	series = {{CHI} '20},
	title = {Co-{Designing} {Checklists} to {Understand} {Organizational} {Challenges} and {Opportunities} around {Fairness} in {AI}},
	isbn = {978-1-4503-6708-0},
	url = {https://dl.acm.org/doi/10.1145/3313831.3376445},
	doi = {10.1145/3313831.3376445},
	abstract = {Many organizations have published principles intended to guide the ethical development and deployment of AI systems; however, their abstract nature makes them difficult to operationalize. Some organizations have therefore produced AI ethics checklists, as well as checklists for more specific concepts, such as fairness, as applied to AI systems. But unless checklists are grounded in practitioners' needs, they may be misused. To understand the role of checklists in AI ethics, we conducted an iterative co-design process with 48 practitioners, focusing on fairness. We co-designed an AI fairness checklist and identified desiderata and concerns for AI fairness checklists in general. We found that AI fairness checklists could provide organizational infrastructure for formalizing ad-hoc processes and empowering individual advocates. We highlight aspects of organizational culture that may impact the efficacy of AI fairness checklists, and suggest future design directions.},
	urldate = {2024-01-22},
	booktitle = {Proceedings of the 2020 {CHI} {Conference} on {Human} {Factors} in {Computing} {Systems}},
	publisher = {Association for Computing Machinery},
	author = {Madaio, Michael A. and Stark, Luke and Wortman Vaughan, Jennifer and Wallach, Hanna},
	month = apr,
	year = {2020},
	keywords = {AI, ML, checklists, co-design, ethics, fairness},
	pages = {1--14},
}

@article{coulton_more-than_2019,
	title = {More-{Than} {Human} {Centred} {Design}: {Considering} {Other} {Things}},
	volume = {22},
	issn = {1460-6925},
	shorttitle = {More-{Than} {Human} {Centred} {Design}},
	url = {https://doi.org/10.1080/14606925.2019.1614320},
	doi = {10.1080/14606925.2019.1614320},
	abstract = {This paper responds to contemporary design contexts that frequently contain complex interdependencies of human and non-human actants. To adequately represent these perspectives requires a shift towards More-Than Human Centred Design. The Internet of Things (IoT) is one context that demonstrates this need. The ‘things’ within such networks transcend their physical forms and extend to include algorithms, humans, data, business models, etc. and each imports independent-but-interdependent motivations and perspectives. Therefore, we use the IoT to clarify our proposition and to convey our three contributions. First, we review the expanding corpus of contemporary Human–Computer Interaction research that seeks to expand the notion of Human Centred Design by moving beyond the dominant anthropocentric perspective. Second, we introduce a novel design metaphor, ‘constellations’, which allows both the interdependencies and independent perspectives to be considered. Third, we provide an account of a speculative design to demonstrate how it may be put into practice.},
	number = {4},
	urldate = {2024-01-22},
	journal = {The Design Journal},
	author = {Coulton, Paul and Lindley, Joseph Galen},
	month = jul,
	year = {2019},
	note = {Publisher: Routledge
\_eprint: https://doi.org/10.1080/14606925.2019.1614320},
	keywords = {Internet of Things (IoT), Object Oriented Ontology (OOO), design fiction, non-anthropocentric, speculative design},
	pages = {463--481},
}

@article{marenko_animistic_2016,
	title = {Animistic design: how to reimagine digital interaction between the human and the nonhuman},
	volume = {27},
	issn = {1462-6268},
	shorttitle = {Animistic design},
	url = {https://doi.org/10.1080/14626268.2016.1145127},
	doi = {10.1080/14626268.2016.1145127},
	abstract = {This article puts forward the notion of animistic design as an uncertainty-driven strategy to reimagine human–machine interaction as a milieu of human and nonhuman. Animistic design is suggested as capable of fostering affects, sensibilities and thoughts that capitalize on the uncertain, the unpredictable and the nonlinear, and their capacity to trigger creative pathways. Informed by post-human philosophies, theories of mediation and materiality, as well as by affect, agency and aesthesia, animistic design eschews the anthropomorphic and the cute playfulness often associated with animism. Instead, it proposes a practical–theoretical framework to articulate the nexus of digital innovation, interaction design practices, technical materialities and affective responses already emerging in the digital cohabitation of the human and the nonhuman. Using a ‘research through making’ approach, the article describes in detail a series of animistic design experiments and prototyping methods that explore ways of rethinking interaction as an open-ended and creative enterprise. Animistic design offers an investigative strategy that exploits degrees of collaboratively curated uncertainty and unpredictability to imagine forms of digital interaction, and to engender creative human–nonhuman relationships within a given digital milieu.},
	number = {1},
	urldate = {2024-01-22},
	journal = {Digital Creativity},
	author = {Marenko, Betti and van Allen, Philip},
	month = jan,
	year = {2016},
	note = {Publisher: Routledge
\_eprint: https://doi.org/10.1080/14626268.2016.1145127},
	keywords = {Animism, digital milieu, interaction design, nonhuman, uncertainty},
	pages = {52--70},
}

@misc{noauthor_interspecies_2019,
	title = {Interspecies {Toolkit}},
	url = {https://www.interspeciesdesign.co.uk/interspecies-toolkit/},
	abstract = {The Interspecies Design Toolkit is a Speculative Design Proposal designed to compliment and critique the Microsoft Inclusive Design Toolkit. The full toolkit can be downloaded from here (30MB). The…},
	language = {en-US},
	urldate = {2024-01-22},
	journal = {Interspecies Design},
	month = feb,
	year = {2019},
}

@misc{noauthor_microsoft_nodate,
	title = {Microsoft {Inclusive} {Design}},
	url = {https://inclusive.microsoft.design/},
	abstract = {Microsoft Inclusive Design is a practice that anyone who creates and manages products and services can use to build more inclusive experiences for everyone. Get the principles, guidebooks, workshop tools, videos, and many more free resources.},
	language = {en},
	urldate = {2024-01-22},
}

@article{forlano_posthumanism_2017,
	title = {Posthumanism and {Design}},
	volume = {3},
	issn = {2405-8726},
	url = {https://www.sciencedirect.com/science/article/pii/S2405872616300971},
	doi = {10.1016/j.sheji.2017.08.001},
	abstract = {Since at least the mid-1980s, design has been dominated by a human-centered and user-centered paradigm. Currently, the implications of technological and environmental transformations are challenging designers to focus on complex socio-technical systems. This article traces emergent discussions around posthumanism from across a range of disciplines and perspectives, and considers examples from emerging design practices that emphasize the interrelations between human and nonhuman actors. Specifically, this article reviews literature from actor-network theory (ANT), feminist new materialism, object-oriented ontology, non-representational theory, and transhumanism to inform the development of new methodologies and practices in the field of design. Finally, this article presents critiques of posthumanism from critical race theory and decolonial theory to consider how emergent design perspectives might better support values such as equality and justice for humans and nonhumans that have been traditionally ignored in design processes.},
	number = {1},
	urldate = {2024-01-22},
	journal = {She Ji: The Journal of Design, Economics, and Innovation},
	author = {Forlano, Laura},
	month = mar,
	year = {2017},
	keywords = {Nonhuman: Feminist new materialism: Science and technology studies: Socio-technical systems, Posthumanism},
	pages = {16--29},
}

@inproceedings{payne_how_2023,
	address = {New York, NY, USA},
	series = {{CSCW} '23 {Companion}},
	title = {How to {Ethically} {Engage} {Fat} {People} in {HCI} {Research}},
	isbn = {9798400701290},
	url = {https://dl.acm.org/doi/10.1145/3584931.3606987},
	doi = {10.1145/3584931.3606987},
	abstract = {How we research and represent people and communities that may be marginalized or vulnerable is a highly relevant question for our research practices. One particular group that has received some recent attention from HCI and CSCW researchers is fat people and, relatedly, anti-fatness. However, more work is needed to understand how to ethically engage in research with fat people–for example, in studying online communities or designing technology. Toward this goal, we provide a brief introduction to fat people, fat activism, and fat oppression so researchers may understand the social, political, and historical landscape in which research with and about fat people takes place. Based on empirical findings from an interview study, we then provide recommendations for researchers wishing to engage fat people in future research.},
	urldate = {2024-01-22},
	booktitle = {Companion {Publication} of the 2023 {Conference} on {Computer} {Supported} {Cooperative} {Work} and {Social} {Computing}},
	publisher = {Association for Computing Machinery},
	author = {Payne, Blakeley H. and Taylor, Jordan and Spiel, Katta and Fiesler, Casey},
	month = oct,
	year = {2023},
	keywords = {Fat, Fat Liberation, Fat People, Marginalized Communities, Research Ethics},
	pages = {117--121},
}

@incollection{king_te_aupouri_indigenous_2023,
	address = {Singapore},
	title = {Indigenous {Peoples}, {Whiteness}, and the {Coloniality} of {Co}-design},
	isbn = {978-981-19161-2-0},
	url = {https://doi.org/10.1007/978-981-19-1612-0_28-1},
	abstract = {The sustained inequities experienced by Indigenous peoples have been rigorously documented. Co-design has increasingly been posited as a way forward in the design of healthHealth interventions or services with Indigenous peoples and other groups that experience significant adverse health Healthand social inequities. However, the relatively rapid rise in co-design rhetoric within health Healthand disability settings has not necessarily been accompanied by an increased understanding of what co-design is. In addition, an ever-increasing enthusiasm for co-design as “the solution” has not equated with a growth in the evidence base around its effectiveness or appropriateness as an applied approach within Indigenous well-being contexts.},
	language = {en},
	urldate = {2024-01-22},
	booktitle = {Handbook of {Critical} {Whiteness}: {Deconstructing} {Dominant} {Discourses} {Across} {Disciplines}},
	publisher = {Springer Nature},
	author = {King (Te Aūpouri, Ngāpuhi, Ngāti Whātua, Waikato-Tainui, Ngāti Maniapoto), Paula Toko, Te Rarawa and Cormack (Kāi Tahu, Donna, Kāti Māmoe)},
	editor = {Ravulo, Jioji and Olcoń, Katarzyna and Dune, Tinashe and Workman, Alex and Liamputtong, Pranee},
	year = {2023},
	doi = {10.1007/978-981-19-1612-0_28-1},
	keywords = {Co-design, Coloniality, Colonization, Health, Indigenous peoples, Māori, Whiteness},
	pages = {1--16},
}

@misc{noauthor_collective_nodate,
	title = {Collective {Constitutional} {AI}: {Aligning} a {Language} {Model} with {Public} {Input}},
	shorttitle = {Collective {Constitutional} {AI}},
	url = {https://www.anthropic.com/news/collective-constitutional-ai-aligning-a-language-model-with-public-input},
	abstract = {Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.},
	language = {en},
	urldate = {2024-01-22},
}

@inproceedings{sloane_participation_2022,
	address = {New York, NY, USA},
	series = {{EAAMO} '22},
	title = {Participation {Is} not a {Design} {Fix} for {Machine} {Learning}},
	isbn = {978-1-4503-9477-2},
	url = {https://dl.acm.org/doi/10.1145/3551624.3555285},
	doi = {10.1145/3551624.3555285},
	abstract = {This paper critiques popular modes of participation in design practice and machine learning. It examines three existing kinds of participation in design practice and machine learning participation as work, participation as consultation, and as participation as justice – to argue that the machine learning community must become attuned to possibly exploitative and extractive forms of community involvement and shift away from the prerogatives of context independent scalability. Cautioning against “participation washing”, it argues that the notion of “participation” should be expanded to acknowledge more subtle, and possibly exploitative, forms of community involvement in participatory machine learning design. Specifically, it suggests that it is imperative to recognize design participation as work; to ensure that participation as consultation is context-specific; and that participation as justice must be genuine and long term. The paper argues that such a development can only be scaffolded by a new epistemology around design harms, including, but not limited to, in machine learning. To facilitate such a development, the paper suggests developing we argue that developing a cross-sectoral database of design participation failures that is cross-referenced with socio-structural dimensions and highlights “edge cases” that can and must be learned from.},
	urldate = {2024-01-22},
	booktitle = {Proceedings of the 2nd {ACM} {Conference} on {Equity} and {Access} in {Algorithms}, {Mechanisms}, and {Optimization}},
	publisher = {Association for Computing Machinery},
	author = {Sloane, Mona and Moss, Emanuel and Awomolo, Olaitan and Forlano, Laura},
	month = oct,
	year = {2022},
	keywords = {design, machine learning, participatory methods},
	pages = {1--6},
}

@misc{delgado_participatory_2023,
	title = {The {Participatory} {Turn} in {AI} {Design}: {Theoretical} {Foundations} and the {Current} {State} of {Practice}},
	shorttitle = {The {Participatory} {Turn} in {AI} {Design}},
	url = {http://arxiv.org/abs/2310.00907},
	doi = {10.48550/arXiv.2310.00907},
	abstract = {Despite the growing consensus that stakeholders affected by AI systems should participate in their design, enormous variation and implicit disagreements exist among current approaches. For researchers and practitioners who are interested in taking a participatory approach to AI design and development, it remains challenging to assess the extent to which any participatory approach grants substantive agency to stakeholders. This article thus aims to ground what we dub the "participatory turn" in AI design by synthesizing existing theoretical literature on participation and through empirical investigation and critique of its current practices. Specifically, we derive a conceptual framework through synthesis of literature across technology design, political theory, and the social sciences that researchers and practitioners can leverage to evaluate approaches to participation in AI design. Additionally, we articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners. We use these empirical findings to understand the current state of participatory practice and subsequently provide guidance to better align participatory goals and methods in a way that accounts for practical constraints.},
	urldate = {2024-01-22},
	publisher = {arXiv},
	author = {Delgado, Fernando and Yang, Stephen and Madaio, Michael and Yang, Qian},
	month = oct,
	year = {2023},
	note = {arXiv:2310.00907 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction},
}

@inproceedings{klumbyte_critical_2022,
	address = {New York, NY, USA},
	series = {{FAccT} '22},
	title = {Critical {Tools} for {Machine} {Learning}: {Working} with {Intersectional} {Critical} {Concepts} in {Machine} {Learning} {Systems} {Design}},
	isbn = {978-1-4503-9352-2},
	shorttitle = {Critical {Tools} for {Machine} {Learning}},
	url = {https://dl.acm.org/doi/10.1145/3531146.3533207},
	doi = {10.1145/3531146.3533207},
	abstract = {This paper investigates how intersectional critical theoretical concepts from social sciences and humanities research can be worked with in machine learning systems design. It does so by presenting a case study of a series of speculative design workshops, conducted in 2021. These workshops drew on intersectional feminist methodologies to construct interdisciplinary interventions in the design of machine learning systems, towards more inclusive, accountable, and contextualized systems design. The concepts of “situating/situated knowledges”, "figuration", "diffraction", and “critical fabulation/speculation” were taken up as theoretical and methodological tools for concept-led design workshops. This paper presents the design framework of the workshops and highlights tensions and possibilities with regards to interdisciplinary machine learning systems design towards more inclusive, contextualized, and accountable systems. It discusses the role that critical theoretical concepts can play in a design process and shows how such concepts can work as methodological tools that nonetheless require an open-ended experimental space to function. It presents insights and discussion points regarding what it means to work with critical intersectional knowledge that is inextricably connected to its historical and socio-political roots, and how this reframes what it might mean to design fair and accountable systems.},
	urldate = {2024-01-22},
	booktitle = {Proceedings of the 2022 {ACM} {Conference} on {Fairness}, {Accountability}, and {Transparency}},
	publisher = {Association for Computing Machinery},
	author = {Klumbytė, Goda and Draude, Claude and Taylor, Alex S.},
	month = jun,
	year = {2022},
	keywords = {Experimental practice, Feminist epistemologies, Interdisciplinary methodologies, Intersectionality, Machine learning systems design},
	pages = {1528--1541},
}

@inproceedings{bardzell_feminist_2010,
	address = {New York, NY, USA},
	series = {{CHI} '10},
	title = {Feminist {HCI}: taking stock and outlining an agenda for design},
	isbn = {978-1-60558-929-9},
	shorttitle = {Feminist {HCI}},
	url = {https://dl.acm.org/doi/10.1145/1753326.1753521},
	doi = {10.1145/1753326.1753521},
	abstract = {Feminism is a natural ally to interaction design, due to its central commitments to issues such as agency, fulfillment, identity, equity, empowerment, and social justice. In this paper, I summarize the state of the art of feminism in HCI and propose ways to build on existing successes to more robustly integrate feminism into interaction design research and practice. I explore the productive role of feminism in analogous fields, such as industrial design, architecture, and game design. I introduce examples of feminist interaction design already in the field. Finally, I propose a set of femi-nist interaction design qualities intended to support design and evaluation processes directly as they unfold.},
	urldate = {2024-01-22},
	booktitle = {Proceedings of the {SIGCHI} {Conference} on {Human} {Factors} in {Computing} {Systems}},
	publisher = {Association for Computing Machinery},
	author = {Bardzell, Shaowen},
	month = apr,
	year = {2010},
	keywords = {HCI, design, feminism, feminist HCI, feminist design qualities, feminist standpoint theory, gender, interaction design},
	pages = {1301--1310},
}

@article{noauthor_design_nodate,
	title = {Design {Justice}},
	url = {https://designjustice.mitpress.mit.edu/},
	abstract = {An exploration of how design might be led by marginalized communities, dismantle structural inequality, and advance collective liberation and ecological survival.},
	language = {en},
	urldate = {2024-01-22},
	journal = {Design Justice},
}

@misc{noauthor_oracle_nodate,
	title = {The {Oracle} for {Transfeminist} {Technologies}},
	url = {https://www.transfeministech.codingrights.org},
	abstract = {The Oracle for Transfeminist Technologies is a card game that help us envision and co-design feminist technologies for a better and more inclusive future.},
	language = {en},
	urldate = {2024-01-22},
	journal = {The Oracle for Transfeminist Technologies},
}

@misc{vram_intersectional_nodate,
	title = {Intersectional {Design} — {HOME}},
	url = {https://intersectionaldesign.com/},
	abstract = {Intersectional Design Cards 
These cards are designed to help teams explore and develop intersectional design solutions.  WHY? 

Navigate assumptions and biases 
Integrate intersectional thinking into your product
Course correct
Identify new markets and business opportunities},
	language = {en-US},
	urldate = {2024-01-22},
	journal = {Intersectional Design Cards},
	author = {{vram}},
}

@book{dunne_speculative_2013,
	address = {Cambridge},
	title = {Speculative {Everything}: {Design}, {Fiction}, and {Social} {Dreaming}},
	isbn = {978-0-262-31850-1},
	shorttitle = {Speculative {Everything}},
	url = {https://muse.jhu.edu/pub/6/monograph/book/28148},
	abstract = {Today designers often focus on making technology easy to use, sexy, and consumable. In  \&lt;I\&gt;Speculative Everything\&lt;/I\&gt;, Anthony Dunne and Fiona Raby propose a kind of design that is used as a tool to create not only things but ideas. For them, design is a means of speculating about how things could be -- to imagine possible futures. This is not the usual sort of predicting or forecasting, spotting trends and extrapolating; these kinds of predictions have been proven wrong, again and again. Instead, Dunne and Raby pose \&quot;what if\&quot; questions that are intended to open debate and discussion about the kind of future people want (and do not want). \&lt;I\&gt;Speculative Everything\&lt;/I\&gt; offers a tour through an emerging cultural landscape of design ideas, ideals, and approaches. Dunne and Raby cite examples from their own design and teaching and from other projects from fine art, design, architecture, cinema, and photography. They also draw on futurology, political theory, the philosophy of technology, and literary fiction. They show us, for example, ideas for a solar kitchen restaurant; a flypaper robotic clock; a menstruation machine; a cloud-seeding truck; a phantom-limb sensation recorder; and devices for food foraging that use the tools of synthetic biology. Dunne and Raby contend that if we speculate more -- about everything -- reality will become more malleable. The ideas freed by speculative design increase the odds of achieving desirable futures.},
	urldate = {2024-01-22},
	publisher = {The MIT Press},
	author = {Dunne, Anthony and Raby, Fiona},
	year = {2013},
}

@inproceedings{yang_re-examining_2020,
	address = {New York, NY, USA},
	series = {{CHI} '20},
	title = {Re-examining {Whether}, {Why}, and {How} {Human}-{AI} {Interaction} {Is} {Uniquely} {Difficult} to {Design}},
	isbn = {978-1-4503-6708-0},
	url = {https://dl.acm.org/doi/10.1145/3313831.3376301},
	doi = {10.1145/3313831.3376301},
	abstract = {Artificial Intelligence (AI) plays an increasingly important role in improving HCI and user experience. Yet many challenges persist in designing and innovating valuable human-AI interactions. For example, AI systems can make unpredictable errors, and these errors damage UX and even lead to undesired societal impact. However, HCI routinely grapples with complex technologies and mitigates their unintended consequences. What makes AI different? What makes human-AI interaction appear particularly difficult to design? This paper investigates these questions. We synthesize prior research, our own design and research experience, and our observations when teaching human-AI interaction. We identify two sources of AI's distinctive design challenges: 1) uncertainty surrounding AI's capabilities, 2) AI's output complexity, spanning from simple to adaptive complex. We identify four levels of AI systems. On each level, designers encounter a different subset of the design challenges. We demonstrate how these findings reveal new insights for designers, researchers, and design tool makers in productively addressing the challenges of human-AI interaction going forward.},
	urldate = {2024-01-22},
	booktitle = {Proceedings of the 2020 {CHI} {Conference} on {Human} {Factors} in {Computing} {Systems}},
	publisher = {Association for Computing Machinery},
	author = {Yang, Qian and Steinfeld, Aaron and Rosé, Carolyn and Zimmerman, John},
	month = apr,
	year = {2020},
	keywords = {artificial intelligence, prototyping, sketching, user experience},
	pages = {1--13},
}

@article{xu_enabling_2023,
	title = {Enabling {Human}-{Centered} {AI}: {A} {New} {Junction} and {Shared} {Journey} {Between} {AI} and {HCI} {Communities}},
	volume = {30},
	issn = {1072-5520},
	shorttitle = {Enabling {Human}-{Centered} {AI}},
	url = {https://dl.acm.org/doi/10.1145/3571883},
	doi = {10.1145/3571883},
	number = {1},
	urldate = {2024-01-22},
	journal = {Interactions},
	author = {Xu, Wei and Dainoff, Marvin},
	month = jan,
	year = {2023},
	pages = {42--47},
}

@inproceedings{bardzell_what_2013,
	address = {New York, NY, USA},
	series = {{CHI} '13},
	title = {What is "critical" about critical design?},
	isbn = {978-1-4503-1899-0},
	url = {https://dl.acm.org/doi/10.1145/2470654.2466451},
	doi = {10.1145/2470654.2466451},
	abstract = {Critical design is a research through design methodology that foregrounds the ethics of design practice, reveals potentially hidden agendas and values, and explores alternative design values. While it seems to be a timely fit for today's socially, aesthetically, and ethically oriented approaches to HCI, its adoption seems surprisingly limited. We argue that its central concepts and methods are unclear and difficult to adopt. Rather than merely attempting to decode the intentions of its originators, Dunne and Raby, we instead turn to traditions of critical thought in the past 150 years to explore a range of critical ideas and their practical uses. We then suggest ways that these ideas and uses can be leveraged as practical resources for HCI researchers interested in critical design. We also offer readings of two designs, which are not billed as critical designs, but which we argue are critical using a broader formulation of the concept than the one found in the current literature.},
	urldate = {2024-01-22},
	booktitle = {Proceedings of the {SIGCHI} {Conference} on {Human} {Factors} in {Computing} {Systems}},
	publisher = {Association for Computing Machinery},
	author = {Bardzell, Jeffrey and Bardzell, Shaowen},
	month = apr,
	year = {2013},
	keywords = {critical design, critical theory, design methodology, hci},
	pages = {3297--3306},
}

@incollection{agre_toward_1998,
	title = {Toward a {Critical} {Technical} {Practice}: {Lessons} {Learned} in {Trying} to {Reform} {AI}},
	isbn = {978-1-315-80584-9},
	shorttitle = {Toward a {Critical} {Technical} {Practice}},
	abstract = {Every technology fits, in its own unique way, into a far-flung network of different sites of social practice. Some technologies are employed in a specific site, and in those cases we often feel that we can warrant clear cause-and-effect stories about the transformations that have accompanied them, either in that site or others. Other technologies, such as electric lighting and the telephone, are so ubiquitous—found contributing to the evolution of the activities and relations of so many distinct sites of practice—that it requires considerable effort to understand their effects on society, assuming that such a global notion of "effects" even makes sense (Hughes, 1983; Latour, 1987).},
	booktitle = {Social {Science}, {Technical} {Systems}, and {Cooperative} {Work}},
	publisher = {Psychology Press},
	author = {Agre, Philip E.},
	year = {1998},
	note = {Num Pages: 27},
}

@inproceedings{xue_pit_2023,
	address = {Singapore},
	title = {Pit {One} {Against} {Many}: {Leveraging} {Attention}-head {Embeddings} for {Parameter}-efficient {Multi}-head {Attention}},
	shorttitle = {Pit {One} {Against} {Many}},
	url = {https://aclanthology.org/2023.findings-emnlp.695},
	doi = {10.18653/v1/2023.findings-emnlp.695},
	abstract = {Scaling pre-trained language models has resulted in large performance gains in various natural language processing tasks but comes with a large cost in memory requirements. Inspired by the position embeddings in transformers, we aim to simplify and reduce the memory footprint of the multi-head attention (MHA) mechanism. We propose an alternative module that uses only a single shared projection matrix and multiple head embeddings (MHE), i.e. one per head. We empirically demonstrate that our MHE attention is substantially more memory efficient compared to alternative attention mechanisms while achieving high predictive performance retention ratio to vanilla MHA on several downstream tasks. MHE attention only requires a negligible fraction of additional parameters (3nd, where n is the number of attention heads and d the size of the head embeddings) compared to a single-head attention, while MHA requires (3n{\textasciicircum}2-3n)d{\textasciicircum}2-3nd additional parameters.},
	urldate = {2024-01-22},
	booktitle = {Findings of the {Association} for {Computational} {Linguistics}: {EMNLP} 2023},
	publisher = {Association for Computational Linguistics},
	author = {Xue, Huiyin and Aletras, Nikolaos},
	editor = {Bouamor, Houda and Pino, Juan and Bali, Kalika},
	month = dec,
	year = {2023},
	pages = {10355--10373},
}

@misc{yenduri_generative_2023,
	title = {Generative {Pre}-trained {Transformer}: {A} {Comprehensive} {Review} on {Enabling} {Technologies}, {Potential} {Applications}, {Emerging} {Challenges}, and {Future} {Directions}},
	shorttitle = {Generative {Pre}-trained {Transformer}},
	url = {http://arxiv.org/abs/2305.10435},
	doi = {10.48550/arXiv.2305.10435},
	abstract = {The Generative Pre-trained Transformer (GPT) represents a notable breakthrough in the domain of natural language processing, which is propelling us toward the development of machines that can understand and communicate using language in a manner that closely resembles that of humans. GPT is based on the transformer architecture, a deep neural network designed for natural language processing tasks. Due to their impressive performance on natural language processing tasks and ability to effectively converse, GPT have gained significant popularity among researchers and industrial communities, making them one of the most widely used and effective models in natural language processing and related fields, which motivated to conduct this review. This review provides a detailed overview of the GPT, including its architecture, working process, training procedures, enabling technologies, and its impact on various applications. In this review, we also explored the potential challenges and limitations of a GPT. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of GPT, enabling technologies, their impact on various applications, emerging challenges, and potential solutions.},
	urldate = {2024-01-22},
	publisher = {arXiv},
	author = {Yenduri, Gokul and M, Ramalingam and G, Chemmalar Selvi and Y, Supriya and Srivastava, Gautam and Maddikunta, Praveen Kumar Reddy and G, Deepti Raj and Jhaveri, Rutvij H. and B, Prabadevi and Wang, Weizheng and Vasilakos, Athanasios V. and Gadekallu, Thippa Reddy},
	month = may,
	year = {2023},
	note = {arXiv:2305.10435 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

@misc{tonmoy_comprehensive_2024,
	title = {A {Comprehensive} {Survey} of {Hallucination} {Mitigation} {Techniques} in {Large} {Language} {Models}},
	url = {http://arxiv.org/abs/2401.01313},
	doi = {10.48550/arXiv.2401.01313},
	abstract = {As Large Language Models (LLMs) continue to advance in their ability to write human-like text, a key challenge remains around their tendency to hallucinate generating content that appears factual but is ungrounded. This issue of hallucination is arguably the biggest hindrance to safely deploying these powerful LLMs into real-world production systems that impact people's lives. The journey toward widespread adoption of LLMs in practical settings heavily relies on addressing and mitigating hallucinations. Unlike traditional AI systems focused on limited tasks, LLMs have been exposed to vast amounts of online text data during training. While this allows them to display impressive language fluency, it also means they are capable of extrapolating information from the biases in training data, misinterpreting ambiguous prompts, or modifying the information to align superficially with the input. This becomes hugely alarming when we rely on language generation capabilities for sensitive applications, such as summarizing medical records, financial analysis reports, etc. This paper presents a comprehensive survey of over 32 techniques developed to mitigate hallucination in LLMs. Notable among these are Retrieval Augmented Generation (Lewis et al, 2021), Knowledge Retrieval (Varshney et al,2023), CoNLI (Lei et al, 2023), and CoVe (Dhuliawala et al, 2023). Furthermore, we introduce a detailed taxonomy categorizing these methods based on various parameters, such as dataset utilization, common tasks, feedback mechanisms, and retriever types. This classification helps distinguish the diverse approaches specifically designed to tackle hallucination issues in LLMs. Additionally, we analyze the challenges and limitations inherent in these techniques, providing a solid foundation for future research in addressing hallucinations and related phenomena within the realm of LLMs.},
	urldate = {2024-01-22},
	publisher = {arXiv},
	author = {Tonmoy, S. M. Towhidul Islam and Zaman, S. M. Mehedi and Jain, Vinija and Rani, Anku and Rawte, Vipula and Chadha, Aman and Das, Amitava},
	month = jan,
	year = {2024},
	note = {arXiv:2401.01313 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{zhang_alleviating_2023,
	title = {Alleviating {Hallucinations} of {Large} {Language} {Models} through {Induced} {Hallucinations}},
	url = {http://arxiv.org/abs/2312.15710},
	doi = {10.48550/arXiv.2312.15710},
	abstract = {Despite their impressive capabilities, large language models (LLMs) have been observed to generate responses that include inaccurate or fabricated information, a phenomenon commonly known as ``hallucination''. In this work, we propose a simple {\textbackslash}textit\{Induce-then-Contrast\} Decoding (ICD) strategy to alleviate hallucinations. We first construct a factually weak LLM by inducing hallucinations from the original LLMs. Then, we penalize these induced hallucinations during decoding to enhance the factuality of the generated content. Concretely, we determine the final next-token predictions by amplifying the predictions from the original model and downplaying the induced untruthful predictions via contrastive decoding. Experimental results on both discrimination-based and generation-based hallucination evaluation benchmarks, such as TruthfulQA and {\textbackslash}textsc\{FActScore\}, demonstrate that our proposed ICD methods can effectively enhance the factuality of LLMs across various model sizes and families. For example, when equipped with ICD, Llama2-7B-Chat and Mistral-7B-Instruct achieve performance comparable to ChatGPT and GPT4 on TruthfulQA, respectively.},
	urldate = {2024-01-22},
	publisher = {arXiv},
	author = {Zhang, Yue and Cui, Leyang and Bi, Wei and Shi, Shuming},
	month = dec,
	year = {2023},
	note = {arXiv:2312.15710 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

@misc{mei_improving_2023,
	title = {Improving hateful memes detection via learning hatefulness-aware embedding space through retrieval-guided contrastive learning},
	url = {http://arxiv.org/abs/2311.08110},
	doi = {10.48550/arXiv.2311.08110},
	abstract = {Hateful memes have emerged as a significant concern on the Internet. These memes, which are a combination of image and text, often convey messages vastly different from their individual meanings. Thus, detecting hateful memes requires the system to jointly understand the visual and textual modalities. However, our investigation reveals that the embedding space of existing CLIP-based systems lacks sensitivity to subtle differences in memes that are vital for correct hatefulness classification. To address this issue, we propose constructing a hatefulness-aware embedding space through retrieval-guided contrastive training. Specifically, we add an auxiliary loss that utilizes hard negative and pseudo-gold samples to train the embedding space. Our approach achieves state-of-the-art performance on the HatefulMemes dataset with an AUROC of 86.7. Notably, our approach outperforms much larger fine-tuned Large Multimodal Models like Flamingo and LLaVA. Finally, we demonstrate a retrieval-based hateful memes detection system, which is capable of making hatefulness classification based on data unseen in training from a database. This allows developers to update the hateful memes detection system by simply adding new data without retraining, a desirable feature for real services in the constantly-evolving landscape of hateful memes on the Internet.},
	urldate = {2024-01-22},
	publisher = {arXiv},
	author = {Mei, Jingbiao and Chen, Jinghong and Lin, Weizhe and Byrne, Bill and Tomalin, Marcus},
	month = nov,
	year = {2023},
	note = {arXiv:2311.08110 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition},
}

@misc{radford_learning_2021,
	title = {Learning {Transferable} {Visual} {Models} {From} {Natural} {Language} {Supervision}},
	url = {http://arxiv.org/abs/2103.00020},
	doi = {10.48550/arXiv.2103.00020},
	abstract = {State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP.},
	urldate = {2024-01-22},
	publisher = {arXiv},
	author = {Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and Krueger, Gretchen and Sutskever, Ilya},
	month = feb,
	year = {2021},
	note = {arXiv:2103.00020 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@misc{workshop_bloom_2023,
	title = {{BLOOM}: {A} {176B}-{Parameter} {Open}-{Access} {Multilingual} {Language} {Model}},
	shorttitle = {{BLOOM}},
	url = {http://arxiv.org/abs/2211.05100},
	doi = {10.48550/arXiv.2211.05100},
	abstract = {Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.},
	urldate = {2024-01-22},
	publisher = {arXiv},
	author = {Workshop, BigScience and Scao, Teven Le and Fan, Angela and Akiki, Christopher and Pavlick, Ellie and Ilić, Suzana and Hesslow, Daniel and Castagné, Roman and Luccioni, Alexandra Sasha and Yvon, François and Gallé, Matthias and Tow, Jonathan and Rush, Alexander M. and Biderman, Stella and Webson, Albert and Ammanamanchi, Pawan Sasanka and Wang, Thomas and Sagot, Benoît and Muennighoff, Niklas and del Moral, Albert Villanova and Ruwase, Olatunji and Bawden, Rachel and Bekman, Stas and McMillan-Major, Angelina and Beltagy, Iz and Nguyen, Huu and Saulnier, Lucile and Tan, Samson and Suarez, Pedro Ortiz and Sanh, Victor and Laurençon, Hugo and Jernite, Yacine and Launay, Julien and Mitchell, Margaret and Raffel, Colin and Gokaslan, Aaron and Simhi, Adi and Soroa, Aitor and Aji, Alham Fikri and Alfassy, Amit and Rogers, Anna and Nitzav, Ariel Kreisberg and Xu, Canwen and Mou, Chenghao and Emezue, Chris and Klamm, Christopher and Leong, Colin and van Strien, Daniel and Adelani, David Ifeoluwa and Radev, Dragomir and Ponferrada, Eduardo González and Levkovizh, Efrat and Kim, Ethan and Natan, Eyal Bar and De Toni, Francesco and Dupont, Gérard and Kruszewski, Germán and Pistilli, Giada and Elsahar, Hady and Benyamina, Hamza and Tran, Hieu and Yu, Ian and Abdulmumin, Idris and Johnson, Isaac and Gonzalez-Dios, Itziar and de la Rosa, Javier and Chim, Jenny and Dodge, Jesse and Zhu, Jian and Chang, Jonathan and Frohberg, Jörg and Tobing, Joseph and Bhattacharjee, Joydeep and Almubarak, Khalid and Chen, Kimbo and Lo, Kyle and Von Werra, Leandro and Weber, Leon and Phan, Long and allal, Loubna Ben and Tanguy, Ludovic and Dey, Manan and Muñoz, Manuel Romero and Masoud, Maraim and Grandury, María and Šaško, Mario and Huang, Max and Coavoux, Maximin and Singh, Mayank and Jiang, Mike Tian-Jian and Vu, Minh Chien and Jauhar, Mohammad A. and Ghaleb, Mustafa and Subramani, Nishant and Kassner, Nora and Khamis, Nurulaqilla and Nguyen, Olivier and Espejel, Omar and de Gibert, Ona and Villegas, Paulo and Henderson, Peter and Colombo, Pierre and Amuok, Priscilla and Lhoest, Quentin and Harliman, Rheza and Bommasani, Rishi and López, Roberto Luis and Ribeiro, Rui and Osei, Salomey and Pyysalo, Sampo and Nagel, Sebastian and Bose, Shamik and Muhammad, Shamsuddeen Hassan and Sharma, Shanya and Longpre, Shayne and Nikpoor, Somaieh and Silberberg, Stanislav and Pai, Suhas and Zink, Sydney and Torrent, Tiago Timponi and Schick, Timo and Thrush, Tristan and Danchev, Valentin and Nikoulina, Vassilina and Laippala, Veronika and Lepercq, Violette and Prabhu, Vrinda and Alyafeai, Zaid and Talat, Zeerak and Raja, Arun and Heinzerling, Benjamin and Si, Chenglei and Taşar, Davut Emre and Salesky, Elizabeth and Mielke, Sabrina J. and Lee, Wilson Y. and Sharma, Abheesht and Santilli, Andrea and Chaffin, Antoine and Stiegler, Arnaud and Datta, Debajyoti and Szczechla, Eliza and Chhablani, Gunjan and Wang, Han and Pandey, Harshit and Strobelt, Hendrik and Fries, Jason Alan and Rozen, Jos and Gao, Leo and Sutawika, Lintang and Bari, M. Saiful and Al-shaibani, Maged S. and Manica, Matteo and Nayak, Nihal and Teehan, Ryan and Albanie, Samuel and Shen, Sheng and Ben-David, Srulik and Bach, Stephen H. and Kim, Taewoon and Bers, Tali and Fevry, Thibault and Neeraj, Trishala and Thakker, Urmish and Raunak, Vikas and Tang, Xiangru and Yong, Zheng-Xin and Sun, Zhiqing and Brody, Shaked and Uri, Yallow and Tojarieh, Hadar and Roberts, Adam and Chung, Hyung Won and Tae, Jaesung and Phang, Jason and Press, Ofir and Li, Conglong and Narayanan, Deepak and Bourfoune, Hatim and Casper, Jared and Rasley, Jeff and Ryabinin, Max and Mishra, Mayank and Zhang, Minjia and Shoeybi, Mohammad and Peyrounette, Myriam and Patry, Nicolas and Tazi, Nouamane and Sanseviero, Omar and von Platen, Patrick and Cornette, Pierre and Lavallée, Pierre François and Lacroix, Rémi and Rajbhandari, Samyam and Gandhi, Sanchit and Smith, Shaden and Requena, Stéphane and Patil, Suraj and Dettmers, Tim and Baruwa, Ahmed and Singh, Amanpreet and Cheveleva, Anastasia and Ligozat, Anne-Laure and Subramonian, Arjun and Névéol, Aurélie and Lovering, Charles and Garrette, Dan and Tunuguntla, Deepak and Reiter, Ehud and Taktasheva, Ekaterina and Voloshina, Ekaterina and Bogdanov, Eli and Winata, Genta Indra and Schoelkopf, Hailey and Kalo, Jan-Christoph and Novikova, Jekaterina and Forde, Jessica Zosa and Clive, Jordan and Kasai, Jungo and Kawamura, Ken and Hazan, Liam and Carpuat, Marine and Clinciu, Miruna and Kim, Najoung and Cheng, Newton and Serikov, Oleg and Antverg, Omer and van der Wal, Oskar and Zhang, Rui and Zhang, Ruochen and Gehrmann, Sebastian and Mirkin, Shachar and Pais, Shani and Shavrina, Tatiana and Scialom, Thomas and Yun, Tian and Limisiewicz, Tomasz and Rieser, Verena and Protasov, Vitaly and Mikhailov, Vladislav and Pruksachatkun, Yada and Belinkov, Yonatan and Bamberger, Zachary and Kasner, Zdeněk and Rueda, Alice and Pestana, Amanda and Feizpour, Amir and Khan, Ammar and Faranak, Amy and Santos, Ana and Hevia, Anthony and Unldreaj, Antigona and Aghagol, Arash and Abdollahi, Arezoo and Tammour, Aycha and HajiHosseini, Azadeh and Behroozi, Bahareh and Ajibade, Benjamin and Saxena, Bharat and Ferrandis, Carlos Muñoz and McDuff, Daniel and Contractor, Danish and Lansky, David and David, Davis and Kiela, Douwe and Nguyen, Duong A. and Tan, Edward and Baylor, Emi and Ozoani, Ezinwanne and Mirza, Fatima and Ononiwu, Frankline and Rezanejad, Habib and Jones, Hessie and Bhattacharya, Indrani and Solaiman, Irene and Sedenko, Irina and Nejadgholi, Isar and Passmore, Jesse and Seltzer, Josh and Sanz, Julio Bonis and Dutra, Livia and Samagaio, Mairon and Elbadri, Maraim and Mieskes, Margot and Gerchick, Marissa and Akinlolu, Martha and McKenna, Michael and Qiu, Mike and Ghauri, Muhammed and Burynok, Mykola and Abrar, Nafis and Rajani, Nazneen and Elkott, Nour and Fahmy, Nour and Samuel, Olanrewaju and An, Ran and Kromann, Rasmus and Hao, Ryan and Alizadeh, Samira and Shubber, Sarmad and Wang, Silas and Roy, Sourav and Viguier, Sylvain and Le, Thanh and Oyebade, Tobi and Le, Trieu and Yang, Yoyo and Nguyen, Zach and Kashyap, Abhinav Ramesh and Palasciano, Alfredo and Callahan, Alison and Shukla, Anima and Miranda-Escalada, Antonio and Singh, Ayush and Beilharz, Benjamin and Wang, Bo and Brito, Caio and Zhou, Chenxi and Jain, Chirag and Xu, Chuxin and Fourrier, Clémentine and Periñán, Daniel León and Molano, Daniel and Yu, Dian and Manjavacas, Enrique and Barth, Fabio and Fuhrimann, Florian and Altay, Gabriel and Bayrak, Giyaseddin and Burns, Gully and Vrabec, Helena U. and Bello, Imane and Dash, Ishani and Kang, Jihyun and Giorgi, John and Golde, Jonas and Posada, Jose David and Sivaraman, Karthik Rangasai and Bulchandani, Lokesh and Liu, Lu and Shinzato, Luisa and de Bykhovetz, Madeleine Hahn and Takeuchi, Maiko and Pàmies, Marc and Castillo, Maria A. and Nezhurina, Marianna and Sänger, Mario and Samwald, Matthias and Cullan, Michael and Weinberg, Michael and De Wolf, Michiel and Mihaljcic, Mina and Liu, Minna and Freidank, Moritz and Kang, Myungsun and Seelam, Natasha and Dahlberg, Nathan and Broad, Nicholas Michio and Muellner, Nikolaus and Fung, Pascale and Haller, Patrick and Chandrasekhar, Ramya and Eisenberg, Renata and Martin, Robert and Canalli, Rodrigo and Su, Rosaline and Su, Ruisi and Cahyawijaya, Samuel and Garda, Samuele and Deshmukh, Shlok S. and Mishra, Shubhanshu and Kiblawi, Sid and Ott, Simon and Sang-aroonsiri, Sinee and Kumar, Srishti and Schweter, Stefan and Bharati, Sushil and Laud, Tanmay and Gigant, Théo and Kainuma, Tomoya and Kusa, Wojciech and Labrak, Yanis and Bajaj, Yash Shailesh and Venkatraman, Yash and Xu, Yifan and Xu, Yingxin and Xu, Yu and Tan, Zhe and Xie, Zhongli and Ye, Zifan and Bras, Mathilde and Belkada, Younes and Wolf, Thomas},
	month = jun,
	year = {2023},
	note = {arXiv:2211.05100 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{vaswani_attention_2023,
	title = {Attention {Is} {All} {You} {Need}},
	url = {http://arxiv.org/abs/1706.03762},
	doi = {10.48550/arXiv.1706.03762},
	abstract = {The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.},
	urldate = {2024-01-22},
	publisher = {arXiv},
	author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
	month = aug,
	year = {2023},
	note = {arXiv:1706.03762 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{liu_understanding_2024,
	title = {Understanding {LLMs}: {A} {Comprehensive} {Overview} from {Training} to {Inference}},
	shorttitle = {Understanding {LLMs}},
	url = {http://arxiv.org/abs/2401.02038},
	doi = {10.48550/arXiv.2401.02038},
	abstract = {The introduction of ChatGPT has led to a significant increase in the utilization of Large Language Models (LLMs) for addressing downstream tasks. There's an increasing focus on cost-efficient training and deployment within this context. Low-cost training and deployment of LLMs represent the future development trend. This paper reviews the evolution of large language model training techniques and inference deployment technologies aligned with this emerging trend. The discussion on training includes various aspects, including data preprocessing, training architecture, pre-training tasks, parallel training, and relevant content related to model fine-tuning. On the inference side, the paper covers topics such as model compression, parallel computation, memory scheduling, and structural optimization. It also explores LLMs' utilization and provides insights into their future development.},
	urldate = {2024-01-22},
	publisher = {arXiv},
	author = {Liu, Yiheng and He, Hao and Han, Tianle and Zhang, Xu and Liu, Mengyuan and Tian, Jiaming and Zhang, Yutong and Wang, Jiaqi and Gao, Xiaohui and Zhong, Tianyang and Pan, Yi and Xu, Shaochen and Wu, Zihao and Liu, Zhengliang and Zhang, Xin and Zhang, Shu and Hu, Xintao and Zhang, Tuo and Qiang, Ning and Liu, Tianming and Ge, Bao},
	month = jan,
	year = {2024},
	note = {arXiv:2401.02038 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{bengesi_advancements_2023,
	title = {Advancements in {Generative} {AI}: {A} {Comprehensive} {Review} of {GANs}, {GPT}, {Autoencoders}, {Diffusion} {Model}, and {Transformers}},
	shorttitle = {Advancements in {Generative} {AI}},
	url = {http://arxiv.org/abs/2311.10242},
	doi = {10.48550/arXiv.2311.10242},
	abstract = {The launch of ChatGPT has garnered global attention, marking a significant milestone in the field of Generative Artificial Intelligence. While Generative AI has been in effect for the past decade, the introduction of ChatGPT has ignited a new wave of research and innovation in the AI domain. This surge in interest has led to the development and release of numerous cutting-edge tools, such as Bard, Stable Diffusion, DALL-E, Make-A-Video, Runway ML, and Jukebox, among others. These tools exhibit remarkable capabilities, encompassing tasks ranging from text generation and music composition, image creation, video production, code generation, and even scientific work. They are built upon various state-of-the-art models, including Stable Diffusion, transformer models like GPT-3 (recent GPT-4), variational autoencoders, and generative adversarial networks. This advancement in Generative AI presents a wealth of exciting opportunities and, simultaneously, unprecedented challenges. Throughout this paper, we have explored these state-of-the-art models, the diverse array of tasks they can accomplish, the challenges they pose, and the promising future of Generative Artificial Intelligence.},
	urldate = {2024-01-22},
	publisher = {arXiv},
	author = {Bengesi, Staphord and El-Sayed, Hoda and Sarker, Md Kamruzzaman and Houkpati, Yao and Irungu, John and Oladunni, Timothy},
	month = nov,
	year = {2023},
	note = {arXiv:2311.10242 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
}

@misc{arazo_unsupervised_2019,
	title = {Unsupervised {Label} {Noise} {Modeling} and {Loss} {Correction}},
	url = {http://arxiv.org/abs/1904.11238},
	doi = {10.48550/arXiv.1904.11238},
	abstract = {Despite being robust to small amounts of label noise, convolutional neural networks trained with stochastic gradient methods have been shown to easily fit random labels. When there are a mixture of correct and mislabelled targets, networks tend to fit the former before the latter. This suggests using a suitable two-component mixture model as an unsupervised generative model of sample loss values during training to allow online estimation of the probability that a sample is mislabelled. Specifically, we propose a beta mixture to estimate this probability and correct the loss by relying on the network prediction (the so-called bootstrapping loss). We further adapt mixup augmentation to drive our approach a step further. Experiments on CIFAR-10/100 and TinyImageNet demonstrate a robustness to label noise that substantially outperforms recent state-of-the-art. Source code is available at https://git.io/fjsvE},
	urldate = {2024-01-17},
	publisher = {arXiv},
	author = {Arazo, Eric and Ortego, Diego and Albert, Paul and O'Connor, Noel E. and McGuinness, Kevin},
	month = jun,
	year = {2019},
	note = {arXiv:1904.11238 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{han_co-teaching_2018,
	title = {Co-teaching: {Robust} {Training} of {Deep} {Neural} {Networks} with {Extremely} {Noisy} {Labels}},
	shorttitle = {Co-teaching},
	url = {http://arxiv.org/abs/1804.06872},
	doi = {10.48550/arXiv.1804.06872},
	abstract = {Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during training. Nonetheless, recent studies on the memorization effects of deep neural networks show that they would first memorize training data of clean labels and then those of noisy labels. Therefore in this paper, we propose a new deep learning paradigm called Co-teaching for combating with noisy labels. Namely, we train two deep neural networks simultaneously, and let them teach each other given every mini-batch: firstly, each network feeds forward all data and selects some data of possibly clean labels; secondly, two networks communicate with each other what data in this mini-batch should be used for training; finally, each network back propagates the data selected by its peer network and updates itself. Empirical results on noisy versions of MNIST, CIFAR-10 and CIFAR-100 demonstrate that Co-teaching is much superior to the state-of-the-art methods in the robustness of trained deep models.},
	urldate = {2024-01-17},
	publisher = {arXiv},
	author = {Han, Bo and Yao, Quanming and Yu, Xingrui and Niu, Gang and Xu, Miao and Hu, Weihua and Tsang, Ivor and Sugiyama, Masashi},
	month = oct,
	year = {2018},
	note = {arXiv:1804.06872 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{pan_continual_2021,
	title = {Continual {Deep} {Learning} by {Functional} {Regularisation} of {Memorable} {Past}},
	url = {http://arxiv.org/abs/2004.14070},
	doi = {10.48550/arXiv.2004.14070},
	abstract = {Continually learning new skills is important for intelligent systems, yet standard deep learning methods suffer from catastrophic forgetting of the past. Recent works address this with weight regularisation. Functional regularisation, although computationally expensive, is expected to perform better, but rarely does so in practice. In this paper, we fix this issue by using a new functional-regularisation approach that utilises a few memorable past examples crucial to avoid forgetting. By using a Gaussian Process formulation of deep networks, our approach enables training in weight-space while identifying both the memorable past and a functional prior. Our method achieves state-of-the-art performance on standard benchmarks and opens a new direction for life-long learning where regularisation and memory-based methods are naturally combined.},
	urldate = {2024-01-17},
	publisher = {arXiv},
	author = {Pan, Pingbo and Swaroop, Siddharth and Immer, Alexander and Eschenhagen, Runa and Turner, Richard E. and Khan, Mohammad Emtiyaz},
	month = jan,
	year = {2021},
	note = {arXiv:2004.14070 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{guo_deepcore_2022,
	title = {{DeepCore}: {A} {Comprehensive} {Library} for {Coreset} {Selection} in {Deep} {Learning}},
	shorttitle = {{DeepCore}},
	url = {http://arxiv.org/abs/2204.08499},
	doi = {10.48550/arXiv.2204.08499},
	abstract = {Coreset selection, which aims to select a subset of the most informative training samples, is a long-standing learning problem that can benefit many downstream tasks such as data-efficient learning, continual learning, neural architecture search, active learning, etc. However, many existing coreset selection methods are not designed for deep learning, which may have high complexity and poor generalization performance. In addition, the recently proposed methods are evaluated on models, datasets, and settings of different complexities. To advance the research of coreset selection in deep learning, we contribute a comprehensive code library, namely DeepCore, and provide an empirical study on popular coreset selection methods on CIFAR10 and ImageNet datasets. Extensive experiments on CIFAR10 and ImageNet datasets verify that, although various methods have advantages in certain experiment settings, random selection is still a strong baseline.},
	urldate = {2024-01-17},
	publisher = {arXiv},
	author = {Guo, Chengcheng and Zhao, Bo and Bai, Yanbing},
	month = jun,
	year = {2022},
	note = {arXiv:2204.08499 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@misc{coleman_selection_2020,
	title = {Selection via {Proxy}: {Efficient} {Data} {Selection} for {Deep} {Learning}},
	shorttitle = {Selection via {Proxy}},
	url = {http://arxiv.org/abs/1906.11829},
	doi = {10.48550/arXiv.1906.11829},
	abstract = {Data selection methods, such as active learning and core-set selection, are useful tools for machine learning on large datasets. However, they can be prohibitively expensive to apply in deep learning because they depend on feature representations that need to be learned. In this work, we show that we can greatly improve the computational efficiency by using a small proxy model to perform data selection (e.g., selecting data points to label for active learning). By removing hidden layers from the target model, using smaller architectures, and training for fewer epochs, we create proxies that are an order of magnitude faster to train. Although these small proxy models have higher error rates, we find that they empirically provide useful signals for data selection. We evaluate this "selection via proxy" (SVP) approach on several data selection tasks across five datasets: CIFAR10, CIFAR100, ImageNet, Amazon Review Polarity, and Amazon Review Full. For active learning, applying SVP can give an order of magnitude improvement in data selection runtime (i.e., the time it takes to repeatedly train and select points) without significantly increasing the final error (often within 0.1\%). For core-set selection on CIFAR10, proxies that are over 10x faster to train than their larger, more accurate targets can remove up to 50\% of the data without harming the final accuracy of the target, leading to a 1.6x end-to-end training time improvement.},
	urldate = {2024-01-17},
	publisher = {arXiv},
	author = {Coleman, Cody and Yeh, Christopher and Mussmann, Stephen and Mirzasoleiman, Baharan and Bailis, Peter and Liang, Percy and Leskovec, Jure and Zaharia, Matei},
	month = oct,
	year = {2020},
	note = {arXiv:1906.11829 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{hendrycks_pretrained_2020,
	title = {Pretrained {Transformers} {Improve} {Out}-of-{Distribution} {Robustness}},
	url = {http://arxiv.org/abs/2004.06100},
	doi = {10.48550/arXiv.2004.06100},
	abstract = {Although pretrained Transformers such as BERT achieve high accuracy on in-distribution examples, do they generalize to new distributions? We systematically measure out-of-distribution (OOD) generalization for seven NLP datasets by constructing a new robustness benchmark with realistic distribution shifts. We measure the generalization of previous models including bag-of-words models, ConvNets, and LSTMs, and we show that pretrained Transformers' performance declines are substantially smaller. Pretrained transformers are also more effective at detecting anomalous or OOD examples, while many previous models are frequently worse than chance. We examine which factors affect robustness, finding that larger models are not necessarily more robust, distillation can be harmful, and more diverse pretraining data can enhance robustness. Finally, we show where future work can improve OOD robustness.},
	urldate = {2024-01-17},
	publisher = {arXiv},
	author = {Hendrycks, Dan and Liu, Xiaoyuan and Wallace, Eric and Dziedzic, Adam and Krishnan, Rishabh and Song, Dawn},
	month = apr,
	year = {2020},
	note = {arXiv:2004.06100 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{naveed_comprehensive_2023,
	title = {A {Comprehensive} {Overview} of {Large} {Language} {Models}},
	url = {http://arxiv.org/abs/2307.06435},
	doi = {10.48550/arXiv.2307.06435},
	abstract = {Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction. These works encompass diverse topics such as architectural innovations, better training strategies, context length improvements, fine-tuning, multi-modal LLMs, robotics, datasets, benchmarking, efficiency, and more. With the rapid development of techniques and regular breakthroughs in LLM research, it has become considerably challenging to perceive the bigger picture of the advances in this direction. Considering the rapidly emerging plethora of literature on LLMs, it is imperative that the research community is able to benefit from a concise yet comprehensive overview of the recent developments in this field. This article provides an overview of the existing literature on a broad range of LLM-related concepts. Our self-contained comprehensive overview of LLMs discusses relevant background concepts along with covering the advanced topics at the frontier of research in LLMs. This review article is intended to not only provide a systematic survey but also a quick comprehensive reference for the researchers and practitioners to draw insights from extensive informative summaries of the existing works to advance the LLM research.},
	urldate = {2024-01-17},
	publisher = {arXiv},
	author = {Naveed, Humza and Khan, Asad Ullah and Qiu, Shi and Saqib, Muhammad and Anwar, Saeed and Usman, Muhammad and Akhtar, Naveed and Barnes, Nick and Mian, Ajmal},
	month = dec,
	year = {2023},
	note = {arXiv:2307.06435 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@article{hupkes_taxonomy_2023,
	title = {A taxonomy and review of generalization research in {NLP}},
	volume = {5},
	copyright = {2023 The Author(s)},
	issn = {2522-5839},
	url = {https://www.nature.com/articles/s42256-023-00729-y},
	doi = {10.1038/s42256-023-00729-y},
	abstract = {The ability to generalize well is one of the primary desiderata for models of natural language processing (NLP), but what ‘good generalization’ entails and how it should be evaluated is not well understood. In this Analysis we present a taxonomy for characterizing and understanding generalization research in NLP. The proposed taxonomy is based on an extensive literature review and contains five axes along which generalization studies can differ: their main motivation, the type of generalization they aim to solve, the type of data shift they consider, the source by which this data shift originated, and the locus of the shift within the NLP modelling pipeline. We use our taxonomy to classify over 700 experiments, and we use the results to present an in-depth analysis that maps out the current state of generalization research in NLP and make recommendations for which areas deserve attention in the future.},
	language = {en},
	number = {10},
	urldate = {2024-01-17},
	journal = {Nature Machine Intelligence},
	author = {Hupkes, Dieuwke and Giulianelli, Mario and Dankers, Verna and Artetxe, Mikel and Elazar, Yanai and Pimentel, Tiago and Christodoulopoulos, Christos and Lasri, Karim and Saphra, Naomi and Sinclair, Arabella and Ulmer, Dennis and Schottmann, Florian and Batsuren, Khuyagbaatar and Sun, Kaiser and Sinha, Koustuv and Khalatbari, Leila and Ryskina, Maria and Frieske, Rita and Cotterell, Ryan and Jin, Zhijing},
	month = oct,
	year = {2023},
	note = {Number: 10
Publisher: Nature Publishing Group},
	keywords = {Computer science, Language and linguistics},
	pages = {1161--1174},
}

@inproceedings{linzen_how_2020,
	address = {Online},
	title = {How {Can} {We} {Accelerate} {Progress} {Towards} {Human}-like {Linguistic} {Generalization}?},
	url = {https://aclanthology.org/2020.acl-main.465},
	doi = {10.18653/v1/2020.acl-main.465},
	abstract = {This position paper describes and critiques the Pretraining-Agnostic Identically Distributed (PAID) evaluation paradigm, which has become a central tool for measuring progress in natural language understanding. This paradigm consists of three stages: (1) pre-training of a word prediction model on a corpus of arbitrary size; (2) fine-tuning (transfer learning) on a training set representing a classification task; (3) evaluation on a test set drawn from the same distribution as that training set. This paradigm favors simple, low-bias architectures, which, first, can be scaled to process vast amounts of data, and second, can capture the fine-grained statistical properties of a particular data set, regardless of whether those properties are likely to generalize to examples of the task outside the data set. This contrasts with humans, who learn language from several orders of magnitude less data than the systems favored by this evaluation paradigm, and generalize to new tasks in a consistent way. We advocate for supplementing or replacing PAID with paradigms that reward architectures that generalize as quickly and robustly as humans.},
	urldate = {2024-01-17},
	booktitle = {Proceedings of the 58th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics}},
	publisher = {Association for Computational Linguistics},
	author = {Linzen, Tal},
	editor = {Jurafsky, Dan and Chai, Joyce and Schluter, Natalie and Tetreault, Joel},
	month = jul,
	year = {2020},
	pages = {5210--5217},
}

@misc{nie_what_2020,
	title = {What {Can} {We} {Learn} from {Collective} {Human} {Opinions} on {Natural} {Language} {Inference} {Data}?},
	url = {http://arxiv.org/abs/2010.03532},
	doi = {10.48550/arXiv.2010.03532},
	abstract = {Despite the subjective nature of many NLP tasks, most NLU evaluations have focused on using the majority label with presumably high agreement as the ground truth. Less attention has been paid to the distribution of human opinions. We collect ChaosNLI, a dataset with a total of 464,500 annotations to study Collective HumAn OpinionS in oft-used NLI evaluation sets. This dataset is created by collecting 100 annotations per example for 3,113 examples in SNLI and MNLI and 1,532 examples in Abductive-NLI. Analysis reveals that: (1) high human disagreement exists in a noticeable amount of examples in these datasets; (2) the state-of-the-art models lack the ability to recover the distribution over human labels; (3) models achieve near-perfect accuracy on the subset of data with a high level of human agreement, whereas they can barely beat a random guess on the data with low levels of human agreement, which compose most of the common errors made by state-of-the-art models on the evaluation sets. This questions the validity of improving model performance on old metrics for the low-agreement part of evaluation datasets. Hence, we argue for a detailed examination of human agreement in future data collection efforts, and evaluating model outputs against the distribution over collective human opinions. The ChaosNLI dataset and experimental scripts are available at https://github.com/easonnie/ChaosNLI},
	urldate = {2024-01-17},
	publisher = {arXiv},
	author = {Nie, Yixin and Zhou, Xiang and Bansal, Mohit},
	month = oct,
	year = {2020},
	note = {arXiv:2010.03532 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{wu_music_2023,
	title = {Music {ControlNet}: {Multiple} {Time}-varying {Controls} for {Music} {Generation}},
	shorttitle = {Music {ControlNet}},
	url = {http://arxiv.org/abs/2311.07069},
	doi = {10.48550/arXiv.2311.07069},
	abstract = {Text-to-music generation models are now capable of generating high-quality music audio in broad styles. However, text control is primarily suitable for the manipulation of global musical attributes like genre, mood, and tempo, and is less suitable for precise control over time-varying attributes such as the positions of beats in time or the changing dynamics of the music. We propose Music ControlNet, a diffusion-based music generation model that offers multiple precise, time-varying controls over generated audio. To imbue text-to-music models with time-varying control, we propose an approach analogous to pixel-wise control of the image-domain ControlNet method. Specifically, we extract controls from training audio yielding paired data, and fine-tune a diffusion-based conditional generative model over audio spectrograms given melody, dynamics, and rhythm controls. While the image-domain Uni-ControlNet method already allows generation with any subset of controls, we devise a new strategy to allow creators to input controls that are only partially specified in time. We evaluate both on controls extracted from audio and controls we expect creators to provide, demonstrating that we can generate realistic music that corresponds to control inputs in both settings. While few comparable music generation models exist, we benchmark against MusicGen, a recent model that accepts text and melody input, and show that our model generates music that is 49\% more faithful to input melodies despite having 35x fewer parameters, training on 11x less data, and enabling two additional forms of time-varying control. Sound examples can be found at https://MusicControlNet.github.io/web/.},
	urldate = {2024-01-16},
	publisher = {arXiv},
	author = {Wu, Shih-Lun and Donahue, Chris and Watanabe, Shinji and Bryan, Nicholas J.},
	month = nov,
	year = {2023},
	note = {arXiv:2311.07069 [cs, eess]},
	keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@article{ren_survey_2021,
	title = {A {Survey} of {Deep} {Active} {Learning}},
	volume = {54},
	issn = {0360-0300},
	url = {https://dl.acm.org/doi/10.1145/3472291},
	doi = {10.1145/3472291},
	abstract = {Active learning (AL) attempts to maximize a model’s performance gain while annotating the fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize a massive number of parameters if the model is to learn how to extract high-quality features. In recent years, due to the rapid development of internet technology, we have entered an era of information abundance characterized by massive amounts of available data. As a result, DL has attracted significant attention from researchers and has been rapidly developed. Compared with DL, however, researchers have a relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples, meaning that early AL is rarely according the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to a large number of publicly available annotated datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, making it unfeasible in fields that require high levels of expertise (such as speech recognition, information extraction, medical images, etc.). Therefore, AL is gradually coming to receive the attention it is due. It is therefore natural to investigate whether AL can be used to reduce the cost of sample annotation while retaining the powerful learning capabilities of DL. As a result of such investigations, deep active learning (DeepAL) has emerged. Although research on this topic is quite abundant, there has not yet been a comprehensive survey of DeepAL-related works; accordingly, this article aims to fill this gap. We provide a formal classification method for the existing work, along with a comprehensive and systematic overview. In addition, we also analyze and summarize the development of DeepAL from an application perspective. Finally, we discuss the confusion and problems associated with DeepAL and provide some possible development directions.},
	number = {9},
	urldate = {2024-01-16},
	journal = {ACM Computing Surveys},
	author = {Ren, Pengzhen and Xiao, Yun and Chang, Xiaojun and Huang, Po-Yao and Li, Zhihui and Gupta, Brij B. and Chen, Xiaojiang and Wang, Xin},
	month = oct,
	year = {2021},
	keywords = {Deep learning, active learning, deep active learning},
	pages = {180:1--180:40},
}

@article{wu_when_2021,
	title = {{WHEN} {DO} {CURRICULA} {WORK}?},
	language = {en},
	author = {Wu, Xiaoxia and Dyer, Ethan and Neyshabur, Behnam},
	year = {2021},
}

@misc{russakovsky_imagenet_2015,
	title = {{ImageNet} {Large} {Scale} {Visual} {Recognition} {Challenge}},
	url = {http://arxiv.org/abs/1409.0575},
	doi = {10.48550/arXiv.1409.0575},
	abstract = {The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.},
	urldate = {2024-01-14},
	publisher = {arXiv},
	author = {Russakovsky, Olga and Deng, Jia and Su, Hao and Krause, Jonathan and Satheesh, Sanjeev and Ma, Sean and Huang, Zhiheng and Karpathy, Andrej and Khosla, Aditya and Bernstein, Michael and Berg, Alexander C. and Fei-Fei, Li},
	month = jan,
	year = {2015},
	note = {arXiv:1409.0575 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, I.4.8, I.5.2},
}

@misc{noauthor_imagenet_nodate,
	title = {{ImageNet}: {A} large-scale hierarchical image database {\textbar} {IEEE} {Conference} {Publication} {\textbar} {IEEE} {Xplore}},
	url = {https://ieeexplore.ieee.org/document/5206848},
	urldate = {2024-01-14},
}

@article{lecun_gradient-based_1998,
	title = {Gradient-based learning applied to document recognition},
	volume = {86},
	issn = {0018-9219},
	url = {http://www.scopus.com/inward/record.url?scp=0032203257&partnerID=8YFLogxK},
	doi = {10.1109/5.726791},
	abstract = {Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient-based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of two dimensional (2-D) shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation, recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN's), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank check is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal checks. It is deployed commercially and reads several million checks per day.},
	number = {11},
	urldate = {2024-01-14},
	journal = {Proceedings of the IEEE},
	author = {LeCun, Yann and Bottou, Léon and Bengio, Yoshua and Haffner, Patrick},
	year = {1998},
	keywords = {Convolutional neural networks, Document recognition, Finite state transducers, Gradient-based learning, Graph transformer networks, Machine learning, Neural networks, Optical character recognition (OCR)},
	pages = {2278--2323},
}

@article{krizhevsky_learning_nodate,
	title = {Learning {Multiple} {Layers} of {Features} from {Tiny} {Images}},
	language = {en},
	author = {Krizhevsky, Alex},
}

@misc{noauthor_gradient-based_nodate,
	title = {Gradient-based learning applied to document recognition {\textbar} {IEEE} {Journals} \& {Magazine} {\textbar} {IEEE} {Xplore}},
	url = {https://ieeexplore.ieee.org/document/726791},
	urldate = {2024-01-14},
}

@article{gou_knowledge_2021,
	title = {Knowledge {Distillation}: {A} {Survey}},
	volume = {129},
	issn = {0920-5691, 1573-1405},
	shorttitle = {Knowledge {Distillation}},
	url = {http://arxiv.org/abs/2006.05525},
	doi = {10.1007/s11263-021-01453-z},
	abstract = {In recent years, deep neural networks have been successful in both industry and academia, especially for computer vision tasks. The great success of deep learning is mainly due to its scalability to encode large-scale data and to maneuver billions of model parameters. However, it is a challenge to deploy these cumbersome deep models on devices with limited resources, e.g., mobile phones and embedded devices, not only because of the high computational complexity but also the large storage requirements. To this end, a variety of model compression and acceleration techniques have been developed. As a representative type of model compression and acceleration, knowledge distillation effectively learns a small student model from a large teacher model. It has received rapid increasing attention from the community. This paper provides a comprehensive survey of knowledge distillation from the perspectives of knowledge categories, training schemes, teacher-student architecture, distillation algorithms, performance comparison and applications. Furthermore, challenges in knowledge distillation are briefly reviewed and comments on future research are discussed and forwarded.},
	number = {6},
	urldate = {2024-01-14},
	journal = {International Journal of Computer Vision},
	author = {Gou, Jianping and Yu, Baosheng and Maybank, Stephen John and Tao, Dacheng},
	month = jun,
	year = {2021},
	note = {arXiv:2006.05525 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	pages = {1789--1819},
}

@misc{rabanser_selective_2022,
	title = {Selective {Classification} {Via} {Neural} {Network} {Training} {Dynamics}},
	url = {http://arxiv.org/abs/2205.13532},
	doi = {10.48550/arXiv.2205.13532},
	abstract = {Selective classification is the task of rejecting inputs a model would predict incorrectly on through a trade-off between input space coverage and model accuracy. Current methods for selective classification impose constraints on either the model architecture or the loss function; this inhibits their usage in practice. In contrast to prior work, we show that state-of-the-art selective classification performance can be attained solely from studying the (discretized) training dynamics of a model. We propose a general framework that, for a given test input, monitors metrics capturing the disagreement with the final predicted label over intermediate models obtained during training; we then reject data points exhibiting too much disagreement at late stages in training. In particular, we instantiate a method that tracks when the label predicted during training stops disagreeing with the final predicted label. Our experimental evaluation shows that our method achieves state-of-the-art accuracy/coverage trade-offs on typical selective classification benchmarks.},
	urldate = {2024-01-14},
	publisher = {arXiv},
	author = {Rabanser, Stephan and Thudi, Anvith and Hamidieh, Kimia and Dziedzic, Adam and Papernot, Nicolas},
	month = oct,
	year = {2022},
	note = {arXiv:2205.13532 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@inproceedings{nam_learning_2020,
	title = {Learning from {Failure}: {De}-biasing {Classifier} from {Biased} {Classifier}},
	volume = {33},
	shorttitle = {Learning from {Failure}},
	url = {https://proceedings.neurips.cc/paper/2020/hash/eddc3427c5d77843c2253f1e799fe933-Abstract.html},
	abstract = {Neural networks often learn to make predictions that overly rely on spurious corre- lation existing in the dataset, which causes the model to be biased. While previous work tackles this issue by using explicit labeling on the spuriously correlated attributes or presuming a particular bias type, we instead utilize a cheaper, yet generic form of human knowledge, which can be widely applicable to various types of bias. We first observe that neural networks learn to rely on the spurious correlation only when it is “easier” to learn than the desired knowledge, and such reliance is most prominent during the early phase of training. Based on the obser- vations, we propose a failure-based debiasing scheme by training a pair of neural networks simultaneously. Our main idea is twofold; (a) we intentionally train the first network to be biased by repeatedly amplifying its “prejudice”, and (b) we debias the training of the second network by focusing on samples that go against the prejudice of the biased network in (a). Extensive experiments demonstrate that our method significantly improves the training of network against various types of biases in both synthetic and real-world datasets. Surprisingly, our framework even occasionally outperforms the debiasing methods requiring explicit supervision of the spuriously correlated attributes.},
	urldate = {2024-01-14},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Nam, Junhyun and Cha, Hyuntak and Ahn, Sungsoo and Lee, Jaeho and Shin, Jinwoo},
	year = {2020},
	pages = {20673--20684},
}

@misc{vodrahalli_are_2018,
	title = {Are {All} {Training} {Examples} {Created} {Equal}? {An} {Empirical} {Study}},
	shorttitle = {Are {All} {Training} {Examples} {Created} {Equal}?},
	url = {http://arxiv.org/abs/1811.12569},
	doi = {10.48550/arXiv.1811.12569},
	abstract = {Modern computer vision algorithms often rely on very large training datasets. However, it is conceivable that a carefully selected subsample of the dataset is sufficient for training. In this paper, we propose a gradient-based importance measure that we use to empirically analyze relative importance of training images in four datasets of varying complexity. We find that in some cases, a small subsample is indeed sufficient for training. For other datasets, however, the relative differences in importance are negligible. These results have important implications for active learning on deep networks. Additionally, our analysis method can be used as a general tool to better understand diversity of training examples in datasets.},
	urldate = {2024-01-14},
	publisher = {arXiv},
	author = {Vodrahalli, Kailas and Li, Ke and Malik, Jitendra},
	month = nov,
	year = {2018},
	note = {arXiv:1811.12569 [cs, stat]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{kwak_trustal_2022,
	title = {{TrustAL}: {Trustworthy} {Active} {Learning} using {Knowledge} {Distillation}},
	shorttitle = {{TrustAL}},
	url = {http://arxiv.org/abs/2201.11661},
	doi = {10.48550/arXiv.2201.11661},
	abstract = {Active learning can be defined as iterations of data labeling, model training, and data acquisition, until sufficient labels are acquired. A traditional view of data acquisition is that, through iterations, knowledge from human labels and models is implicitly distilled to monotonically increase the accuracy and label consistency. Under this assumption, the most recently trained model is a good surrogate for the current labeled data, from which data acquisition is requested based on uncertainty/diversity. Our contribution is debunking this myth and proposing a new objective for distillation. First, we found example forgetting, which indicates the loss of knowledge learned across iterations. Second, for this reason, the last model is no longer the best teacher -- For mitigating such forgotten knowledge, we select one of its predecessor models as a teacher, by our proposed notion of "consistency". We show that this novel distillation is distinctive in the following three aspects; First, consistency ensures to avoid forgetting labels. Second, consistency improves both uncertainty/diversity of labeled data. Lastly, consistency redeems defective labels produced by human annotators.},
	urldate = {2024-01-14},
	publisher = {arXiv},
	author = {Kwak, Beong-woo and Kim, Youngwook and Kim, Yu Jin and Hwang, Seung-won and Yeo, Jinyoung},
	month = jan,
	year = {2022},
	note = {arXiv:2201.11661 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
}

@misc{karamcheti_mind_2021,
	title = {Mind {Your} {Outliers}! {Investigating} the {Negative} {Impact} of {Outliers} on {Active} {Learning} for {Visual} {Question} {Answering}},
	url = {http://arxiv.org/abs/2107.02331},
	doi = {10.48550/arXiv.2107.02331},
	abstract = {Active learning promises to alleviate the massive data needs of supervised machine learning: it has successfully improved sample efficiency by an order of magnitude on traditional tasks like topic classification and object recognition. However, we uncover a striking contrast to this promise: across 5 models and 4 datasets on the task of visual question answering, a wide variety of active learning approaches fail to outperform random selection. To understand this discrepancy, we profile 8 active learning methods on a per-example basis, and identify the problem as collective outliers -- groups of examples that active learning methods prefer to acquire but models fail to learn (e.g., questions that ask about text in images or require external knowledge). Through systematic ablation experiments and qualitative visualizations, we verify that collective outliers are a general phenomenon responsible for degrading pool-based active learning. Notably, we show that active learning sample efficiency increases significantly as the number of collective outliers in the active learning pool decreases. We conclude with a discussion and prescriptive recommendations for mitigating the effects of these outliers in future work.},
	urldate = {2024-01-14},
	publisher = {arXiv},
	author = {Karamcheti, Siddharth and Krishna, Ranjay and Fei-Fei, Li and Manning, Christopher D.},
	month = jul,
	year = {2021},
	note = {arXiv:2107.02331 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@inproceedings{lee_learning_2011,
	title = {Learning the easy things first: {Self}-paced visual category discovery},
	shorttitle = {Learning the easy things first},
	doi = {10.1109/CVPR.2011.5995523},
	booktitle = {The 24th {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}, {CVPR} 2011, {Colorado} {Springs}, {CO}, {USA}, 20-25 {June} 2011},
	publisher = {IEEE Computer Society},
	author = {Lee, Yong Jae and Grauman, Kristen},
	year = {2011},
	pages = {1721--1728},
}

@inproceedings{kumar_self-paced_2010,
	title = {Self-{Paced} {Learning} for {Latent} {Variable} {Models}},
	volume = {23},
	url = {https://papers.nips.cc/paper_files/paper/2010/hash/e57c6b956a6521b28495f2886ca0977a-Abstract.html},
	abstract = {Latent variable models are a powerful tool for addressing several tasks in machine learning. However, the algorithms for learning the parameters of latent variable models are prone to getting stuck in a bad local optimum. To alleviate this problem, we build on the intuition that, rather than considering all samples simultaneously, the algorithm should be presented with the training data in a meaningful order that facilitates learning. The order of the samples is determined by how easy they are. The main challenge is that often we are not provided with a readily computable measure of the easiness of samples. We address this issue by  proposing a novel, iterative self-paced learning algorithm where each iteration simultaneously selects easy samples and learns a new parameter vector. The number of samples selected is governed by a weight that is annealed until the entire training data has been considered. We empirically demonstrate that the self-paced learning algorithm outperforms the state of the art method for learning a latent structural SVM on four applications: object localization, noun phrase coreference, motif finding and handwritten digit recognition.},
	urldate = {2024-01-14},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Kumar, M. and Packer, Benjamin and Koller, Daphne},
	year = {2010},
}

@misc{soviany_curriculum_2022,
	title = {Curriculum {Learning}: {A} {Survey}},
	shorttitle = {Curriculum {Learning}},
	url = {http://arxiv.org/abs/2101.10382},
	doi = {10.48550/arXiv.2101.10382},
	abstract = {Training machine learning models in a meaningful order, from the easy samples to the hard ones, using curriculum learning can provide performance improvements over the standard training approach based on random data shuffling, without any additional computational costs. Curriculum learning strategies have been successfully employed in all areas of machine learning, in a wide range of tasks. However, the necessity of finding a way to rank the samples from easy to hard, as well as the right pacing function for introducing more difficult data can limit the usage of the curriculum approaches. In this survey, we show how these limits have been tackled in the literature, and we present different curriculum learning instantiations for various tasks in machine learning. We construct a multi-perspective taxonomy of curriculum learning approaches by hand, considering various classification criteria. We further build a hierarchical tree of curriculum learning methods using an agglomerative clustering algorithm, linking the discovered clusters with our taxonomy. At the end, we provide some interesting directions for future work.},
	urldate = {2024-01-14},
	publisher = {arXiv},
	author = {Soviany, Petru and Ionescu, Radu Tudor and Rota, Paolo and Sebe, Nicu},
	month = apr,
	year = {2022},
	note = {arXiv:2101.10382 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@article{wu_deep_2022,
	title = {Deep {Active} {Learning} for {Computer} {Vision} {Tasks}: {Methodologies}, {Applications}, and {Challenges}},
	volume = {12},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2076-3417},
	shorttitle = {Deep {Active} {Learning} for {Computer} {Vision} {Tasks}},
	url = {https://www.mdpi.com/2076-3417/12/16/8103},
	doi = {10.3390/app12168103},
	abstract = {Active learning is a label-efficient machine learning method that actively selects the most valuable unlabeled samples to annotate. Active learning focuses on achieving the best possible performance while using as few, high-quality sample annotations as possible. Recently, active learning achieved promotion combined with deep learning-based methods, which are named deep active learning methods in this paper. Deep active learning plays a crucial role in computer vision tasks, especially in label-insensitive scenarios, such as hard-to-label tasks (medical images analysis) and time-consuming tasks (autonomous driving). However, deep active learning still has some challenges, such as unstable performance and dirty data, which are future research trends. Compared with other reviews on deep active learning, our work introduced the deep active learning from computer vision-related methodologies and corresponding applications. The expected audience of this vision-friendly survey are researchers who are working in computer vision but willing to utilize deep active learning methods to solve vision problems. Specifically, this review systematically focuses on the details of methods, applications, and challenges in vision tasks, and we also introduce the classic theories, strategies, and scenarios of active learning in brief.},
	language = {en},
	number = {16},
	urldate = {2024-01-14},
	journal = {Applied Sciences},
	author = {Wu, Mingfei and Li, Chen and Yao, Zehuan},
	month = jan,
	year = {2022},
	note = {Number: 16
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {active learning, artificial intelligence, computer vision, deep learning},
	pages = {8103},
}

@article{takezoe_deep_2023,
	title = {Deep {Active} {Learning} for {Computer} {Vision}: {Past} and {Future}},
	volume = {12},
	issn = {2048-7703},
	shorttitle = {Deep {Active} {Learning} for {Computer} {Vision}},
	url = {http://arxiv.org/abs/2211.14819},
	doi = {10.1561/116.00000057},
	abstract = {As an important data selection schema, active learning emerges as the essential component when iterating an Artificial Intelligence (AI) model. It becomes even more critical given the dominance of deep neural network based models, which are composed of a large number of parameters and data hungry, in application. Despite its indispensable role for developing AI models, research on active learning is not as intensive as other research directions. In this paper, we present a review of active learning through deep active learning approaches from the following perspectives: 1) technical advancements in active learning, 2) applications of active learning in computer vision, 3) industrial systems leveraging or with potential to leverage active learning for data iteration, 4) current limitations and future research directions. We expect this paper to clarify the significance of active learning in a modern AI model manufacturing process and to bring additional research attention to active learning. By addressing data automation challenges and coping with automated machine learning systems, active learning will facilitate democratization of AI technologies by boosting model production at scale.},
	number = {1},
	urldate = {2024-01-14},
	journal = {APSIPA Transactions on Signal and Information Processing},
	author = {Takezoe, Rinyoichi and Liu, Xu and Mao, Shunan and Chen, Marco Tianyu and Feng, Zhanpeng and Zhang, Shiliang and Wang, Xiaoyu},
	year = {2023},
	note = {arXiv:2211.14819 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@inproceedings{peris_active_2018,
	address = {Brussels, Belgium},
	title = {Active {Learning} for {Interactive} {Neural} {Machine} {Translation} of {Data} {Streams}},
	url = {https://aclanthology.org/K18-1015},
	doi = {10.18653/v1/K18-1015},
	abstract = {We study the application of active learning techniques to the translation of unbounded data streams via interactive neural machine translation. The main idea is to select, from an unbounded stream of source sentences, those worth to be supervised by a human agent. The user will interactively translate those samples. Once validated, these data is useful for adapting the neural machine translation model. We propose two novel methods for selecting the samples to be validated. We exploit the information from the attention mechanism of a neural machine translation system. Our experiments show that the inclusion of active learning techniques into this pipeline allows to reduce the effort required during the process, while increasing the quality of the translation system. Moreover, it enables to balance the human effort required for achieving a certain translation quality. Moreover, our neural system outperforms classical approaches by a large margin.},
	urldate = {2024-01-14},
	booktitle = {Proceedings of the 22nd {Conference} on {Computational} {Natural} {Language} {Learning}},
	publisher = {Association for Computational Linguistics},
	author = {Peris, Álvaro and Casacuberta, Francisco},
	editor = {Korhonen, Anna and Titov, Ivan},
	month = oct,
	year = {2018},
	pages = {151--160},
}

@article{settles_active_nodate,
	title = {Active {Learning} {Literature} {Survey}},
	language = {en},
	author = {Settles, Burr},
}

@article{boca_pruned_nodate,
	title = {Pruned teachers are friendly teachers: improving knowledge distillation in {GAT} networks},
	abstract = {Knowledge distillation (KD) is a popular technique used to compress large machine learning models into smaller ones that can be more easily deployed on devices with limited resources. A recent line of work investigates “student-friendly” teachers that are more appropriate for knowledge transfer; pruned networks constitute one such example, with recent results suggesting that students trained with pruned models outperform models trained with unpruned teachers. While this line of work looks promising for future deployment of performant networks on resource-limited environments, little work has been done on investigating whether knowledge distillation using pruned networks can be used for Graph Neural Networks (GNNs). This project investigates whether pruned GNNs are better teachers than their unpruned counterparts. Results indicate that pruning GAT networks improves the distillation process on transductive node classficaition tasks. This is promising for many memory-constrained applications that require GNNs to perform inference, such as mobile navigation services and privacy-driven local recommender systems on apps. The code can be found at: https://github.com/semiluna/l46\_project.},
	language = {en},
	author = {Boca, Antonia},
}

@article{ince_harnessing_2023,
	title = {Harnessing {Dataset} {Cartography} for {Improved} {Compositional} {Generalization} in {Transformers}},
	copyright = {arXiv.org perpetual, non-exclusive license},
	url = {https://arxiv.org/abs/2310.12118},
	doi = {10.48550/ARXIV.2310.12118},
	abstract = {Neural networks have revolutionized language modeling and excelled in various downstream tasks. However, the extent to which these models achieve compositional generalization comparable to human cognitive abilities remains a topic of debate. While existing approaches in the field have mainly focused on novel architectures and alternative learning paradigms, we introduce a pioneering method harnessing the power of dataset cartography (Swayamdipta et al., 2020). By strategically identifying a subset of compositional generalization data using this approach, we achieve a remarkable improvement in model accuracy, yielding enhancements of up to 10\% on CFQ and COGS datasets. Notably, our technique incorporates dataset cartography as a curriculum learning criterion, eliminating the need for hyperparameter tuning while consistently achieving superior performance. Our findings highlight the untapped potential of dataset cartography in unleashing the full capabilities of compositional generalization within Transformer models. Our code is available at https://github.com/cyberiada/cartography-for-compositionality.},
	urldate = {2024-01-09},
	author = {İnce, Osman Batur and Zeraati, Tanin and Yagcioglu, Semih and Yaghoobzadeh, Yadollah and Erdem, Erkut and Erdem, Aykut},
	year = {2023},
	note = {Publisher: arXiv
Version Number: 1},
	keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences},
}

@inproceedings{nie_adversarial_2020,
	address = {Online},
	title = {Adversarial {NLI}: {A} {New} {Benchmark} for {Natural} {Language} {Understanding}},
	shorttitle = {Adversarial {NLI}},
	url = {https://aclanthology.org/2020.acl-main.441},
	doi = {10.18653/v1/2020.acl-main.441},
	abstract = {We introduce a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure. We show that training models on this new dataset leads to state-of-the-art performance on a variety of popular NLI benchmarks, while posing a more difficult challenge with its new test set. Our analysis sheds light on the shortcomings of current state-of-the-art models, and shows that non-expert annotators are successful at finding their weaknesses. The data collection method can be applied in a never-ending learning scenario, becoming a moving target for NLU, rather than a static benchmark that will quickly saturate.},
	urldate = {2024-01-08},
	booktitle = {Proceedings of the 58th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics}},
	publisher = {Association for Computational Linguistics},
	author = {Nie, Yixin and Williams, Adina and Dinan, Emily and Bansal, Mohit and Weston, Jason and Kiela, Douwe},
	editor = {Jurafsky, Dan and Chai, Joyce and Schluter, Natalie and Tetreault, Joel},
	month = jul,
	year = {2020},
	pages = {4885--4901},
}

@misc{williams_broad-coverage_2018,
	title = {A {Broad}-{Coverage} {Challenge} {Corpus} for {Sentence} {Understanding} through {Inference}},
	url = {http://arxiv.org/abs/1704.05426},
	doi = {10.48550/arXiv.1704.05426},
	abstract = {This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. In addition to being one of the largest corpora available for the task of NLI, at 433k examples, this corpus improves upon available resources in its coverage: it offers data from ten distinct genres of written and spoken English--making it possible to evaluate systems on nearly the full complexity of the language--and it offers an explicit setting for the evaluation of cross-genre domain adaptation.},
	urldate = {2024-01-08},
	publisher = {arXiv},
	author = {Williams, Adina and Nangia, Nikita and Bowman, Samuel R.},
	month = feb,
	year = {2018},
	note = {arXiv:1704.05426 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{wang_adversarial_2022,
	title = {Adversarial {GLUE}: {A} {Multi}-{Task} {Benchmark} for {Robustness} {Evaluation} of {Language} {Models}},
	shorttitle = {Adversarial {GLUE}},
	url = {http://arxiv.org/abs/2111.02840},
	doi = {10.48550/arXiv.2111.02840},
	abstract = {Large-scale pre-trained language models have achieved tremendous success across a wide range of natural language understanding (NLU) tasks, even surpassing human performance. However, recent studies reveal that the robustness of these models can be challenged by carefully crafted textual adversarial examples. While several individual datasets have been proposed to evaluate model robustness, a principled and comprehensive benchmark is still missing. In this paper, we present Adversarial GLUE (AdvGLUE), a new multi-task benchmark to quantitatively and thoroughly explore and evaluate the vulnerabilities of modern large-scale language models under various types of adversarial attacks. In particular, we systematically apply 14 textual adversarial attack methods to GLUE tasks to construct AdvGLUE, which is further validated by humans for reliable annotations. Our findings are summarized as follows. (i) Most existing adversarial attack algorithms are prone to generating invalid or ambiguous adversarial examples, with around 90\% of them either changing the original semantic meanings or misleading human annotators as well. Therefore, we perform a careful filtering process to curate a high-quality benchmark. (ii) All the language models and robust training methods we tested perform poorly on AdvGLUE, with scores lagging far behind the benign accuracy. We hope our work will motivate the development of new adversarial attacks that are more stealthy and semantic-preserving, as well as new robust language models against sophisticated adversarial attacks. AdvGLUE is available at https://adversarialglue.github.io.},
	urldate = {2024-01-08},
	publisher = {arXiv},
	author = {Wang, Boxin and Xu, Chejian and Wang, Shuohang and Gan, Zhe and Cheng, Yu and Gao, Jianfeng and Awadallah, Ahmed Hassan and Li, Bo},
	month = jan,
	year = {2022},
	note = {arXiv:2111.02840 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Cryptography and Security, Computer Science - Machine Learning},
}

@inproceedings{khot_scitail_2018,
	title = {{SciTaiL}: {A} {Textual} {Entailment} {Dataset} from {Science} {Question} {Answering}},
	volume = {32},
	shorttitle = {{SciTaiL}},
	url = {https://ojs.aaai.org/index.php/AAAI/article/view/12022},
	doi = {10.1609/aaai.v32i1.12022},
	abstract = {We present a new dataset and model for textual entailment, derived from treating multiple-choice question-answering as an entailment problem. SciTail is the first entailment set that is created solely from natural sentences that already exist independently ``in the wild'' rather than sentences authored specifically for the entailment task. Different from existing entailment datasets, we create hypotheses from science questions and the corresponding answer candidates, and premises from relevant web sentences retrieved from a large corpus. These sentences are often linguistically challenging. This, combined with the high lexical similarity of premise and hypothesis for both entailed and non-entailed pairs, makes this new entailment task particularly difficult. The resulting challenge is evidenced by state-of-the-art textual entailment systems achieving mediocre performance on SciTail, especially in comparison to a simple majority class baseline. As a step forward, we demonstrate that one can improve accuracy on SciTail by 5\% using a new neural model that exploits linguistic structure.},
	urldate = {2024-01-08},
	booktitle = {Proceedings of the {AAAI} {Conference} on {Artificial} {Intelligence}},
	author = {Khot, Tushar and Sabharwal, Ashish and Clark, Peter},
	month = apr,
	year = {2018},
	note = {ISSN: 2374-3468, 2159-5399
Issue: 1
Journal Abbreviation: AAAI},
}

@misc{rajpurkar_squad_2016,
	title = {{SQuAD}: 100,000+ {Questions} for {Machine} {Comprehension} of {Text}},
	shorttitle = {{SQuAD}},
	url = {http://arxiv.org/abs/1606.05250},
	doi = {10.48550/arXiv.1606.05250},
	abstract = {We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0\%, a significant improvement over a simple baseline (20\%). However, human performance (86.8\%) is much higher, indicating that the dataset presents a good challenge problem for future research. The dataset is freely available at https://stanford-qa.com},
	urldate = {2024-01-08},
	publisher = {arXiv},
	author = {Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy},
	month = oct,
	year = {2016},
	note = {arXiv:1606.05250 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{gupta_we_2023,
	title = {Do we need entire training data for adversarial training?},
	url = {http://arxiv.org/abs/2303.06241},
	abstract = {Deep Neural Networks (DNNs) are being used to solve a wide range of problems in many domains including safety-critical domains like self-driving cars and medical imagery. DNNs suffer from vulnerability against adversarial attacks. In the past few years, numerous approaches have been proposed to tackle this problem by training networks using adversarial training. Almost all the approaches generate adversarial examples for the entire training dataset, thus increasing the training time drastically. We show that we can decrease the training time for any adversarial training algorithm by using only a subset of training data for adversarial training. To select the subset, we filter the adversarially-prone samples from the training data. We perform a simple adversarial attack on all training examples to filter this subset. In this attack, we add a small perturbation to each pixel and a few grid lines to the input image. We perform adversarial training on the adversarially-prone subset and mix it with vanilla training performed on the entire dataset. Our results show that when our method-agnostic approach is plugged into FGSM, we achieve a speedup of 3.52x on MNIST and 1.98x on the CIFAR-10 dataset with comparable robust accuracy. We also test our approach on state-of-the-art Free adversarial training and achieve a speedup of 1.2x in training time with a marginal drop in robust accuracy on the ImageNet dataset.},
	urldate = {2024-01-06},
	publisher = {arXiv},
	author = {Gupta, Vipul and Narayan, Apurva},
	month = apr,
	year = {2023},
	note = {arXiv:2303.06241 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@misc{liu_music_2023,
	title = {Music {Understanding} {LLaMA}: {Advancing} {Text}-to-{Music} {Generation} with {Question} {Answering} and {Captioning}},
	shorttitle = {Music {Understanding} {LLaMA}},
	url = {http://arxiv.org/abs/2308.11276},
	doi = {10.48550/arXiv.2308.11276},
	abstract = {Text-to-music generation (T2M-Gen) faces a major obstacle due to the scarcity of large-scale publicly available music datasets with natural language captions. To address this, we propose the Music Understanding LLaMA (MU-LLaMA), capable of answering music-related questions and generating captions for music files. Our model utilizes audio representations from a pretrained MERT model to extract music features. However, obtaining a suitable dataset for training the MU-LLaMA model remains challenging, as existing publicly accessible audio question answering datasets lack the necessary depth for open-ended music question answering. To fill this gap, we present a methodology for generating question-answer pairs from existing audio captioning datasets and introduce the MusicQA Dataset designed for answering open-ended music-related questions. The experiments demonstrate that the proposed MU-LLaMA model, trained on our designed MusicQA dataset, achieves outstanding performance in both music question answering and music caption generation across various metrics, outperforming current state-of-the-art (SOTA) models in both fields and offering a promising advancement in the T2M-Gen research field.},
	urldate = {2024-01-02},
	publisher = {arXiv},
	author = {Liu, Shansong and Hussain, Atin Sakkeer and Sun, Chenshuo and Shan, Ying},
	month = aug,
	year = {2023},
	note = {arXiv:2308.11276 [cs, eess]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@article{shen_end--end_2019,
	title = {End-to-{End} {Deep} {Image} {Reconstruction} {From} {Human} {Brain} {Activity}},
	volume = {13},
	issn = {1662-5188},
	url = {https://www.frontiersin.org/articles/10.3389/fncom.2019.00021},
	abstract = {Deep neural networks (DNNs) have recently been applied successfully to brain decoding and image reconstruction from functional magnetic resonance imaging (fMRI) activity. However, direct training of a DNN with fMRI data is often avoided because the size of available data is thought to be insufficient for training a complex network with numerous parameters. Instead, a pre-trained DNN usually serves as a proxy for hierarchical visual representations, and fMRI data are used to decode individual DNN features of a stimulus image using a simple linear model, which are then passed to a reconstruction module. Here, we directly trained a DNN model with fMRI data and the corresponding stimulus images to build an end-to-end reconstruction model. We accomplished this by training a generative adversarial network with an additional loss term that was defined in high-level feature space (feature loss) using up to 6,000 training data samples (natural images and fMRI responses). The above model was tested on independent datasets and directly reconstructed image using an fMRI pattern as the input. Reconstructions obtained from our proposed method resembled the test stimuli (natural and artificial images) and reconstruction accuracy increased as a function of training-data size. Ablation analyses indicated that the feature loss that we employed played a critical role in achieving accurate reconstruction. Our results show that the end-to-end model can learn a direct mapping between brain activity and perception.},
	urldate = {2024-01-02},
	journal = {Frontiers in Computational Neuroscience},
	author = {Shen, Guohua and Dwivedi, Kshitij and Majima, Kei and Horikawa, Tomoyasu and Kamitani, Yukiyasu},
	year = {2019},
}

@misc{ho_denoising_2020,
	title = {Denoising {Diffusion} {Probabilistic} {Models}},
	url = {https://arxiv.org/abs/2006.11239v2},
	abstract = {We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at https://github.com/hojonathanho/diffusion},
	language = {en},
	urldate = {2024-01-01},
	journal = {arXiv.org},
	author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
	month = jun,
	year = {2020},
}

@article{bhaskar_prevalence_2016,
	title = {Prevalence of chronic insomnia in adult patients and its correlation with medical comorbidities},
	volume = {5},
	issn = {2249-4863},
	doi = {10.4103/2249-4863.201153},
	abstract = {Introduction:
Insomnia is one of the common but neglected conditions seen in family practice with long term and serious effects on health of a patient. Family physicians have the responsibility of diagnosing and adequately treating this. This study was done to find the prevalence of chronic insomnia in adult patients visiting a family medicine outpatient department (OPD) in a hospital and to assess the risk factors and co morbidities associated with it.

Materials and Methods:
A cross-sectional study was done in the family medicine OPD at St. Philomena's Hospital, Bengaluru. All adult patients attending the OPD from September 1 to October 30, 2015 were enrolled in the study after obtaining written consent. Athens Insomnia Scale was used to diagnose insomnia and information regarding medical co morbidities was collected. Data was analyzed for the prevalence of insomnia and its association with co morbidities.

Results:
Chronic insomnia was seen in 33\% of the adult population sampled. Increasing age and diabetes were significantly associated with insomnia, while other socioeconomic factors and co morbidities were not significantly associated. Twenty-seven percent of patients who had insomnia did not perceive the condition, which was statistically significant.

Conclusion:
Insomnia is a common sleep disorder which is many times missed by a primary care physician until/unless asked for. Since there is a higher incidence with increasing age and co morbidities such as diabetes, all patients, especially middle-aged and diabetics, should be screened for insomnia by the primary care physician with a self assessed questionnaire and counseled.},
	number = {4},
	urldate = {2024-01-01},
	journal = {Journal of Family Medicine and Primary Care},
	author = {Bhaskar, Swapna and Hemavathy, D. and Prasad, Shankar},
	year = {2016},
	pmid = {28348990},
	pmcid = {PMC5353813},
	pages = {780--784},
}

@article{ellis_chronic_2023,
	title = {Chronic {Insomnia} {Disorder} across {Europe}: {Expert} {Opinion} on {Challenges} and {Opportunities} to {Improve} {Care}},
	volume = {11},
	issn = {2227-9032},
	shorttitle = {Chronic {Insomnia} {Disorder} across {Europe}},
	doi = {10.3390/healthcare11050716},
	abstract = {One in ten adults in Europe have chronic insomnia, which is characterised by frequent and persistent difficulties initiating and/or maintaining sleep and daily functioning impairments. Regional differences in practices and access to healthcare services lead to variable clinical care across Europe. Typically, a patient with chronic insomnia (a) will usually present to a primary care physician; (b) will not be offered cognitive behavioural therapy for insomnia—the recommended first-line treatment; (c) will instead receive sleep hygiene recommendations and eventually pharmacotherapy to manage their long-term condition; and (d) will use medications such as GABA receptor agonists for longer than the approved duration. Available evidence suggests that patients in Europe have multiple unmet needs, and actions for clearer diagnosis of chronic insomnia and effective management of this condition are long overdue. In this article, we provide an update on the clinical management of chronic insomnia in Europe. Old and new treatments are summarised with information on indications, contraindications, precautions, warnings, and side effects. Challenges of treating chronic insomnia in European healthcare systems, considering patients’ perspectives and preferences are presented and discussed. Finally, suggestions are provided—with healthcare providers and healthcare policy makers in mind—for strategies to achieve the optimal clinical management.},
	number = {5},
	urldate = {2024-01-01},
	journal = {Healthcare},
	author = {Ellis, Jason and Ferini-Strambi, Luigi and García-Borreguero, Diego and Heidbreder, Anna and O’Regan, David and Parrino, Liborio and Selsick, Hugh and Penzel, Thomas},
	month = feb,
	year = {2023},
	pmid = {36900721},
	pmcid = {PMC10001099},
	pages = {716},
}

@misc{goodfellow_generative_2014,
	title = {Generative {Adversarial} {Networks}},
	url = {https://arxiv.org/abs/1406.2661v1},
	abstract = {We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.},
	language = {en},
	urldate = {2024-01-01},
	journal = {arXiv.org},
	author = {Goodfellow, Ian J. and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
	month = jun,
	year = {2014},
}

@misc{kingma_introduction_2019,
	title = {An {Introduction} to {Variational} {Autoencoders}},
	url = {https://arxiv.org/abs/1906.02691v3},
	abstract = {Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. In this work, we provide an introduction to variational autoencoders and some important extensions.},
	language = {en},
	urldate = {2024-01-01},
	journal = {arXiv.org},
	author = {Kingma, Diederik P. and Welling, Max},
	month = jun,
	year = {2019},
	doi = {10.1561/2200000056},
}

@misc{_haoheliuaudioldm_2023,
	title = {haoheliu/{AudioLDM}},
	url = {https://github.com/haoheliu/AudioLDM},
	abstract = {AudioLDM: Generate speech, sound effects, music and beyond, with text.},
	urldate = {2024-01-01},
	author = {刘濠赫, Haohe (Leo) Liu /},
	month = dec,
	year = {2023},
	note = {original-date: 2023-01-29T22:40:38Z},
}

@misc{luo_comprehensive_2023,
	title = {A {Comprehensive} {Survey} on {Knowledge} {Distillation} of {Diffusion} {Models}},
	url = {http://arxiv.org/abs/2304.04262},
	doi = {10.48550/arXiv.2304.04262},
	abstract = {Diffusion Models (DMs), also referred to as score-based diffusion models, utilize neural networks to specify score functions. Unlike most other probabilistic models, DMs directly model the score functions, which makes them more flexible to parametrize and potentially highly expressive for probabilistic modeling. DMs can learn fine-grained knowledge, i.e., marginal score functions, of the underlying distribution. Therefore, a crucial research direction is to explore how to distill the knowledge of DMs and fully utilize their potential. Our objective is to provide a comprehensible overview of the modern approaches for distilling DMs, starting with an introduction to DMs and a discussion of the challenges involved in distilling them into neural vector fields. We also provide an overview of the existing works on distilling DMs into both stochastic and deterministic implicit generators. Finally, we review the accelerated diffusion sampling algorithms as a training-free method for distillation. Our tutorial is intended for individuals with a basic understanding of generative models who wish to apply DM's distillation or embark on a research project in this field.},
	urldate = {2024-01-01},
	publisher = {arXiv},
	author = {Luo, Weijian},
	month = apr,
	year = {2023},
	note = {arXiv:2304.04262 [cs]},
	keywords = {Computer Science - Machine Learning},
}

@misc{noauthor_unbiased_nodate,
	title = {Unbiased look at dataset bias {\textbar} {IEEE} {Conference} {Publication} {\textbar} {IEEE} {Xplore}},
	url = {https://ieeexplore.ieee.org/document/5995347},
	urldate = {2023-12-31},
}

@misc{noauthor_papers_nodate,
	title = {Papers with {Code} - {ChaosNLI} {Dataset}},
	url = {https://paperswithcode.com/dataset/chaosnli},
	abstract = {Chaos NLI is a Natural Language Inference (NLI) dataset with 100 annotations per example (for a total of 464,500 annotations) for some existing data points in the development sets of SNLI, MNLI, and Abductive NLI. The dataset provides additional labels for NLI annotations that reflect the distribution of human annotators, instead of picking the majority label as the gold standard label.},
	language = {en},
	urldate = {2023-12-29},
}

@misc{lang_survey_2023,
	title = {A {Survey} on {Out}-of-{Distribution} {Detection} in {NLP}},
	url = {http://arxiv.org/abs/2305.03236},
	doi = {10.48550/arXiv.2305.03236},
	abstract = {Out-of-distribution (OOD) detection is essential for the reliable and safe deployment of machine learning systems in the real world. Great progress has been made over the past years. This paper presents the first review of recent advances in OOD detection with a particular focus on natural language processing approaches. First, we provide a formal definition of OOD detection and discuss several related fields. We then categorize recent algorithms into three classes according to the data they used: (1) OOD data available, (2) OOD data unavailable + in-distribution (ID) label available, and (3) OOD data unavailable + ID label unavailable. Third, we introduce datasets, applications, and metrics. Finally, we summarize existing work and present potential future research topics.},
	urldate = {2023-12-29},
	publisher = {arXiv},
	author = {Lang, Hao and Zheng, Yinhe and Li, Yixuan and Sun, Jian and Huang, Fei and Li, Yongbin},
	month = dec,
	year = {2023},
	note = {arXiv:2305.03236 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@inproceedings{arora_types_2021,
	address = {Online and Punta Cana, Dominican Republic},
	title = {Types of {Out}-of-{Distribution} {Texts} and {How} to {Detect} {Them}},
	url = {https://aclanthology.org/2021.emnlp-main.835},
	doi = {10.18653/v1/2021.emnlp-main.835},
	abstract = {Despite agreement on the importance of detecting out-of-distribution (OOD) examples, there is little consensus on the formal definition of the distribution shifts of OOD examples and how to best detect them. We categorize these examples as exhibiting a background shift or semantic shift, and find that the two major approaches to OOD detection, calibration and density estimation (language modeling for text), have distinct behavior on these types of OOD data. Across 14 pairs of in-distribution and OOD English natural language understanding datasets, we find that density estimation methods consistently beat calibration methods in background shift settings and perform worse in semantic shift settings. In addition, we find that both methods generally fail to detect examples from challenge data, indicating that these examples constitute a different type of OOD data. Overall, while the categorization we apply explains many of the differences between the two methods, our results call for a more explicit definition of OOD to create better benchmarks and build detectors that can target the type of OOD data expected at test time.},
	urldate = {2023-12-28},
	booktitle = {Proceedings of the 2021 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Arora, Udit and Huang, William and He, He},
	editor = {Moens, Marie-Francine and Huang, Xuanjing and Specia, Lucia and Yih, Scott Wen-tau},
	month = nov,
	year = {2021},
	pages = {10687--10701},
}

@inproceedings{gardner_competency_2021,
	address = {Online and Punta Cana, Dominican Republic},
	title = {Competency {Problems}: {On} {Finding} and {Removing} {Artifacts} in {Language} {Data}},
	shorttitle = {Competency {Problems}},
	url = {https://aclanthology.org/2021.emnlp-main.135},
	doi = {10.18653/v1/2021.emnlp-main.135},
	abstract = {Much recent work in NLP has documented dataset artifacts, bias, and spurious correlations between input features and output labels. However, how to tell which features have “spurious” instead of legitimate correlations is typically left unspecified. In this work we argue that for complex language understanding tasks, all simple feature correlations are spurious, and we formalize this notion into a class of problems which we call competency problems. For example, the word “amazing” on its own should not give information about a sentiment label independent of the context in which it appears, which could include negation, metaphor, sarcasm, etc. We theoretically analyze the difficulty of creating data for competency problems when human bias is taken into account, showing that realistic datasets will increasingly deviate from competency problems as dataset size increases. This analysis gives us a simple statistical test for dataset artifacts, which we use to show more subtle biases than were described in prior work, including demonstrating that models are inappropriately affected by these less extreme biases. Our theoretical treatment of this problem also allows us to analyze proposed solutions, such as making local edits to dataset instances, and to give recommendations for future data collection and model design efforts that target competency problems.},
	urldate = {2023-12-28},
	booktitle = {Proceedings of the 2021 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Gardner, Matt and Merrill, William and Dodge, Jesse and Peters, Matthew and Ross, Alexis and Singh, Sameer and Smith, Noah A.},
	editor = {Moens, Marie-Francine and Huang, Xuanjing and Specia, Lucia and Yih, Scott Wen-tau},
	month = nov,
	year = {2021},
	pages = {1801--1813},
}

@inproceedings{liu_wanli_2022,
	address = {Abu Dhabi, United Arab Emirates},
	title = {{WANLI}: {Worker} and {AI} {Collaboration} for {Natural} {Language} {Inference} {Dataset} {Creation}},
	shorttitle = {{WANLI}},
	url = {https://aclanthology.org/2022.findings-emnlp.508},
	doi = {10.18653/v1/2022.findings-emnlp.508},
	abstract = {A recurring challenge of crowdsourcing NLP datasets at scale is that human writers often rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity. We introduce a novel approach for dataset creation based on worker and AI collaboration, which brings together the generative strength of language models and the evaluative strength of humans. Starting with an existing dataset, MultiNLI for natural language inference (NLI), our approach uses dataset cartography to automatically identify examples that demonstrate challenging reasoning patterns, and instructs GPT-3 to compose new examples with similar patterns. Machine generated examples are then automatically filtered, and finally revised and labeled by human crowdworkers. The resulting dataset, WANLI, consists of 107,885 NLI examples and presents unique empirical strengths over existing NLI datasets. Remarkably, training a model on WANLI improves performance on eight out-of-domain test sets we consider, including by 11\% on HANS and 9\% on Adversarial NLI, compared to training on the 4x larger MultiNLI. Moreover, it continues to be more effective than MultiNLI augmented with other NLI datasets. Our results demonstrate the promise of leveraging natural language generation techniques and re-imagining the role of humans in the dataset creation process.},
	urldate = {2023-12-28},
	booktitle = {Findings of the {Association} for {Computational} {Linguistics}: {EMNLP} 2022},
	publisher = {Association for Computational Linguistics},
	author = {Liu, Alisa and Swayamdipta, Swabha and Smith, Noah A. and Choi, Yejin},
	editor = {Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue},
	month = dec,
	year = {2022},
	pages = {6826--6847},
}

@inproceedings{koreeda_contractnli_2021,
	address = {Punta Cana, Dominican Republic},
	title = {{ContractNLI}: {A} {Dataset} for {Document}-level {Natural} {Language} {Inference} for {Contracts}},
	shorttitle = {{ContractNLI}},
	url = {https://aclanthology.org/2021.findings-emnlp.164},
	doi = {10.18653/v1/2021.findings-emnlp.164},
	abstract = {Reviewing contracts is a time-consuming procedure that incurs large expenses to companies and social inequality to those who cannot afford it. In this work, we propose “document-level natural language inference (NLI) for contracts”, a novel, real-world application of NLI that addresses such problems. In this task, a system is given a set of hypotheses (such as “Some obligations of Agreement may survive termination.”) and a contract, and it is asked to classify whether each hypothesis is “entailed by”, “contradicting to” or “not mentioned by” (neutral to) the contract as well as identifying “evidence” for the decision as spans in the contract. We annotated and release the largest corpus to date consisting of 607 annotated contracts. We then show that existing models fail badly on our task and introduce a strong baseline, which (a) models evidence identification as multi-label classification over spans instead of trying to predict start and end tokens, and (b) employs more sophisticated context segmentation for dealing with long documents. We also show that linguistic characteristics of contracts, such as negations by exceptions, are contributing to the difficulty of this task and that there is much room for improvement.},
	urldate = {2023-12-28},
	booktitle = {Findings of the {Association} for {Computational} {Linguistics}: {EMNLP} 2021},
	publisher = {Association for Computational Linguistics},
	author = {Koreeda, Yuta and Manning, Christopher},
	editor = {Moens, Marie-Francine and Huang, Xuanjing and Specia, Lucia and Yih, Scott Wen-tau},
	month = nov,
	year = {2021},
	pages = {1907--1919},
}

@inproceedings{nie_adversarial_2020-1,
	address = {Online},
	title = {Adversarial {NLI}: {A} {New} {Benchmark} for {Natural} {Language} {Understanding}},
	shorttitle = {Adversarial {NLI}},
	url = {https://aclanthology.org/2020.acl-main.441},
	doi = {10.18653/v1/2020.acl-main.441},
	abstract = {We introduce a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure. We show that training models on this new dataset leads to state-of-the-art performance on a variety of popular NLI benchmarks, while posing a more difficult challenge with its new test set. Our analysis sheds light on the shortcomings of current state-of-the-art models, and shows that non-expert annotators are successful at finding their weaknesses. The data collection method can be applied in a never-ending learning scenario, becoming a moving target for NLU, rather than a static benchmark that will quickly saturate.},
	urldate = {2023-12-28},
	booktitle = {Proceedings of the 58th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics}},
	publisher = {Association for Computational Linguistics},
	author = {Nie, Yixin and Williams, Adina and Dinan, Emily and Bansal, Mohit and Weston, Jason and Kiela, Douwe},
	editor = {Jurafsky, Dan and Chai, Joyce and Schluter, Natalie and Tetreault, Joel},
	month = jul,
	year = {2020},
	pages = {4885--4901},
}

@misc{yuan_revisiting_2023,
	title = {Revisiting {Out}-of-distribution {Robustness} in {NLP}: {Benchmark}, {Analysis}, and {LLMs} {Evaluations}},
	shorttitle = {Revisiting {Out}-of-distribution {Robustness} in {NLP}},
	url = {http://arxiv.org/abs/2306.04618},
	abstract = {This paper reexamines the research on out-of-distribution (OOD) robustness in the field of NLP. We find that the distribution shift settings in previous studies commonly lack adequate challenges, hindering the accurate evaluation of OOD robustness. To address these issues, we propose a benchmark construction protocol that ensures clear differentiation and challenging distribution shifts. Then we introduce BOSS, a Benchmark suite for Out-of-distribution robustneSS evaluation covering 5 tasks and 20 datasets. Based on BOSS, we conduct a series of experiments on pre-trained language models for analysis and evaluation of OOD robustness. First, for vanilla fine-tuning, we examine the relationship between in-distribution (ID) and OOD performance. We identify three typical types that unveil the inner learning mechanism, which could potentially facilitate the forecasting of OOD robustness, correlating with the advancements on ID datasets. Then, we evaluate 5 classic methods on BOSS and find that, despite exhibiting some effectiveness in specific cases, they do not offer significant improvement compared to vanilla fine-tuning. Further, we evaluate 5 LLMs with various adaptation paradigms and find that when sufficient ID data is available, fine-tuning domain-specific models outperform LLMs on ID examples significantly. However, in the case of OOD instances, prioritizing LLMs with in-context learning yields better results. We identify that both fine-tuned small models and LLMs face challenges in effectively addressing downstream tasks. The code is public at {\textbackslash}url\{https://github.com/lifan-yuan/OOD\_NLP\}.},
	urldate = {2023-12-28},
	publisher = {arXiv},
	author = {Yuan, Lifan and Chen, Yangyi and Cui, Ganqu and Gao, Hongcheng and Zou, Fangyuan and Cheng, Xingyi and Ji, Heng and Liu, Zhiyuan and Sun, Maosong},
	month = oct,
	year = {2023},
	note = {arXiv:2306.04618 [cs]
version: 2},
	keywords = {Computer Science - Computation and Language, Computer Science - Cryptography and Security, Computer Science - Machine Learning},
}

@misc{noauthor_papers_nodate-1,
	title = {Papers with {Code} - {Revisiting} {Out}-of-distribution {Robustness} in {NLP}: {Benchmark}, {Analysis}, and {LLMs} {Evaluations}},
	shorttitle = {Papers with {Code} - {Revisiting} {Out}-of-distribution {Robustness} in {NLP}},
	url = {https://paperswithcode.com/paper/revisiting-out-of-distribution-robustness-in},
	abstract = {Implemented in one code library.},
	language = {en},
	urldate = {2023-12-28},
}

@misc{noauthor_training_2021,
	title = {Training {Machine} {Learning} {Models} {More} {Efficiently} with {Dataset} {Distillation}},
	url = {https://blog.research.google/2021/12/training-machine-learning-models-more.html},
	language = {en},
	urldate = {2023-12-26},
	month = dec,
	year = {2021},
}

@misc{ghorbani_interpretation_2018,
	title = {Interpretation of {Neural} {Networks} is {Fragile}},
	url = {http://arxiv.org/abs/1710.10547},
	doi = {10.48550/arXiv.1710.10547},
	abstract = {In order for machine learning to be deployed and trusted in many applications, it is crucial to be able to reliably explain why the machine learning algorithm makes certain predictions. For example, if an algorithm classifies a given pathology image to be a malignant tumor, then the doctor may need to know which parts of the image led the algorithm to this classification. How to interpret black-box predictors is thus an important and active area of research. A fundamental question is: how much can we trust the interpretation itself? In this paper, we show that interpretation of deep learning predictions is extremely fragile in the following sense: two perceptively indistinguishable inputs with the same predicted label can be assigned very different interpretations. We systematically characterize the fragility of several widely-used feature-importance interpretation methods (saliency maps, relevance propagation, and DeepLIFT) on ImageNet and CIFAR-10. Our experiments show that even small random perturbation can change the feature importance and new systematic perturbations can lead to dramatically different interpretations without changing the label. We extend these results to show that interpretations based on exemplars (e.g. influence functions) are similarly fragile. Our analysis of the geometry of the Hessian matrix gives insight on why fragility could be a fundamental challenge to the current interpretation approaches.},
	urldate = {2023-12-25},
	publisher = {arXiv},
	author = {Ghorbani, Amirata and Abid, Abubakar and Zou, James},
	month = nov,
	year = {2018},
	note = {arXiv:1710.10547 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{hinton_distilling_2015,
	title = {Distilling the {Knowledge} in a {Neural} {Network}},
	url = {http://arxiv.org/abs/1503.02531},
	doi = {10.48550/arXiv.1503.02531},
	abstract = {A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel.},
	urldate = {2023-12-24},
	publisher = {arXiv},
	author = {Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff},
	month = mar,
	year = {2015},
	note = {arXiv:1503.02531 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Statistics - Machine Learning},
}

@misc{noauthor_papers_nodate-2,
	title = {Papers with {Code} - {An} {Image} is {Worth} 16x16 {Words}: {Transformers} for {Image} {Recognition} at {Scale}},
	shorttitle = {Papers with {Code} - {An} {Image} is {Worth} 16x16 {Words}},
	url = {https://paperswithcode.com/paper/an-image-is-worth-16x16-words-transformers-1},
	abstract = {�� SOTA for Image Classification on CIFAR-10 (Percentage correct metric)},
	language = {en},
	urldate = {2023-12-23},
}

@inproceedings{jia_adversarial_2017,
	address = {Copenhagen, Denmark},
	title = {Adversarial {Examples} for {Evaluating} {Reading} {Comprehension} {Systems}},
	url = {https://aclanthology.org/D17-1215},
	doi = {10.18653/v1/D17-1215},
	abstract = {Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. In this adversarial setting, the accuracy of sixteen published models drops from an average of 75\% F1 score to 36\%; when the adversary is allowed to add ungrammatical sequences of words, average accuracy on four models decreases further to 7\%. We hope our insights will motivate the development of new models that understand language more precisely.},
	urldate = {2023-12-19},
	booktitle = {Proceedings of the 2017 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Jia, Robin and Liang, Percy},
	editor = {Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian},
	month = sep,
	year = {2017},
	pages = {2021--2031},
}

@misc{sakaguchi_winogrande_2019,
	title = {{WinoGrande}: {An} {Adversarial} {Winograd} {Schema} {Challenge} at {Scale}},
	shorttitle = {{WinoGrande}},
	url = {http://arxiv.org/abs/1907.10641},
	abstract = {The Winograd Schema Challenge (WSC) (Levesque, Davis, and Morgenstern 2011), a benchmark for commonsense reasoning, is a set of 273 expert-crafted pronoun resolution problems originally designed to be unsolvable for statistical models that rely on selectional preferences or word associations. However, recent advances in neural language models have already reached around 90\% accuracy on variants of WSC. This raises an important question whether these models have truly acquired robust commonsense capabilities or whether they rely on spurious biases in the datasets that lead to an overestimation of the true capabilities of machine commonsense. To investigate this question, we introduce WinoGrande, a large-scale dataset of 44k problems, inspired by the original WSC design, but adjusted to improve both the scale and the hardness of the dataset. The key steps of the dataset construction consist of (1) a carefully designed crowdsourcing procedure, followed by (2) systematic bias reduction using a novel AfLite algorithm that generalizes human-detectable word associations to machine-detectable embedding associations. The best state-of-the-art methods on WinoGrande achieve 59.4-79.1\%, which are 15-35\% below human performance of 94.0\%, depending on the amount of the training data allowed. Furthermore, we establish new state-of-the-art results on five related benchmarks - WSC (90.1\%), DPR (93.1\%), COPA (90.6\%), KnowRef (85.6\%), and Winogender (97.1\%). These results have dual implications: on one hand, they demonstrate the effectiveness of WinoGrande when used as a resource for transfer learning. On the other hand, they raise a concern that we are likely to be overestimating the true capabilities of machine commonsense across all these benchmarks. We emphasize the importance of algorithmic bias reduction in existing and future benchmarks to mitigate such overestimation.},
	urldate = {2023-12-19},
	publisher = {arXiv},
	author = {Sakaguchi, Keisuke and Bras, Ronan Le and Bhagavatula, Chandra and Choi, Yejin},
	month = nov,
	year = {2019},
	note = {arXiv:1907.10641 [cs]
version: 2},
	keywords = {Computer Science - Computation and Language},
}

@misc{noauthor_papers_nodate-3,
	title = {Papers with {Code} - {WinoGrande}: {An} {Adversarial} {Winograd} {Schema} {Challenge} at {Scale}},
	shorttitle = {Papers with {Code} - {WinoGrande}},
	url = {https://paperswithcode.com/paper/winogrande-an-adversarial-winograd-schema},
	abstract = {Implemented in 3 code libraries.},
	language = {en},
	urldate = {2023-12-19},
}

@misc{noauthor_papers_nodate-4,
	title = {Papers with {Code} - {WinoGrande} {Dataset}},
	url = {https://paperswithcode.com/dataset/winogrande},
	abstract = {WinoGrande is a large-scale dataset of 44k problems, inspired by the original WSC design, but adjusted to improve both the scale and the hardness of the dataset. The key steps of the dataset construction consist of (1) a carefully designed crowdsourcing procedure, followed by (2) systematic bias reduction using a novel AfLite algorithm that generalizes human-detectable word associations to machine-detectable embedding associations.},
	language = {en},
	urldate = {2023-12-19},
}

@misc{warden_speech_2018,
	title = {Speech {Commands}: {A} {Dataset} for {Limited}-{Vocabulary} {Speech} {Recognition}},
	shorttitle = {Speech {Commands}},
	url = {http://arxiv.org/abs/1804.03209},
	doi = {10.48550/arXiv.1804.03209},
	abstract = {Describes an audio dataset of spoken words designed to help train and evaluate keyword spotting systems. Discusses why this task is an interesting challenge, and why it requires a specialized dataset that is different from conventional datasets used for automatic speech recognition of full sentences. Suggests a methodology for reproducible and comparable accuracy metrics for this task. Describes how the data was collected and verified, what it contains, previous versions and properties. Concludes by reporting baseline results of models trained on this dataset.},
	urldate = {2023-12-16},
	publisher = {arXiv},
	author = {Warden, Pete},
	month = apr,
	year = {2018},
	note = {arXiv:1804.03209 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Human-Computer Interaction},
}

@misc{du_ftft_2023,
	title = {{FTFT}: efficient and robust {Fine}-{Tuning} by {transFerring} {Training} dynamics},
	shorttitle = {{FTFT}},
	url = {http://arxiv.org/abs/2310.06588},
	doi = {10.48550/arXiv.2310.06588},
	abstract = {Despite the massive success of fine-tuning large Pre-trained Language Models (PLMs) on a wide range of Natural Language Processing (NLP) tasks, they remain susceptible to out-of-distribution (OOD) and adversarial inputs. Data map (DM) is a simple yet effective dual-model approach that enhances the robustness of fine-tuned PLMs, which involves fine-tuning a model on the original training set (i.e. reference model), selecting a specified fraction of important training examples according to the training dynamics of the reference model, and fine-tuning the same model on these selected examples (i.e. main model). However, it suffers from the drawback of requiring fine-tuning the same model twice, which is computationally expensive for large models. In this paper, we first show that 1) training dynamics are highly transferable across different model sizes and different pre-training methods, and that 2) main models fine-tuned using DM learn faster than when using conventional Empirical Risk Minimization (ERM). Building on these observations, we propose a novel fine-tuning approach based on the DM method: Fine-Tuning by transFerring Training dynamics (FTFT). Compared with DM, FTFT uses more efficient reference models and then fine-tunes more capable main models for fewer steps. Our experiments show that FTFT achieves better generalization robustness than ERM while spending less than half of the training cost.},
	urldate = {2023-12-15},
	publisher = {arXiv},
	author = {Du, Yupei and Gatt, Albert and Nguyen, Dong},
	month = oct,
	year = {2023},
	note = {arXiv:2310.06588 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@inproceedings{sar-shalom_curating_2023,
	address = {Toronto, Canada},
	title = {Curating {Datasets} for {Better} {Performance} with {Example} {Training} {Dynamics}},
	url = {https://aclanthology.org/2023.findings-acl.674},
	doi = {10.18653/v1/2023.findings-acl.674},
	abstract = {The landscape of NLP research is dominated by large-scale models training on colossal datasets, relying on data quantity rather than quality. As an alternative to this landscape, we propose a method for weighing the relative importance of examples in a dataset based on their Example Training dynamics (swayamdipta et al., 2020) — a set of metrics computed during training. We propose a new way of computing the ETD of a dataset, and show that they can be used to improve performance in both in-distribution and out-of-distribution testing. We show that ETD can be transferable, i.e., they can be computed once and used for training different models, effectively reducing their computation cost. Finally, we suggest an active learning approach for computing ETD during training rather than as a preprocessing step — an approach that is not as effective, but dramatically reduces the extra computational costs.},
	urldate = {2023-12-15},
	booktitle = {Findings of the {Association} for {Computational} {Linguistics}: {ACL} 2023},
	publisher = {Association for Computational Linguistics},
	author = {Sar-Shalom, Aviad and Schwartz, Roy},
	editor = {Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki},
	month = jul,
	year = {2023},
	pages = {10597--10608},
}

@inproceedings{jia_adversarial_2017-1,
	address = {Copenhagen, Denmark},
	title = {Adversarial {Examples} for {Evaluating} {Reading} {Comprehension} {Systems}},
	url = {https://aclanthology.org/D17-1215},
	doi = {10.18653/v1/D17-1215},
	abstract = {Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. In this adversarial setting, the accuracy of sixteen published models drops from an average of 75\% F1 score to 36\%; when the adversary is allowed to add ungrammatical sequences of words, average accuracy on four models decreases further to 7\%. We hope our insights will motivate the development of new models that understand language more precisely.},
	urldate = {2023-12-14},
	booktitle = {Proceedings of the 2017 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Jia, Robin and Liang, Percy},
	editor = {Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian},
	month = sep,
	year = {2017},
	pages = {2021--2031},
}

@misc{chaudhari_attentive_2021,
	title = {An {Attentive} {Survey} of {Attention} {Models}},
	url = {http://arxiv.org/abs/1904.02874},
	abstract = {Attention Model has now become an important concept in neural networks that has been researched within diverse application domains. This survey provides a structured and comprehensive overview of the developments in modeling attention. In particular, we propose a taxonomy which groups existing techniques into coherent categories. We review salient neural architectures in which attention has been incorporated, and discuss applications in which modeling attention has shown a significant impact. We also describe how attention has been used to improve the interpretability of neural networks. Finally, we discuss some future research directions in attention. We hope this survey will provide a succinct introduction to attention models and guide practitioners while developing approaches for their applications.},
	urldate = {2023-12-11},
	publisher = {arXiv},
	author = {Chaudhari, Sneha and Mithal, Varun and Polatkan, Gungor and Ramanath, Rohan},
	month = jul,
	year = {2021},
	note = {arXiv:1904.02874 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{noauthor_glue_nodate,
	title = {glue · {Datasets} at {Hugging} {Face}},
	url = {https://huggingface.co/datasets/glue/viewer/qnli},
	abstract = {We’re on a journey to advance and democratize artificial intelligence through open source and open science.},
	urldate = {2023-12-10},
}

@misc{wang_glue_2019,
	title = {{GLUE}: {A} {Multi}-{Task} {Benchmark} and {Analysis} {Platform} for {Natural} {Language} {Understanding}},
	shorttitle = {{GLUE}},
	url = {http://arxiv.org/abs/1804.07461},
	doi = {10.48550/arXiv.1804.07461},
	abstract = {For natural language understanding (NLU) technology to be maximally useful, both practically and as a scientific object of study, it must be general: it must be able to process language in a way that is not exclusively tailored to any one specific task or dataset. In pursuit of this objective, we introduce the General Language Understanding Evaluation benchmark (GLUE), a tool for evaluating and analyzing the performance of models across a diverse range of existing NLU tasks. GLUE is model-agnostic, but it incentivizes sharing knowledge across tasks because certain tasks have very limited training data. We further provide a hand-crafted diagnostic test suite that enables detailed linguistic analysis of NLU models. We evaluate baselines based on current methods for multi-task and transfer learning and find that they do not immediately give substantial improvements over the aggregate performance of training a separate model per task, indicating room for improvement in developing general and robust NLU systems.},
	urldate = {2023-12-10},
	publisher = {arXiv},
	author = {Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
	month = feb,
	year = {2019},
	note = {arXiv:1804.07461 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{zellers_swag_2018,
	title = {{SWAG}: {A} {Large}-{Scale} {Adversarial} {Dataset} for {Grounded} {Commonsense} {Inference}},
	shorttitle = {{SWAG}},
	url = {http://arxiv.org/abs/1808.05326},
	doi = {10.48550/arXiv.1808.05326},
	abstract = {Given a partial description like "she opened the hood of the car," humans can reason about the situation and anticipate what might come next ("then, she examined the engine"). In this paper, we introduce the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning. We present SWAG, a new dataset with 113k multiple choice questions about a rich spectrum of grounded situations. To address the recurring challenges of the annotation artifacts and human biases found in many existing datasets, we propose Adversarial Filtering (AF), a novel procedure that constructs a de-biased dataset by iteratively training an ensemble of stylistic classifiers, and using them to filter the data. To account for the aggressive adversarial filtering, we use state-of-the-art language models to massively oversample a diverse set of potential counterfactuals. Empirical results demonstrate that while humans can solve the resulting inference problems with high accuracy (88\%), various competitive models struggle on our task. We provide comprehensive analysis that indicates significant opportunities for future research.},
	urldate = {2023-12-10},
	publisher = {arXiv},
	author = {Zellers, Rowan and Bisk, Yonatan and Schwartz, Roy and Choi, Yejin},
	month = aug,
	year = {2018},
	note = {arXiv:1808.05326 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@inproceedings{zhou_challenges_2021,
	address = {Online},
	title = {Challenges in {Automated} {Debiasing} for {Toxic} {Language} {Detection}},
	url = {https://aclanthology.org/2021.eacl-main.274},
	doi = {10.18653/v1/2021.eacl-main.274},
	abstract = {Biased associations have been a challenge in the development of classifiers for detecting toxic language, hindering both fairness and accuracy. As potential solutions, we investigate recently introduced debiasing methods for text classification datasets and models, as applied to toxic language detection. Our focus is on lexical (e.g., swear words, slurs, identity mentions) and dialectal markers (specifically African American English). Our comprehensive experiments establish that existing methods are limited in their ability to prevent biased behavior in current toxicity detectors. We then propose an automatic, dialect-aware data correction method, as a proof-of-concept. Despite the use of synthetic labels, this method reduces dialectal associations with toxicity. Overall, our findings show that debiasing a model trained on biased toxic language data is not as effective as simply relabeling the data to remove existing biases.},
	urldate = {2023-12-09},
	booktitle = {Proceedings of the 16th {Conference} of the {European} {Chapter} of the {Association} for {Computational} {Linguistics}: {Main} {Volume}},
	publisher = {Association for Computational Linguistics},
	author = {Zhou, Xuhui and Sap, Maarten and Swayamdipta, Swabha and Choi, Yejin and Smith, Noah},
	editor = {Merlo, Paola and Tiedemann, Jorg and Tsarfaty, Reut},
	month = apr,
	year = {2021},
	pages = {3143--3155},
}

@inproceedings{ghorbani_interpretation_2019,
	title = {Interpretation of neural networks is fragile},
	volume = {33},
	url = {https://aaai.org/ojs/index.php/AAAI/article/view/4252/4130},
	urldate = {2023-12-09},
	booktitle = {Proceedings of the {AAAI} conference on artificial intelligence},
	author = {Ghorbani, Amirata and Abid, Abubakar and Zou, James},
	year = {2019},
	note = {Issue: 01},
	pages = {3681--3688},
}

@inproceedings{ghorbani_data_2019,
	title = {Data shapley: {Equitable} valuation of data for machine learning},
	shorttitle = {Data shapley},
	url = {http://proceedings.mlr.press/v97/ghorbani19c.html},
	urldate = {2023-12-09},
	booktitle = {International conference on machine learning},
	publisher = {PMLR},
	author = {Ghorbani, Amirata and Zou, James},
	year = {2019},
	pages = {2242--2251},
}

@misc{watson_accelerated_2023,
	title = {Accelerated {Shapley} {Value} {Approximation} for {Data} {Evaluation}},
	url = {http://arxiv.org/abs/2311.05346},
	abstract = {Data valuation has found various applications in machine learning, such as data filtering, efficient learning and incentives for data sharing. The most popular current approach to data valuation is the Shapley value. While popular for its various applications, Shapley value is computationally expensive even to approximate, as it requires repeated iterations of training models on different subsets of data. In this paper we show that the Shapley value of data points can be approximated more efficiently by leveraging the structural properties of machine learning problems. We derive convergence guarantees on the accuracy of the approximate Shapley value for different learning settings including Stochastic Gradient Descent with convex and non-convex loss functions. Our analysis suggests that in fact models trained on small subsets are more important in the context of data valuation. Based on this idea, we describe \${\textbackslash}delta\$-Shapley -- a strategy of only using small subsets for the approximation. Experiments show that this approach preserves approximate value and rank of data, while achieving speedup of up to 9.9x. In pre-trained networks the approach is found to bring more efficiency in terms of accurate evaluation using small subsets.},
	urldate = {2023-12-09},
	publisher = {arXiv},
	author = {Watson, Lauren and Kujawa, Zeno and Andreeva, Rayna and Yang, Hao-Tsung and Elahi, Tariq and Sarkar, Rik},
	month = nov,
	year = {2023},
	note = {arXiv:2311.05346 [cs]},
	keywords = {Computer Science - Machine Learning},
}

@misc{goetz_active_2019,
	title = {Active {Federated} {Learning}},
	url = {http://arxiv.org/abs/1909.12641},
	doi = {10.48550/arXiv.1909.12641},
	abstract = {Federated Learning allows for population level models to be trained without centralizing client data by transmitting the global model to clients, calculating gradients locally, then averaging the gradients. Downloading models and uploading gradients uses the client's bandwidth, so minimizing these transmission costs is important. The data on each client is highly variable, so the benefit of training on different clients may differ dramatically. To exploit this we propose Active Federated Learning, where in each round clients are selected not uniformly at random, but with a probability conditioned on the current model and the data on the client to maximize efficiency. We propose a cheap, simple and intuitive sampling scheme which reduces the number of required training iterations by 20-70\% while maintaining the same model accuracy, and which mimics well known resampling techniques under certain conditions.},
	urldate = {2023-12-09},
	publisher = {arXiv},
	author = {Goetz, Jack and Malik, Kshitiz and Bui, Duc and Moon, Seungwhan and Liu, Honglei and Kumar, Anuj},
	month = sep,
	year = {2019},
	note = {arXiv:1909.12641 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{ma_mitigating_2023,
	title = {Mitigating {Bias} for {Question} {Answering} {Models} by {Tracking} {Bias} {Influence}},
	url = {http://arxiv.org/abs/2310.08795},
	doi = {10.48550/arXiv.2310.08795},
	abstract = {Models of various NLP tasks have been shown to exhibit stereotypes, and the bias in the question answering (QA) models is especially harmful as the output answers might be directly consumed by the end users. There have been datasets to evaluate bias in QA models, while bias mitigation technique for the QA models is still under-explored. In this work, we propose BMBI, an approach to mitigate the bias of multiple-choice QA models. Based on the intuition that a model would lean to be more biased if it learns from a biased example, we measure the bias level of a query instance by observing its influence on another instance. If the influenced instance is more biased, we derive that the query instance is biased. We then use the bias level detected as an optimization objective to form a multi-task learning setting in addition to the original QA task. We further introduce a new bias evaluation metric to quantify bias in a comprehensive and sensitive way. We show that our method could be applied to multiple QA formulations across multiple bias categories. It can significantly reduce the bias level in all 9 bias categories in the BBQ dataset while maintaining comparable QA accuracy.},
	urldate = {2023-12-08},
	publisher = {arXiv},
	author = {Ma, Mingyu Derek and Kao, Jiun-Yu and Gupta, Arpit and Lin, Yu-Hsiang and Zhao, Wenbo and Chung, Tagyoung and Wang, Wei and Chang, Kai-Wei and Peng, Nanyun},
	month = oct,
	year = {2023},
	note = {arXiv:2310.08795 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Machine Learning},
}

@misc{noauthor_bias_nodate,
	title = {Bias and unfairness in machine learning models: a systematic literature review},
	shorttitle = {Bias and unfairness in machine learning models},
	url = {https://ar5iv.labs.arxiv.org/html/2202.08176},
	abstract = {One of the difficulties of artificial intelligence is to ensure that model decisions are fair and free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and mitigate algorithmic unfai…},
	language = {en},
	urldate = {2023-12-08},
	journal = {ar5iv},
}

@article{karamcheti_mind_2021,
	title = {Mind {Your} {Outliers}! {Investigating} the {Negative} {Impact} of {Outliers} on {Active} {Learning} for {Visual} {Question} {Answering}},
	url = {https://aclanthology.org/2021.acl-long.564},
	doi = {10.18653/v1/2021.acl-long.564},
	abstract = {Active learning promises to alleviate the massive data needs of supervised machine learning: it has successfully improved sample efficiency by an order of magnitude on traditional tasks like topic classification and object recognition. However, we uncover a striking contrast to this promise: across 5 models and 4 datasets on the task of visual question answering, a wide variety of active learning approaches fail to outperform random selection. To understand this discrepancy, we profile 8 active learning methods on a per-example basis, and identify the problem as collective outliers – groups of examples that active learning methods prefer to acquire but models fail to learn (e.g., questions that ask about text in images or require external knowledge). Through systematic ablation experiments and qualitative visualizations, we verify that collective outliers are a general phenomenon responsible for degrading pool-based active learning. Notably, we show that active learning sample efficiency increases significantly as the number of collective outliers in the active learning pool decreases. We conclude with a discussion and prescriptive recommendations for mitigating the effects of these outliers in future work.},
	language = {en},
	urldate = {2023-12-08},
	journal = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
	author = {Karamcheti, Siddharth and Krishna, Ranjay and Fei-Fei, Li and Manning, Christopher},
	year = {2021},
	note = {Conference Name: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Place: Online
Publisher: Association for Computational Linguistics},
	pages = {7265--7281},
}

@misc{noauthor_pdf_nodate,
	title = {[{PDF}] {WinoGrande} {\textbar} {Semantic} {Scholar}},
	url = {https://www.semanticscholar.org/paper/WinoGrande-Sakaguchi-Bras/401dc39c2c8c910253d47980cfa3b4d2f7790d9b},
	urldate = {2023-12-08},
}

@inproceedings{utama_towards_2020,
	address = {Online},
	title = {Towards {Debiasing} {NLU} {Models} from {Unknown} {Biases}},
	url = {https://www.aclweb.org/anthology/2020.emnlp-main.613},
	doi = {10.18653/v1/2020.emnlp-main.613},
	abstract = {NLU models often exploit biases to achieve high dataset-specific performance without properly learning the intended task. Recently proposed debiasing methods are shown to be effective in mitigating this tendency. However, these methods rely on a major assumption that the types of bias should be known a-priori, which limits their application to many NLU tasks and datasets. In this work, we present the first step to bridge this gap by introducing a self-debiasing framework that prevents models from mainly utilizing biases without knowing them in advance. The proposed framework is general and complementary to the existing debiasing methods. We show that it allows these existing methods to retain the improvement on the challenge datasets (i.e., sets of examples designed to expose models' reliance on biases) without specifically targeting certain biases. Furthermore, the evaluation suggests that applying the framework results in improved overall robustness.},
	language = {en},
	urldate = {2023-12-08},
	booktitle = {Proceedings of the 2020 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing} ({EMNLP})},
	publisher = {Association for Computational Linguistics},
	author = {Utama, Prasetya Ajie and Moosavi, Nafise Sadat and Gurevych, Iryna},
	year = {2020},
	pages = {7597--7610},
}

@inproceedings{sar-shalom_curating_2023-1,
	address = {Toronto, Canada},
	title = {Curating {Datasets} for {Better} {Performance} with {Example} {Training} {Dynamics}},
	url = {https://aclanthology.org/2023.findings-acl.674},
	doi = {10.18653/v1/2023.findings-acl.674},
	abstract = {,},
	language = {en},
	urldate = {2023-12-08},
	booktitle = {Findings of the {Association} for {Computational} {Linguistics}: {ACL} 2023},
	publisher = {Association for Computational Linguistics},
	author = {Sar-Shalom, Aviad and Schwartz, Roy},
	year = {2023},
	pages = {10597--10608},
}

@misc{noauthor_connected_nodate,
	title = {Connected {Papers} {\textbar} {Find} and explore academic papers},
	url = {https://www.connectedpapers.com/main/ee5fff85d3ec62698eddba162f054b7e73670b2a/Dataset-Cartography%3A-Mapping-and-Diagnosing-Datasets-with-Training-Dynamics/graph},
	abstract = {A unique, visual tool to help researchers and applied scientists find and explore papers relevant to their field of work.},
	language = {en},
	urldate = {2023-12-08},
}

@inproceedings{bras_adversarial_2020,
	title = {Adversarial {Filters} of {Dataset} {Biases}},
	url = {https://www.semanticscholar.org/paper/Adversarial-Filters-of-Dataset-Biases-Bras-Swayamdipta/22d834f7983fbd7cf2418978571f23efcd224bd9},
	abstract = {Large neural models have demonstrated human-level performance on language and vision benchmarks, while their performance degrades considerably on adversarial or out-of-distribution samples. This raises the question of whether these models have learned to solve a dataset rather than the underlying task by overfitting to spurious dataset biases. We investigate one recently proposed approach, AFLite, which adversarially filters such dataset biases, as a means to mitigate the prevalent overestimation of machine performance. We provide a theoretical understanding for AFLite, by situating it in the generalized framework for optimum bias reduction. We present extensive supporting evidence that AFLite is broadly applicable for reduction of measurable dataset biases, and that models trained on the filtered datasets yield better generalization to out-of-distribution tasks. Finally, filtering results in a large drop in model performance (e.g., from 92\% to 62\% for SNLI), while human performance still remains high. Our work thus shows that such filtered datasets can pose new research challenges for robust generalization by serving as upgraded benchmarks.},
	urldate = {2023-12-08},
	author = {Bras, Ronan Le and Swayamdipta, Swabha and Bhagavatula, Chandra and Zellers, Rowan and Peters, Matthew E. and Sabharwal, Ashish and Choi, Yejin},
	month = feb,
	year = {2020},
}

@inproceedings{korakakis_improving_2023,
	address = {Toronto, Canada},
	title = {Improving the robustness of {NLI} models with minimax training},
	url = {https://aclanthology.org/2023.acl-long.801},
	doi = {10.18653/v1/2023.acl-long.801},
	abstract = {Natural language inference (NLI) models are susceptible to learning shortcuts, i.e. decision rules that spuriously correlate with the label. As a result, they achieve high in-distribution performance, but fail to generalize to out-of-distribution samples where such correlations do not hold. In this paper, we present a training method to reduce the reliance of NLI models on shortcuts and improve their out-of-distribution performance without assuming prior knowledge of the shortcuts being targeted. To this end, we propose a minimax objective between a learner model being trained for the NLI task, and an auxiliary model aiming to maximize the learner's loss by up-weighting examples from regions of the input space where the learner incurs high losses. This process incentivizes the learner to focus on under-represented “hard” examples with patterns that contradict the shortcuts learned from the prevailing “easy” examples. Experimental results on three NLI datasets demonstrate that our method consistently outperforms other robustness enhancing techniques on out-of-distribution adversarial test sets, while maintaining high in-distribution accuracy.},
	urldate = {2023-12-08},
	booktitle = {Proceedings of the 61st {Annual} {Meeting} of the {Association} for {Computational} {Linguistics} ({Volume} 1: {Long} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Korakakis, Michalis and Vlachos, Andreas},
	editor = {Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki},
	month = jul,
	year = {2023},
	pages = {14322--14339},
}

@misc{yang_diffusion_2023,
	title = {Diffusion {Models}: {A} {Comprehensive} {Survey} of {Methods} and {Applications}},
	shorttitle = {Diffusion {Models}},
	url = {http://arxiv.org/abs/2209.00796},
	doi = {10.48550/arXiv.2209.00796},
	abstract = {Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation, and molecule design. In this survey, we provide an overview of the rapidly expanding body of work on diffusion models, categorizing the research into three key areas: efficient sampling, improved likelihood estimation, and handling data with special structures. We also discuss the potential for combining diffusion models with other generative models for enhanced results. We further review the wide-ranging applications of diffusion models in fields spanning from computer vision, natural language generation, temporal data modeling, to interdisciplinary applications in other scientific disciplines. This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration. Github: https://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy.},
	urldate = {2023-12-08},
	publisher = {arXiv},
	author = {Yang, Ling and Zhang, Zhilong and Song, Yang and Hong, Shenda and Xu, Runsheng and Zhao, Yue and Zhang, Wentao and Cui, Bin and Yang, Ming-Hsuan},
	month = oct,
	year = {2023},
	note = {arXiv:2209.00796 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@article{croitoru_diffusion_2023,
	title = {Diffusion {Models} in {Vision}: {A} {Survey}},
	volume = {45},
	issn = {0162-8828, 2160-9292, 1939-3539},
	shorttitle = {Diffusion {Models} in {Vision}},
	url = {http://arxiv.org/abs/2209.04747},
	doi = {10.1109/TPAMI.2023.3261988},
	abstract = {Denoising diffusion models represent a recent emerging topic in computer vision, demonstrating remarkable results in the area of generative modeling. A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and a reverse diffusion stage. In the forward diffusion stage, the input data is gradually perturbed over several steps by adding Gaussian noise. In the reverse stage, a model is tasked at recovering the original input data by learning to gradually reverse the diffusion process, step by step. Diffusion models are widely appreciated for the quality and diversity of the generated samples, despite their known computational burdens, i.e. low speeds due to the high number of steps involved during sampling. In this survey, we provide a comprehensive review of articles on denoising diffusion models applied in vision, comprising both theoretical and practical contributions in the field. First, we identify and present three generic diffusion modeling frameworks, which are based on denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential equations. We further discuss the relations between diffusion models and other deep generative models, including variational auto-encoders, generative adversarial networks, energy-based models, autoregressive models and normalizing flows. Then, we introduce a multi-perspective categorization of diffusion models applied in computer vision. Finally, we illustrate the current limitations of diffusion models and envision some interesting directions for future research.},
	number = {9},
	urldate = {2023-12-08},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	author = {Croitoru, Florinel-Alin and Hondru, Vlad and Ionescu, Radu Tudor and Shah, Mubarak},
	month = sep,
	year = {2023},
	note = {arXiv:2209.04747 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
	pages = {10850--10869},
}

@misc{du_beyond_2023,
	title = {Beyond {Deep} {Reinforcement} {Learning}: {A} {Tutorial} on {Generative} {Diffusion} {Models} in {Network} {Optimization}},
	shorttitle = {Beyond {Deep} {Reinforcement} {Learning}},
	url = {http://arxiv.org/abs/2308.05384},
	doi = {10.48550/arXiv.2308.05384},
	abstract = {Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GAI), demonstrating their versatility and efficacy across a variety of applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation.We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization.},
	urldate = {2023-12-08},
	publisher = {arXiv},
	author = {Du, Hongyang and Zhang, Ruichen and Liu, Yinqiu and Wang, Jiacheng and Lin, Yijing and Li, Zonghang and Niyato, Dusit and Kang, Jiawen and Xiong, Zehui and Cui, Shuguang and Ai, Bo and Zhou, Haibo and Kim, Dong In},
	month = aug,
	year = {2023},
	note = {arXiv:2308.05384 [cs, eess]},
	keywords = {Computer Science - Networking and Internet Architecture, Electrical Engineering and Systems Science - Signal Processing},
}

@inproceedings{tromble_lattice_2008,
	address = {Honolulu, Hawaii},
	title = {Lattice {Minimum} {Bayes}-{Risk} decoding for statistical machine translation},
	url = {http://portal.acm.org/citation.cfm?doid=1613715.1613792},
	doi = {10.3115/1613715.1613792},
	abstract = {We present Minimum Bayes-Risk (MBR) decoding over translation lattices that compactly encode a huge number of translation hypotheses. We describe conditions on the loss function that will enable efﬁcient implementation of MBR decoders on lattices. We introduce an approximation to the BLEU score (Papineni et al., 2001) that satisﬁes these conditions. The MBR decoding under this approximate BLEU is realized using Weighted Finite State Automata. Our experiments show that the Lattice MBR decoder yields moderate, consistent gains in translation performance over N-best MBR decoding on Arabicto-English, Chinese-to-English and Englishto-Chinese translation tasks. We conduct a range of experiments to understand why Lattice MBR improves upon N-best MBR and study the impact of various parameters on MBR performance.},
	language = {en},
	urldate = {2023-12-08},
	booktitle = {Proceedings of the {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing} - {EMNLP} '08},
	publisher = {Association for Computational Linguistics},
	author = {Tromble, Roy W. and Kumar, Shankar and Och, Franz and Macherey, Wolfgang},
	year = {2008},
	pages = {620},
}

@misc{eikema_sampling-based_2021,
	title = {Sampling-{Based} {Approximations} to {Minimum} {Bayes} {Risk} {Decoding} for {Neural} {Machine} {Translation}},
	url = {https://arxiv.org/abs/2108.04718v2},
	abstract = {In NMT we search for the mode of the model distribution to form predictions. The mode and other high-probability translations found by beam search have been shown to often be inadequate in a number of ways. This prevents improving translation quality through better search, as these idiosyncratic translations end up selected by the decoding algorithm, a problem known as the beam search curse. Recently, an approximation to minimum Bayes risk (MBR) decoding has been proposed as an alternative decision rule that would likely not suffer from the same problems. We analyse this approximation and establish that it has no equivalent to the beam search curse. We then design approximations that decouple the cost of exploration from the cost of robust estimation of expected utility. This allows for much larger hypothesis spaces, which we show to be beneficial. We also show that mode-seeking strategies can aid in constructing compact sets of promising hypotheses and that MBR is effective in identifying good translations in them. We conduct experiments on three language pairs varying in amounts of resources available: English into and from German, Romanian, and Nepali.},
	language = {en},
	urldate = {2023-12-08},
	journal = {arXiv.org},
	author = {Eikema, Bryan and Aziz, Wilker},
	month = aug,
	year = {2021},
}

@misc{grootendorst_bertopic_2022,
	title = {{BERTopic}: {Neural} topic modeling with a class-based {TF}-{IDF} procedure},
	shorttitle = {{BERTopic}},
	url = {http://arxiv.org/abs/2203.05794},
	doi = {10.48550/arXiv.2203.05794},
	abstract = {Topic models can be useful tools to discover latent topics in collections of documents. Recent studies have shown the feasibility of approach topic modeling as a clustering task. We present BERTopic, a topic model that extends this process by extracting coherent topic representation through the development of a class-based variation of TF-IDF. More specifically, BERTopic generates document embedding with pre-trained transformer-based language models, clusters these embeddings, and finally, generates topic representations with the class-based TF-IDF procedure. BERTopic generates coherent topics and remains competitive across a variety of benchmarks involving classical models and those that follow the more recent clustering approach of topic modeling.},
	urldate = {2023-12-07},
	publisher = {arXiv},
	author = {Grootendorst, Maarten},
	month = mar,
	year = {2022},
	note = {arXiv:2203.05794 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@inproceedings{banerjee_meteor_2005,
	address = {Ann Arbor, Michigan},
	title = {{METEOR}: {An} {Automatic} {Metric} for {MT} {Evaluation} with {Improved} {Correlation} with {Human} {Judgments}},
	shorttitle = {{METEOR}},
	url = {https://aclanthology.org/W05-0909},
	urldate = {2023-12-07},
	booktitle = {Proceedings of the {ACL} {Workshop} on {Intrinsic} and {Extrinsic} {Evaluation} {Measures} for {Machine} {Translation} and/or {Summarization}},
	publisher = {Association for Computational Linguistics},
	author = {Banerjee, Satanjeev and Lavie, Alon},
	editor = {Goldstein, Jade and Lavie, Alon and Lin, Chin-Yew and Voss, Clare},
	month = jun,
	year = {2005},
	pages = {65--72},
}

@misc{zhang_bertscore_2020,
	title = {{BERTScore}: {Evaluating} {Text} {Generation} with {BERT}},
	shorttitle = {{BERTScore}},
	url = {http://arxiv.org/abs/1904.09675},
	doi = {10.48550/arXiv.1904.09675},
	abstract = {We propose BERTScore, an automatic evaluation metric for text generation. Analogously to common metrics, BERTScore computes a similarity score for each token in the candidate sentence with each token in the reference sentence. However, instead of exact matches, we compute token similarity using contextual embeddings. We evaluate using the outputs of 363 machine translation and image captioning systems. BERTScore correlates better with human judgments and provides stronger model selection performance than existing metrics. Finally, we use an adversarial paraphrase detection task to show that BERTScore is more robust to challenging examples when compared to existing metrics.},
	urldate = {2023-12-07},
	publisher = {arXiv},
	author = {Zhang, Tianyi and Kishore, Varsha and Wu, Felix and Weinberger, Kilian Q. and Artzi, Yoav},
	month = feb,
	year = {2020},
	note = {arXiv:1904.09675 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{huang_factual_2023,
	title = {The {Factual} {Inconsistency} {Problem} in {Abstractive} {Text} {Summarization}: {A} {Survey}},
	shorttitle = {The {Factual} {Inconsistency} {Problem} in {Abstractive} {Text} {Summarization}},
	url = {http://arxiv.org/abs/2104.14839},
	doi = {10.48550/arXiv.2104.14839},
	abstract = {Recently, various neural encoder-decoder models pioneered by Seq2Seq framework have been proposed to achieve the goal of generating more abstractive summaries by learning to map input text to output text. At a high level, such neural models can freely generate summaries without any constraint on the words or phrases used. Moreover, their format is closer to human-edited summaries and output is more readable and fluent. However, the neural model's abstraction ability is a double-edged sword. A commonly observed problem with the generated summaries is the distortion or fabrication of factual information in the article. This inconsistency between the original text and the summary has caused various concerns over its applicability, and the previous evaluation methods of text summarization are not suitable for this issue. In response to the above problems, the current research direction is predominantly divided into two categories, one is to design fact-aware evaluation metrics to select outputs without factual inconsistency errors, and the other is to develop new summarization systems towards factual consistency. In this survey, we focus on presenting a comprehensive review of these fact-specific evaluation methods and text summarization models.},
	urldate = {2023-12-06},
	publisher = {arXiv},
	author = {Huang, Yichong and Feng, Xiachong and Feng, Xiaocheng and Qin, Bing},
	month = apr,
	year = {2023},
	note = {arXiv:2104.14839 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@inproceedings{deutsch_understanding_2021,
	address = {Online},
	title = {Understanding the {Extent} to which {Content} {Quality} {Metrics} {Measure} the {Information} {Quality} of {Summaries}},
	url = {https://aclanthology.org/2021.conll-1.24},
	doi = {10.18653/v1/2021.conll-1.24},
	abstract = {Reference-based metrics such as ROUGE or BERTScore evaluate the content quality of a summary by comparing the summary to a reference. Ideally, this comparison should measure the summary's information quality by calculating how much information the summaries have in common. In this work, we analyze the token alignments used by ROUGE and BERTScore to compare summaries and argue that their scores largely cannot be interpreted as measuring information overlap. Rather, they are better estimates of the extent to which the summaries discuss the same topics. Further, we provide evidence that this result holds true for many other summarization evaluation metrics. The consequence of this result is that the most frequently used summarization evaluation metrics do not align with the community's research goal, to generate summaries with high-quality information. However, we conclude by demonstrating that a recently proposed metric, QAEval, which scores summaries using question-answering, appears to better capture information quality than current evaluations, highlighting a direction for future research.},
	urldate = {2023-12-06},
	booktitle = {Proceedings of the 25th {Conference} on {Computational} {Natural} {Language} {Learning}},
	publisher = {Association for Computational Linguistics},
	author = {Deutsch, Daniel and Roth, Dan},
	editor = {Bisazza, Arianna and Abend, Omri},
	month = nov,
	year = {2021},
	pages = {300--309},
}

@misc{liu_roberta_2019,
	title = {{RoBERTa}: {A} {Robustly} {Optimized} {BERT} {Pretraining} {Approach}},
	shorttitle = {{RoBERTa}},
	url = {http://arxiv.org/abs/1907.11692},
	doi = {10.48550/arXiv.1907.11692},
	abstract = {Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code.},
	urldate = {2023-12-04},
	publisher = {arXiv},
	author = {Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin},
	month = jul,
	year = {2019},
	note = {arXiv:1907.11692 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@article{flesch_modelling_2023,
	title = {Modelling continual learning in humans with {Hebbian} context gating and exponentially decaying task signals},
	volume = {19},
	url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010808},
	number = {1},
	urldate = {2023-12-03},
	journal = {PLOS Computational Biology},
	author = {Flesch, Timo and Nagy, David G. and Saxe, Andrew and Summerfield, Christopher},
	year = {2023},
	note = {Publisher: Public Library of Science San Francisco, CA USA},
	pages = {e1010808},
}

@article{flesch_orthogonal_2022,
	title = {Orthogonal representations for robust context-dependent task performance in brains and neural networks},
	volume = {110},
	url = {https://www.cell.com/neuron/pdf/S0896-6273(22)00005-8.pdf},
	number = {7},
	urldate = {2023-12-03},
	journal = {Neuron},
	author = {Flesch, Timo and Juechems, Keno and Dumbalska, Tsvetomira and Saxe, Andrew and Summerfield, Christopher},
	year = {2022},
	note = {Publisher: Elsevier},
	pages = {1258--1270},
}

@article{flesch_comparing_2018,
	title = {Comparing continual task learning in minds and machines},
	volume = {115},
	issn = {0027-8424, 1091-6490},
	url = {https://pnas.org/doi/full/10.1073/pnas.1800755115},
	doi = {10.1073/pnas.1800755115},
	abstract = {Significance
            Humans learn to perform many different tasks over the lifespan, such as speaking both French and Spanish. The brain has to represent task information without mutual interference. In machine learning, this “continual learning” is a major unsolved challenge. Here, we studied the patterns of errors made by humans and state-of-the-art neural networks while they learned new tasks from scratch and without instruction. Humans, but not machines, seem to benefit from training regimes that blocked one task at a time, especially when they had a prior bias to represent stimuli in a way that encouraged task separation. Machines trained to exhibit the same prior bias suffered less interference between tasks, suggesting new avenues for solving continual learning in artificial systems.
          , 
            Humans can learn to perform multiple tasks in succession over the lifespan (“continual” learning), whereas current machine learning systems fail. Here, we investigated the cognitive mechanisms that permit successful continual learning in humans and harnessed our behavioral findings for neural network design. Humans categorized naturalistic images of trees according to one of two orthogonal task rules that were learned by trial and error. Training regimes that focused on individual rules for prolonged periods (blocked training) improved human performance on a later test involving randomly interleaved rules, compared with control regimes that trained in an interleaved fashion. Analysis of human error patterns suggested that blocked training encouraged humans to form “factorized” representation that optimally segregated the tasks, especially for those individuals with a strong prior bias to represent the stimulus space in a well-structured way. By contrast, standard supervised deep neural networks trained on the same tasks suffered catastrophic forgetting under blocked training, due to representational interference in the deeper layers. However, augmenting deep networks with an unsupervised generative model that allowed it to first learn a good embedding of the stimulus space (similar to that observed in humans) reduced catastrophic forgetting under blocked training. Building artificial agents that first learn a model of the world may be one promising route to solving continual task performance in artificial intelligence research.},
	language = {en},
	number = {44},
	urldate = {2023-12-03},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Flesch, Timo and Balaguer, Jan and Dekker, Ronald and Nili, Hamed and Summerfield, Christopher},
	month = oct,
	year = {2018},
}

@article{ma_computational_2021,
	title = {A computational lens into how music characterizes genre in film},
	volume = {16},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0249957},
	number = {4},
	urldate = {2023-12-03},
	journal = {PloS one},
	author = {Ma, Benjamin and Greer, Timothy and Knox, Dillon and Narayanan, Shrikanth},
	year = {2021},
	note = {Publisher: Public Library of Science San Francisco, CA USA},
	pages = {e0249957},
}

@misc{noauthor_learning_nodate,
	title = {Learning {Shared} {Vector} {Representations} of {Lyrics} and {Chords} in {Music} {\textbar} {IEEE} {Conference} {Publication} {\textbar} {IEEE} {Xplore}},
	url = {https://ieeexplore.ieee.org/abstract/document/8683735},
	urldate = {2023-12-03},
}

@article{greer_multi-modal_2022,
	title = {Multi-modal, {Multi}-task, {Music} {BERT}: {A} {Context}-{Aware} {Music} {Encoder} {Based} on {Transformers}},
	shorttitle = {Multi-modal, {Multi}-task, {Music} {BERT}},
	url = {https://www.researchsquare.com/article/rs-2090671/latest},
	urldate = {2023-12-03},
	author = {Greer, Timothy and Shi, Xuan and Ma, Benjamin and Narayanan, Shrikanth},
	year = {2022},
}

@inproceedings{knox_loss_2021,
	address = {Lille, France},
	title = {Loss {Function} {Approaches} for {Multi}-label {Music} {Tagging}},
	isbn = {978-1-66544-220-6},
	url = {https://ieeexplore.ieee.org/document/9461913/},
	doi = {10.1109/CBMI50038.2021.9461913},
	abstract = {We present USC SAIL’s submission to the 2020 Emotions and Themes in Music challenge: an ensemble-based convolutional neural network (CNN) model trained using various loss functions. In this work, we investigate the effect of different loss functions and resampling strategies on prediction performance, finding that using focal loss improves overall performance on the provided imbalanced, multi-label dataset. Additionally, we report results from varying the receptive field on our base classifier—a CNN-based architecture trained using Mel spectrograms—which also results in better model performance. We conclude that the choice of the loss function is paramount for improving on existing methods in music tagging, particularly in the presence of class imbalance.},
	language = {en},
	urldate = {2023-12-03},
	booktitle = {2021 {International} {Conference} on {Content}-{Based} {Multimedia} {Indexing} ({CBMI})},
	publisher = {IEEE},
	author = {Knox, Dillon and Greer, Timothy and Ma, Benjamin and Kuo, Emily and Somandepalli, Krishna and Narayanan, Shrikanth},
	month = jun,
	year = {2021},
	pages = {1--4},
}

@inproceedings{greer_multimodal_2019,
	address = {New York, NY, USA},
	series = {{MM} '19},
	title = {A {Multimodal} {View} into {Music}'s {Effect} on {Human} {Neural}, {Physiological}, and {Emotional} {Experience}},
	isbn = {978-1-4503-6889-6},
	url = {https://dl.acm.org/doi/10.1145/3343031.3350867},
	doi = {10.1145/3343031.3350867},
	abstract = {Music has a powerful influence on human experience. In this paper, we investigate how music affects brain activity, physiological response, and human-reported behavior. Using auditory features related to dynamics, timbre, harmony, rhythm, and register, we predicted brain activity in the form of phase synchronizations in bilateral Heschl's gyri and superior temporal gyri; physiological response in the form of galvanic skin response and heart activity; and emotional experience in the form of continuous, subjective descriptions reported by music listeners. We found that using multivariate time series models with attention mechanisms are effective in predicting emotional ratings, while vector-autoregressive models are effective in predicting involuntary human responses. Musical features related to dynamics, register, rhythm, and harmony were found to be particularly helpful in predicting these human reactions. This work adds to our understanding of how music affects multimodal human experience and has applications in affective computing, music emotion recognition, neuroscience, and music information retrieval.},
	urldate = {2023-12-03},
	booktitle = {Proceedings of the 27th {ACM} {International} {Conference} on {Multimedia}},
	publisher = {Association for Computing Machinery},
	author = {Greer, Timothy and Ma, Benjamin and Sachs, Matthew and Habibi, Assal and Narayanan, Shrikanth},
	month = oct,
	year = {2019},
	keywords = {multimodal experience, music processing, neural networks},
	pages = {167--175},
}

@inproceedings{greer_multimodal_2019-1,
	address = {Nice France},
	title = {A {Multimodal} {View} into {Music}'s {Effect} on {Human} {Neural}, {Physiological}, and {Emotional} {Experience}},
	isbn = {978-1-4503-6889-6},
	url = {https://dl.acm.org/doi/10.1145/3343031.3350867},
	doi = {10.1145/3343031.3350867},
	language = {en},
	urldate = {2023-12-03},
	booktitle = {Proceedings of the 27th {ACM} {International} {Conference} on {Multimedia}},
	publisher = {ACM},
	author = {Greer, Timothy and Ma, Benjamin and Sachs, Matthew and Habibi, Assal and Narayanan, Shrikanth},
	month = oct,
	year = {2019},
	pages = {167--175},
}

@misc{raffel_exploring_2019,
	title = {Exploring the {Limits} of {Transfer} {Learning} with a {Unified} {Text}-to-{Text} {Transformer}},
	url = {https://arxiv.org/abs/1910.10683v4},
	abstract = {Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new ``Colossal Clean Crawled Corpus'', we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code.},
	language = {en},
	urldate = {2023-12-02},
	journal = {arXiv.org},
	author = {Raffel, Colin and Shazeer, Noam and Roberts, Adam and Lee, Katherine and Narang, Sharan and Matena, Michael and Zhou, Yanqi and Li, Wei and Liu, Peter J.},
	month = oct,
	year = {2019},
}

@misc{kingma_adam_2017,
	title = {Adam: {A} {Method} for {Stochastic} {Optimization}},
	shorttitle = {Adam},
	url = {http://arxiv.org/abs/1412.6980},
	abstract = {We introduce Adam, an algorithm for ﬁrst-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efﬁcient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the inﬁnity norm.},
	language = {en},
	urldate = {2023-12-02},
	publisher = {arXiv},
	author = {Kingma, Diederik P. and Ba, Jimmy},
	month = jan,
	year = {2017},
	note = {arXiv:1412.6980 [cs]},
	keywords = {Computer Science - Machine Learning},
}

@misc{kudo_sentencepiece_2018,
	title = {{SentencePiece}: {A} simple and language independent subword tokenizer and detokenizer for {Neural} {Text} {Processing}},
	shorttitle = {{SentencePiece}},
	url = {http://arxiv.org/abs/1808.06226},
	doi = {10.48550/arXiv.1808.06226},
	abstract = {This paper describes SentencePiece, a language-independent subword tokenizer and detokenizer designed for Neural-based text processing, including Neural Machine Translation. It provides open-source C++ and Python implementations for subword units. While existing subword segmentation tools assume that the input is pre-tokenized into word sequences, SentencePiece can train subword models directly from raw sentences, which allows us to make a purely end-to-end and language independent system. We perform a validation experiment of NMT on English-Japanese machine translation, and find that it is possible to achieve comparable accuracy to direct subword training from raw sentences. We also compare the performance of subword training and segmentation with various configurations. SentencePiece is available under the Apache 2 license at https://github.com/google/sentencepiece.},
	urldate = {2023-12-01},
	publisher = {arXiv},
	author = {Kudo, Taku and Richardson, John},
	month = aug,
	year = {2018},
	note = {arXiv:1808.06226 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@article{shibata_byte_1999,
	title = {Byte {Pair} {Encoding}: {A} {Text} {Compression} {Scheme} {That} {Accelerates} {Pattern} {Matching}},
	shorttitle = {Byte {Pair} {Encoding}},
	abstract = {Byte pair encoding (BPE) is a simple universal text compression scheme. Decompression is very fast and requires small work space. Moreover, it is easy to decompress an arbitrary part of the original text. However, it has not been so popular since the compression is rather slow and the compression ratio is not as good as other methods such as Lempel-Ziv type compression. In this paper, we bring out a potential advantage of BPE compression. We show that it is very suitable from a practical view point of compressed pattern matching, where the goal is to find a pattern directly in compressed text without decompressing it explicitly. We compare running times to find a pattern in (1) BPE compressed files, (2) Lempel-Ziv-Welch compressed files, and (3) original text files, in various situations. Experimental results show that pattern matching in BPE compressed text is even faster than matching in the original text. Thus the BPE compression reduces not only the disk space but also the searchin...},
	author = {Shibata, Yusuke and Kida, Takuya and Fukamachi, Shuichi and Takeda, Masayuki and Shinohara, Ayumi and Shinohara, Takeshi},
	month = sep,
	year = {1999},
}

@misc{chung_empirical_2014,
	title = {Empirical {Evaluation} of {Gated} {Recurrent} {Neural} {Networks} on {Sequence} {Modeling}},
	url = {http://arxiv.org/abs/1412.3555},
	doi = {10.48550/arXiv.1412.3555},
	abstract = {In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). We evaluate these recurrent units on the tasks of polyphonic music modeling and speech signal modeling. Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM.},
	urldate = {2023-11-30},
	publisher = {arXiv},
	author = {Chung, Junyoung and Gulcehre, Caglar and Cho, KyungHyun and Bengio, Yoshua},
	month = dec,
	year = {2014},
	note = {arXiv:1412.3555 [cs]},
	keywords = {Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing},
}

@article{hochreiter_long_1997,
	title = {Long short-term memory},
	volume = {9},
	issn = {0899-7667},
	doi = {10.1162/neco.1997.9.8.1735},
	abstract = {Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.},
	language = {eng},
	number = {8},
	journal = {Neural Computation},
	author = {Hochreiter, S. and Schmidhuber, J.},
	month = nov,
	year = {1997},
	pmid = {9377276},
	keywords = {Algorithms, Learning, Memory, Memory, Short-Term, Models, Neurological, Models, Psychological, Nerve Net, Neural Networks, Computer, Time Factors},
	pages = {1735--1780},
}

@misc{devlin_bert_2019,
	title = {{BERT}: {Pre}-training of {Deep} {Bidirectional} {Transformers} for {Language} {Understanding}},
	shorttitle = {{BERT}},
	url = {http://arxiv.org/abs/1810.04805},
	doi = {10.48550/arXiv.1810.04805},
	abstract = {We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5\% (7.7\% point absolute improvement), MultiNLI accuracy to 86.7\% (4.6\% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).},
	urldate = {2023-11-30},
	publisher = {arXiv},
	author = {Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
	month = may,
	year = {2019},
	note = {arXiv:1810.04805 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{vaswani_attention_2023,
	title = {Attention {Is} {All} {You} {Need}},
	url = {http://arxiv.org/abs/1706.03762},
	doi = {10.48550/arXiv.1706.03762},
	abstract = {The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.},
	urldate = {2023-11-30},
	publisher = {arXiv},
	author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
	month = aug,
	year = {2023},
	note = {arXiv:1706.03762 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{noauthor_chatgpt_nodate,
	title = {{ChatGPT}},
	url = {https://chat.openai.com/c/da233f77-01b1-4dfe-af5b-a4d82f8a728a},
	urldate = {2023-11-30},
}

@misc{plantec_flow-lenia_2023,
	title = {Flow-{Lenia}: {Towards} open-ended evolution in cellular automata through mass conservation and parameter localization},
	shorttitle = {Flow-{Lenia}},
	url = {http://arxiv.org/abs/2212.07906},
	doi = {10.48550/arXiv.2212.07906},
	abstract = {The design of complex self-organising systems producing life-like phenomena, such as the open-ended evolution of virtual creatures, is one of the main goals of artificial life. Lenia, a family of cellular automata (CA) generalizing Conway's Game of Life to continuous space, time and states, has attracted a lot of attention because of the wide diversity of self-organizing patterns it can generate. Among those, some spatially localized patterns (SLPs) resemble life-like artificial creatures and display complex behaviors. However, those creatures are found in only a small subspace of the Lenia parameter space and are not trivial to discover, necessitating advanced search algorithms. Furthermore, each of these creatures exist only in worlds governed by specific update rules and thus cannot interact in the same one. This paper proposes as mass-conservative extension of Lenia, called Flow Lenia, that solve both of these issues. We present experiments demonstrating its effectiveness in generating SLPs with complex behaviors and show that the update rule parameters can be optimized to generate SLPs showing behaviors of interest. Finally, we show that Flow Lenia enables the integration of the parameters of the CA update rules within the CA dynamics, making them dynamic and localized, allowing for multi-species simulations, with locally coherent update rules that define properties of the emerging creatures, and that can be mixed with neighbouring rules. We argue that this paves the way for the intrinsic evolution of self-organized artificial life forms within continuous CAs.},
	urldate = {2023-11-29},
	publisher = {arXiv},
	author = {Plantec, Erwan and Hamon, Gautier and Etcheverry, Mayalen and Oudeyer, Pierre-Yves and Moulin-Frier, Clément and Chan, Bert Wang-Chak},
	month = mar,
	year = {2023},
	note = {arXiv:2212.07906 [nlin]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing, Nonlinear Sciences - Cellular Automata and Lattice Gases},
}

@article{huang_mulan_nodate,
	title = {{MULAN}: {A} {JOINT} {EMBEDDING} {OF} {MUSIC} {AUDIO} {AND} {NATURAL} {LANGUAGE}},
	abstract = {Music tagging and content-based retrieval systems have traditionally been constructed using pre-deﬁned ontologies covering a rigid set of music attributes or text queries. This paper presents MuLan: a ﬁrst attempt at a new generation of acoustic models that link music audio directly to unconstrained natural language music descriptions. MuLan takes the form of a two-tower, joint audio-text embedding model trained using 44 million music recordings (370K hours) and weakly-associated, free-form text annotations. Through its compatibility with a wide range of music genres and text styles (including conventional music tags), the resulting audio-text representation subsumes existing ontologies while graduating to true zero-shot functionalities. We demonstrate the versatility of the MuLan embeddings with a range of experiments including transfer learning, zero-shot music tagging, language understanding in the music domain, and cross-modal retrieval applications.},
	language = {en},
	author = {Huang, Qingqing and Jansen, Aren and Lee, Joonseok},
}

@misc{noauthor_papers_nodate-5,
	title = {Papers with {Code} - {Paper} tables with annotated results for {MuLan}: {A} {Joint} {Embedding} of {Music} {Audio} and {Natural} {Language}},
	shorttitle = {Papers with {Code} - {Paper} tables with annotated results for {MuLan}},
	url = {https://paperswithcode.com/paper/mulan-a-joint-embedding-of-music-audio-and/review/},
	abstract = {Paper tables with annotated results for MuLan: A Joint Embedding of Music Audio and Natural Language},
	language = {en},
	urldate = {2023-11-27},
}

@misc{vaswani_attention_2023-1,
	title = {Attention {Is} {All} {You} {Need}},
	url = {http://arxiv.org/abs/1706.03762},
	doi = {10.48550/arXiv.1706.03762},
	abstract = {The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.},
	urldate = {2023-11-26},
	publisher = {arXiv},
	author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
	month = aug,
	year = {2023},
	note = {arXiv:1706.03762 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{ho_imagen_2022,
	title = {Imagen {Video}: {High} {Definition} {Video} {Generation} with {Diffusion} {Models}},
	shorttitle = {Imagen {Video}},
	url = {http://arxiv.org/abs/2210.02303},
	doi = {10.48550/arXiv.2210.02303},
	abstract = {We present Imagen Video, a text-conditional video generation system based on a cascade of video diffusion models. Given a text prompt, Imagen Video generates high definition videos using a base video generation model and a sequence of interleaved spatial and temporal video super-resolution models. We describe how we scale up the system as a high definition text-to-video model including design decisions such as the choice of fully-convolutional temporal and spatial super-resolution models at certain resolutions, and the choice of the v-parameterization of diffusion models. In addition, we confirm and transfer findings from previous work on diffusion-based image generation to the video generation setting. Finally, we apply progressive distillation to our video models with classifier-free guidance for fast, high quality sampling. We find Imagen Video not only capable of generating videos of high fidelity, but also having a high degree of controllability and world knowledge, including the ability to generate diverse videos and text animations in various artistic styles and with 3D object understanding. See https://imagen.research.google/video/ for samples.},
	urldate = {2023-11-24},
	publisher = {arXiv},
	author = {Ho, Jonathan and Chan, William and Saharia, Chitwan and Whang, Jay and Gao, Ruiqi and Gritsenko, Alexey and Kingma, Diederik P. and Poole, Ben and Norouzi, Mohammad and Fleet, David J. and Salimans, Tim},
	month = oct,
	year = {2022},
	note = {arXiv:2210.02303 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@inproceedings{meng_distillation_2023,
	title = {On {Distillation} of {Guided} {Diffusion} {Models}},
	url = {https://openaccess.thecvf.com/content/CVPR2023/html/Meng_On_Distillation_of_Guided_Diffusion_Models_CVPR_2023_paper.html},
	language = {en},
	urldate = {2023-11-24},
	author = {Meng, Chenlin and Rombach, Robin and Gao, Ruiqi and Kingma, Diederik and Ermon, Stefano and Ho, Jonathan and Salimans, Tim},
	year = {2023},
	pages = {14297--14306},
}

@patent{kingma_diffusion_2023,
	title = {Diffusion {Models} {Having} {Improved} {Accuracy} and {Reduced} {Consumption} of {Computational} {Resources}},
	url = {https://patents.google.com/patent/US20230267315A1/en},
	nationality = {US},
	assignee = {Google LLC},
	number = {US20230267315A1},
	urldate = {2023-11-24},
	author = {Kingma, Diederik Pieter and Salimans, Tim},
	month = aug,
	year = {2023},
	keywords = {data, diffusion, learned, model, noising},
}

@misc{kingma_adam_2017-1,
	title = {Adam: {A} {Method} for {Stochastic} {Optimization}},
	shorttitle = {Adam},
	url = {http://arxiv.org/abs/1412.6980},
	doi = {10.48550/arXiv.1412.6980},
	abstract = {We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.},
	urldate = {2023-11-24},
	publisher = {arXiv},
	author = {Kingma, Diederik P. and Ba, Jimmy},
	month = jan,
	year = {2017},
	note = {arXiv:1412.6980 [cs]},
	keywords = {Computer Science - Machine Learning},
}

@misc{pasini_musika_2022,
	title = {Musika! {Fast} {Infinite} {Waveform} {Music} {Generation}},
	url = {http://arxiv.org/abs/2208.08706},
	doi = {10.48550/arXiv.2208.08706},
	abstract = {Fast and user-controllable music generation could enable novel ways of composing or performing music. However, state-of-the-art music generation systems require large amounts of data and computational resources for training, and are slow at inference. This makes them impractical for real-time interactive use. In this work, we introduce Musika, a music generation system that can be trained on hundreds of hours of music using a single consumer GPU, and that allows for much faster than real-time generation of music of arbitrary length on a consumer CPU. We achieve this by first learning a compact invertible representation of spectrogram magnitudes and phases with adversarial autoencoders, then training a Generative Adversarial Network (GAN) on this representation for a particular music domain. A latent coordinate system enables generating arbitrarily long sequences of excerpts in parallel, while a global context vector allows the music to remain stylistically coherent through time. We perform quantitative evaluations to assess the quality of the generated samples and showcase options for user control in piano and techno music generation. We release the source code and pretrained autoencoder weights at github.com/marcoppasini/musika, such that a GAN can be trained on a new music domain with a single GPU in a matter of hours.},
	urldate = {2023-11-24},
	publisher = {arXiv},
	author = {Pasini, Marco and Schlüter, Jan},
	month = aug,
	year = {2022},
	note = {arXiv:2208.08706 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{noauthor_huggingfacediffusers_nodate,
	title = {huggingface/diffusers: �� {Diffusers}: {State}-of-the-art diffusion models for image and audio generation in {PyTorch}},
	url = {https://github.com/huggingface/diffusers},
	urldate = {2023-11-23},
}

@misc{noauthor__2023,
	title = {�� {Riffusion}},
	copyright = {MIT},
	url = {https://github.com/riffusion/riffusion},
	abstract = {Stable diffusion for real-time music generation},
	urldate = {2023-11-23},
	publisher = {Riffusion},
	month = nov,
	year = {2023},
	note = {original-date: 2022-11-25T21:16:13Z},
	keywords = {ai, audio, diffusers, diffusion, music, stable-diffusion},
}

@article{jespersen_music_2015,
	title = {Music for insomnia in adults},
	volume = {2015},
	issn = {1469-493X},
	doi = {10.1002/14651858.CD010459.pub2},
	abstract = {BACKGROUND: Insomnia is a common sleep disorder in modern society. It causes reduced quality of life and is associated with impairments in physical and mental health. Listening to music is widely used as a sleep aid, but it remains unclear if it can actually improve insomnia in adults.
OBJECTIVES: To assess the effects of listening to music on insomnia in adults and to assess the influence of specific variables that may moderate the effect.
SEARCH METHODS: We searched CENTRAL, PubMed, Embase, nine other databases and two trials registers in May 2015. In addition, we handsearched specific music therapy journals, reference lists of included studies, and contacted authors of published studies to identify additional studies eligible for inclusion, including any unpublished or ongoing trials.
SELECTION CRITERIA: Randomised controlled trials and quasi-randomised controlled trials that compared the effects of listening to music with no treatment or treatment-as-usual on sleep improvement in adults with insomnia.
DATA COLLECTION AND ANALYSIS: Two authors independently screened abstracts, selected studies, assessed risk of bias, and extracted data from all studies eligible for inclusion. Data on pre-defined outcome measures were subjected to meta-analyses when consistently reported by at least two studies. We undertook meta-analyses using both fixed-effect and random-effects models. Heterogeneity across included studies was assessed using the I² statistic.
MAIN RESULTS: We included six studies comprising a total of 314 participants. The studies examined the effect of listening to pre-recorded music daily, for 25 to 60 minutes, for a period of three days to five weeks.Based on the Grades of Recommendations, Assessment, Development and Evaluation (GRADE) approach, we judged the evidence from five studies that measured the effect of music listening on sleep quality to be of moderate quality. We judged the evidence from one study that examined other aspects of sleep (see below) to be of low quality. We downgraded the quality of the evidence mainly because of limitations in design or being the only published study. As regards risk of bias, most studies were at high risk of bias on at least one domain: one study was at high risk of selection bias and one was judged to be at unclear risk; six studies were at high risk of performance bias; three studies were at high risk of detection bias; one study was at high risk of attrition bias and was study was judged to be at unclear risk; two studies were judged to be at unclear risk of reporting bias; and four studies were at high risk of other bias.Five studies (N = 264) reporting on sleep quality as assessed by the Pittsburgh Sleep Quality Index (PSQI) were included in the meta-analysis. The results of a random-effects meta-analysis revealed an effect in favour of music listening (mean difference (MD) -2.80; 95\% confidence interval (CI) -3.42 to -2.17; Z = 8.77, P {\textless} 0.00001; moderate-quality evidence). The size of the effect indicates an increase in sleep quality of the size of about one standard deviation in favour of the intervention compared to no treatment or treatment-as-usual.Only one study (N = 50; low-quality evidence) reported data on sleep onset latency, total sleep time, sleep interruption, and sleep efficiency. However, It found no evidence to suggest that the intervention benefited these outcomes. None of the included studies reported any adverse events.
AUTHORS' CONCLUSIONS: The findings of this review provide evidence that music may be effective for improving subjective sleep quality in adults with insomnia symptoms. The intervention is safe and easy to administer. More research is needed to establish the effect of listening to music on other aspects of sleep as well as the daytime consequences of insomnia.},
	language = {eng},
	number = {8},
	journal = {The Cochrane Database of Systematic Reviews},
	author = {Jespersen, Kira V. and Koenig, Julian and Jennum, Poul and Vuust, Peter},
	month = aug,
	year = {2015},
	pmid = {26270746},
	pmcid = {PMC9241357},
	keywords = {Adolescent, Adult, Aged, Aged, 80 and over, Female, Humans, Male, Middle Aged, Music, Music Therapy, Randomized Controlled Trials as Topic, Sleep, Sleep Initiation and Maintenance Disorders, Time Factors},
	pages = {CD010459},
}

@article{oxtoby_impact_2013,
	title = {The impact of relaxing music on insomnia-related thoughts and behaviours},
	volume = {24},
	copyright = {free\_to\_read},
	issn = {1036-9457},
	abstract = {Insomnia is a pervasive problem involving poor sleep quality and quantity. Previous research has suggested that music listening can help alleviate insomnia, but exactly how music helps sleep problems has not been determined. A greater understanding of these processes could help practitioners to design more effective music-based insomnia treatments. This randomised controlled trial was designed to assess the influences of nightly music listening on the sleep-related thoughts and behaviours described in Harvey’s (2002) cognitive model of insomnia maintenance. University students, including a range of good and poor sleepers, were randomly assigned to a music listening group or a control group and were assessed before and after a two-week music listening intervention. Measures included a range of self-report scales, each assessing an element of Harvey’s cognitive model. During the intervention, the music listening group was asked to listen to provided music for at least 20 minutes each night. The control group was asked to maintain their regular nightly routines. Results indicated that the music listening group significantly improved on most of the factors theorised to influence sleep quality, although their actual sleep quality did not significantly improve. The control group did not change significantly on any measures. The results of this study suggest that music listening can have positive impacts on a range of factors theorised to influence sleep quality. However, as the music was not shown to actually improve sleep quality, Harvey’s cognitive model explanation of music’s effect on sleep quality may require further investigation.},
	language = {en},
	urldate = {2023-11-23},
	journal = {Australian Journal of Music Therapy},
	author = {Oxtoby, Jim and Sacre, Sandy and Lurie, Janine},
	year = {2013},
	note = {Publisher: Australian Music Therapy Association Inc.},
	pages = {67--86},
}

@article{sulun_symbolic_2022,
	title = {Symbolic music generation conditioned on continuous-valued emotions},
	volume = {10},
	issn = {2169-3536},
	url = {http://arxiv.org/abs/2203.16165},
	doi = {10.1109/ACCESS.2022.3169744},
	abstract = {In this paper we present a new approach for the generation of multi-instrument symbolic music driven by musical emotion. The principal novelty of our approach centres on conditioning a state-of-the-art transformer based on continuous-valued valence and arousal labels. In addition, we provide a new large-scale dataset of symbolic music paired with emotion labels in terms of valence and arousal. We evaluate our approach in a quantitative manner in two ways, first by measuring its note prediction accuracy, and second via a regression task in the valence-arousal plane. Our results demonstrate that our proposed approaches outperform conditioning using control tokens which is representative of the current state of the art.},
	urldate = {2023-11-23},
	journal = {IEEE Access},
	author = {Sulun, Serkan and Davies, Matthew E. P. and Viana, Paula},
	year = {2022},
	note = {arXiv:2203.16165 [cs, eess]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Multimedia, Electrical Engineering and Systems Science - Audio and Speech Processing},
	pages = {44617--44626},
}

@misc{huang_make--audio_2023,
	title = {Make-{An}-{Audio}: {Text}-{To}-{Audio} {Generation} with {Prompt}-{Enhanced} {Diffusion} {Models}},
	shorttitle = {Make-{An}-{Audio}},
	url = {http://arxiv.org/abs/2301.12661},
	doi = {10.48550/arXiv.2301.12661},
	abstract = {Large-scale multimodal generative modeling has created milestones in text-to-image and text-to-video generation. Its application to audio still lags behind for two main reasons: the lack of large-scale datasets with high-quality text-audio pairs, and the complexity of modeling long continuous audio data. In this work, we propose Make-An-Audio with a prompt-enhanced diffusion model that addresses these gaps by 1) introducing pseudo prompt enhancement with a distill-then-reprogram approach, it alleviates data scarcity with orders of magnitude concept compositions by using language-free audios; 2) leveraging spectrogram autoencoder to predict the self-supervised audio representation instead of waveforms. Together with robust contrastive language-audio pretraining (CLAP) representations, Make-An-Audio achieves state-of-the-art results in both objective and subjective benchmark evaluation. Moreover, we present its controllability and generalization for X-to-Audio with "No Modality Left Behind", for the first time unlocking the ability to generate high-definition, high-fidelity audios given a user-defined modality input. Audio samples are available at https://Text-to-Audio.github.io},
	urldate = {2023-11-23},
	publisher = {arXiv},
	author = {Huang, Rongjie and Huang, Jiawei and Yang, Dongchao and Ren, Yi and Liu, Luping and Li, Mingze and Ye, Zhenhui and Liu, Jinglin and Yin, Xiang and Zhao, Zhou},
	month = jan,
	year = {2023},
	note = {arXiv:2301.12661 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{oord_wavenet_2016,
	title = {{WaveNet}: {A} {Generative} {Model} for {Raw} {Audio}},
	shorttitle = {{WaveNet}},
	url = {http://arxiv.org/abs/1609.03499},
	doi = {10.48550/arXiv.1609.03499},
	abstract = {This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonetheless we show that it can be efficiently trained on data with tens of thousands of samples per second of audio. When applied to text-to-speech, it yields state-of-the-art performance, with human listeners rating it as significantly more natural sounding than the best parametric and concatenative systems for both English and Mandarin. A single WaveNet can capture the characteristics of many different speakers with equal fidelity, and can switch between them by conditioning on the speaker identity. When trained to model music, we find that it generates novel and often highly realistic musical fragments. We also show that it can be employed as a discriminative model, returning promising results for phoneme recognition.},
	urldate = {2023-11-23},
	publisher = {arXiv},
	author = {Oord, Aaron van den and Dieleman, Sander and Zen, Heiga and Simonyan, Karen and Vinyals, Oriol and Graves, Alex and Kalchbrenner, Nal and Senior, Andrew and Kavukcuoglu, Koray},
	month = sep,
	year = {2016},
	note = {arXiv:1609.03499 [cs]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound},
}

@misc{liu_audioldm_2023,
	title = {{AudioLDM}: {Text}-to-{Audio} {Generation} with {Latent} {Diffusion} {Models}},
	shorttitle = {{AudioLDM}},
	url = {http://arxiv.org/abs/2301.12503},
	doi = {10.48550/arXiv.2301.12503},
	abstract = {Text-to-audio (TTA) system has recently gained attention for its ability to synthesize general audio based on text descriptions. However, previous studies in TTA have limited generation quality with high computational costs. In this study, we propose AudioLDM, a TTA system that is built on a latent space to learn the continuous audio representations from contrastive language-audio pretraining (CLAP) latents. The pretrained CLAP models enable us to train LDMs with audio embedding while providing text embedding as a condition during sampling. By learning the latent representations of audio signals and their compositions without modeling the cross-modal relationship, AudioLDM is advantageous in both generation quality and computational efficiency. Trained on AudioCaps with a single GPU, AudioLDM achieves state-of-the-art TTA performance measured by both objective and subjective metrics (e.g., frechet distance). Moreover, AudioLDM is the first TTA system that enables various text-guided audio manipulations (e.g., style transfer) in a zero-shot fashion. Our implementation and demos are available at https://audioldm.github.io.},
	urldate = {2023-11-23},
	publisher = {arXiv},
	author = {Liu, Haohe and Chen, Zehua and Yuan, Yi and Mei, Xinhao and Liu, Xubo and Mandic, Danilo and Wang, Wenwu and Plumbley, Mark D.},
	month = sep,
	year = {2023},
	note = {arXiv:2301.12503 [cs, eess]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Electrical Engineering and Systems Science - Signal Processing},
}

@misc{li_jen-1_2023,
	title = {{JEN}-1: {Text}-{Guided} {Universal} {Music} {Generation} with {Omnidirectional} {Diffusion} {Models}},
	shorttitle = {{JEN}-1},
	url = {http://arxiv.org/abs/2308.04729},
	doi = {10.48550/arXiv.2308.04729},
	abstract = {Music generation has attracted growing interest with the advancement of deep generative models. However, generating music conditioned on textual descriptions, known as text-to-music, remains challenging due to the complexity of musical structures and high sampling rate requirements. Despite the task's significance, prevailing generative models exhibit limitations in music quality, computational efficiency, and generalization. This paper introduces JEN-1, a universal high-fidelity model for text-to-music generation. JEN-1 is a diffusion model incorporating both autoregressive and non-autoregressive training. Through in-context learning, JEN-1 performs various generation tasks including text-guided music generation, music inpainting, and continuation. Evaluations demonstrate JEN-1's superior performance over state-of-the-art methods in text-music alignment and music quality while maintaining computational efficiency. Our demos are available at http://futureverse.com/research/jen/demos/jen1},
	urldate = {2023-11-23},
	publisher = {arXiv},
	author = {Li, Peike and Chen, Boyu and Yao, Yao and Wang, Yikai and Wang, Allen and Wang, Alex},
	month = aug,
	year = {2023},
	note = {arXiv:2308.04729 [cs, eess]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@article{gavrilova_real-time_2011,
	title = {Real-{Time} {EEG}-{Based} {Emotion} {Recognition} and {Its} {Applications}},
	volume = {6670},
	url = {http://link.springer.com/10.1007/978-3-642-22336-5_13},
	doi = {10.1007/978-3-642-22336-5_13},
	abstract = {Since emotions play an important role in the daily life of human beings, the need and importance of automatic emotion recognition has grown with increasing role of human computer interface applications. Emotion recognition could be done from the text, speech, facial expression or gesture. In this paper, we concentrate on recognition of "inner" emotions from electroencephalogram (EEG) signals. We propose real-time fractal dimension based algorithm of quantification of basic emotions using Arousal-Valence emotion model. Two emotion induction experiments with music stimuli and sound stimuli from International Affective Digitized Sounds (IADS) database were proposed and implemented. Finally, the real-time algorithm was proposed, implemented and tested to recognize six emotions such as fear, frustrated, sad, happy, pleasant and satisfied. Real-time applications were proposed and implemented in 3D virtual environments. The user emotions are recognized and visualized in real time on his/her avatar adding one more so-called "emotion dimension" to human computer interfaces. An EEG-enabled music therapy site was proposed and implemented. The music played to the patients helps them deal with problems such as pain and depression. An EEG-based web-enable music player which can display the music according to the user's current emotion states was designed and implemented.},
	urldate = {2023-11-23},
	author = {Liu, Yisi and Sourina, Olga and Nguyen, Minh Khoa},
	editor = {Gavrilova, Marina L. and Tan, C. J. Kenneth and Sourin, Alexei and Sourina, Olga},
	year = {2011},
	doi = {10.1007/978-3-642-22336-5_13},
	note = {Book Title: Transactions on Computational Science XII
ISBN: 9783642223358 9783642223365
Place: Berlin, Heidelberg
Publisher: Springer Berlin Heidelberg},
	pages = {256--277},
}

@inproceedings{kana_music_2020,
	title = {Music generation and emotion estimation from {EEG} for inducing affective states},
	url = {https://www.semanticscholar.org/paper/Music-generation-and-emotion-estimation-from-EEG-Kana-Hiroki/c45d6ec4fda5b4fbb2e48135ab81e6a251d3dca1},
	abstract = {We propose a music generation feedback system that compares the user’s emotion estimated by EEG with the desired emotion. First, we introduced and evaluated a music generator used for emotion induction. The 108 cloud workers evaluated emotion felt by listening to the music generated by the music generator. We recorded EEG and emotion while listening to music and estimated emotion using linear regression models and ensemble regression models.},
	urldate = {2023-11-23},
	author = {Kana, Miyamoto and Hiroki, Tanaka and Satoshi, Nakamura},
	month = may,
	year = {2020},
}

@misc{yang_diffsound_2023,
	title = {Diffsound: {Discrete} {Diffusion} {Model} for {Text}-to-sound {Generation}},
	shorttitle = {Diffsound},
	url = {http://arxiv.org/abs/2207.09983},
	doi = {10.48550/arXiv.2207.09983},
	abstract = {Generating sound effects that humans want is an important topic. However, there are few studies in this area for sound generation. In this study, we investigate generating sound conditioned on a text prompt and propose a novel text-to-sound generation framework that consists of a text encoder, a Vector Quantized Variational Autoencoder (VQ-VAE), a decoder, and a vocoder. The framework first uses the decoder to transfer the text features extracted from the text encoder to a mel-spectrogram with the help of VQ-VAE, and then the vocoder is used to transform the generated mel-spectrogram into a waveform. We found that the decoder significantly influences the generation performance. Thus, we focus on designing a good decoder in this study. We begin with the traditional autoregressive decoder, which has been proved as a state-of-the-art method in previous sound generation works. However, the AR decoder always predicts the mel-spectrogram tokens one by one in order, which introduces the unidirectional bias and accumulation of errors problems. Moreover, with the AR decoder, the sound generation time increases linearly with the sound duration. To overcome the shortcomings introduced by AR decoders, we propose a non-autoregressive decoder based on the discrete diffusion model, named Diffsound. Specifically, the Diffsound predicts all of the mel-spectrogram tokens in one step and then refines the predicted tokens in the next step, so the best-predicted results can be obtained after several steps. Our experiments show that our proposed Diffsound not only produces better text-to-sound generation results when compared with the AR decoder but also has a faster generation speed, e.g., MOS: 3.56 {\textbackslash}textit\{v.s\} 2.786, and the generation speed is five times faster than the AR decoder.},
	urldate = {2023-11-23},
	publisher = {arXiv},
	author = {Yang, Dongchao and Yu, Jianwei and Wang, Helin and Wang, Wen and Weng, Chao and Zou, Yuexian and Yu, Dong},
	month = apr,
	year = {2023},
	note = {arXiv:2207.09983 [cs, eess]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{huang_mulan_2022,
	title = {{MuLan}: {A} {Joint} {Embedding} of {Music} {Audio} and {Natural} {Language}},
	shorttitle = {{MuLan}},
	url = {http://arxiv.org/abs/2208.12415},
	doi = {10.48550/arXiv.2208.12415},
	abstract = {Music tagging and content-based retrieval systems have traditionally been constructed using pre-defined ontologies covering a rigid set of music attributes or text queries. This paper presents MuLan: a first attempt at a new generation of acoustic models that link music audio directly to unconstrained natural language music descriptions. MuLan takes the form of a two-tower, joint audio-text embedding model trained using 44 million music recordings (370K hours) and weakly-associated, free-form text annotations. Through its compatibility with a wide range of music genres and text styles (including conventional music tags), the resulting audio-text representation subsumes existing ontologies while graduating to true zero-shot functionalities. We demonstrate the versatility of the MuLan embeddings with a range of experiments including transfer learning, zero-shot music tagging, language understanding in the music domain, and cross-modal retrieval applications.},
	urldate = {2023-11-22},
	publisher = {arXiv},
	author = {Huang, Qingqing and Jansen, Aren and Lee, Joonseok and Ganti, Ravi and Li, Judith Yue and Ellis, Daniel P. W.},
	month = aug,
	year = {2022},
	note = {arXiv:2208.12415 [cs, eess, stat]},
	keywords = {Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning},
}

@misc{kilgour_frechet_2019,
	title = {Fr{\textbackslash}'echet {Audio} {Distance}: {A} {Metric} for {Evaluating} {Music} {Enhancement} {Algorithms}},
	shorttitle = {Fr{\textbackslash}'echet {Audio} {Distance}},
	url = {http://arxiv.org/abs/1812.08466},
	doi = {10.48550/arXiv.1812.08466},
	abstract = {We propose the Fr{\textbackslash}'echet Audio Distance (FAD), a novel, reference-free evaluation metric for music enhancement algorithms. We demonstrate how typical evaluation metrics for speech enhancement and blind source separation can fail to accurately measure the perceived effect of a wide variety of distortions. As an alternative, we propose adapting the Fr{\textbackslash}'echet Inception Distance (FID) metric used to evaluate generative image models to the audio domain. FAD is validated using a wide variety of artificial distortions and is compared to the signal based metrics signal to distortion ratio (SDR), cosine distance and magnitude L2 distance. We show that, with a correlation coefficient of 0.52, FAD correlates more closely with human perception than either SDR, cosine distance or magnitude L2 distance, with correlation coefficients of 0.39, -0.15 and -0.01 respectively.},
	urldate = {2023-11-22},
	publisher = {arXiv},
	author = {Kilgour, Kevin and Zuluaga, Mauricio and Roblek, Dominik and Sharifi, Matthew},
	month = jan,
	year = {2019},
	note = {arXiv:1812.08466 [cs, eess]},
	keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{sohl-dickstein_deep_2015,
	title = {Deep {Unsupervised} {Learning} using {Nonequilibrium} {Thermodynamics}},
	url = {http://arxiv.org/abs/1503.03585},
	doi = {10.48550/arXiv.1503.03585},
	abstract = {A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm.},
	urldate = {2023-11-22},
	publisher = {arXiv},
	author = {Sohl-Dickstein, Jascha and Weiss, Eric A. and Maheswaranathan, Niru and Ganguli, Surya},
	month = nov,
	year = {2015},
	note = {arXiv:1503.03585 [cond-mat, q-bio, stat]},
	keywords = {Computer Science - Machine Learning, Condensed Matter - Disordered Systems and Neural Networks, Quantitative Biology - Neurons and Cognition, Statistics - Machine Learning},
}

@misc{song_generative_2020,
	title = {Generative {Modeling} by {Estimating} {Gradients} of the {Data} {Distribution}},
	url = {http://arxiv.org/abs/1907.05600},
	doi = {10.48550/arXiv.1907.05600},
	abstract = {We introduce a new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching. Because gradients can be ill-defined and hard to estimate when the data resides on low-dimensional manifolds, we perturb the data with different levels of Gaussian noise, and jointly estimate the corresponding scores, i.e., the vector fields of gradients of the perturbed data distribution for all noise levels. For sampling, we propose an annealed Langevin dynamics where we use gradients corresponding to gradually decreasing noise levels as the sampling process gets closer to the data manifold. Our framework allows flexible model architectures, requires no sampling during training or the use of adversarial methods, and provides a learning objective that can be used for principled model comparisons. Our models produce samples comparable to GANs on MNIST, CelebA and CIFAR-10 datasets, achieving a new state-of-the-art inception score of 8.87 on CIFAR-10. Additionally, we demonstrate that our models learn effective representations via image inpainting experiments.},
	urldate = {2023-11-22},
	publisher = {arXiv},
	author = {Song, Yang and Ermon, Stefano},
	month = oct,
	year = {2020},
	note = {arXiv:1907.05600 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{dhariwal_jukebox_2020,
	title = {Jukebox: {A} {Generative} {Model} for {Music}},
	shorttitle = {Jukebox},
	url = {http://arxiv.org/abs/2005.00341},
	doi = {10.48550/arXiv.2005.00341},
	abstract = {We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multi-scale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples at https://jukebox.openai.com, along with model weights and code at https://github.com/openai/jukebox},
	urldate = {2023-11-22},
	publisher = {arXiv},
	author = {Dhariwal, Prafulla and Jun, Heewoo and Payne, Christine and Kim, Jong Wook and Radford, Alec and Sutskever, Ilya},
	month = apr,
	year = {2020},
	note = {arXiv:2005.00341 [cs, eess, stat]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning},
}

@misc{noauthor_pdf_nodate,
	title = {[{PDF}] {Music} that is used while studying and music that is used for sleep share similar musical features, genres and subgroups {\textbar} {Semantic} {Scholar}},
	url = {https://www.semanticscholar.org/paper/Music-that-is-used-while-studying-and-music-that-is-Scarratt-Heggli/8e6e6e3fdce362796bd1de37f333e30c2c6d74d8},
	urldate = {2023-11-22},
}

@misc{saharia_palette_2022,
	title = {Palette: {Image}-to-{Image} {Diffusion} {Models}},
	shorttitle = {Palette},
	url = {http://arxiv.org/abs/2111.05826},
	doi = {10.48550/arXiv.2111.05826},
	abstract = {This paper develops a unified framework for image-to-image translation based on conditional diffusion models and evaluates this framework on four challenging image-to-image translation tasks, namely colorization, inpainting, uncropping, and JPEG restoration. Our simple implementation of image-to-image diffusion models outperforms strong GAN and regression baselines on all tasks, without task-specific hyper-parameter tuning, architecture customization, or any auxiliary loss or sophisticated new techniques needed. We uncover the impact of an L2 vs. L1 loss in the denoising diffusion objective on sample diversity, and demonstrate the importance of self-attention in the neural architecture through empirical studies. Importantly, we advocate a unified evaluation protocol based on ImageNet, with human evaluation and sample quality scores (FID, Inception Score, Classification Accuracy of a pre-trained ResNet-50, and Perceptual Distance against original images). We expect this standardized evaluation protocol to play a role in advancing image-to-image translation research. Finally, we show that a generalist, multi-task diffusion model performs as well or better than task-specific specialist counterparts. Check out https://diffusion-palette.github.io for an overview of the results.},
	urldate = {2023-11-22},
	publisher = {arXiv},
	author = {Saharia, Chitwan and Chan, William and Chang, Huiwen and Lee, Chris A. and Ho, Jonathan and Salimans, Tim and Fleet, David J. and Norouzi, Mohammad},
	month = may,
	year = {2022},
	note = {arXiv:2111.05826 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@article{cochrane_developmental_psychosocial_and_learning_problems_group_listening_2022,
	title = {Listening to music for insomnia in adults},
	volume = {2022},
	issn = {14651858},
	url = {http://doi.wiley.com/10.1002/14651858.CD010459.pub3},
	doi = {10.1002/14651858.CD010459.pub3},
	abstract = {BACKGROUND
Insomnia is a common problem in modern society. It is associated with reduced quality of life and impairments in physical and mental health. Listening to music is widely used as a sleep aid, but it remains unclear if it can actually improve insomnia in adults. This Cochrane Review is an update of a review published in 2015.


OBJECTIVES
To assess the effects of listening to music on sleep in adults with insomnia and to assess the influence of specific variables that may moderate the effect.


SEARCH METHODS
For this update, we searched CENTRAL, MEDLINE, Embase, nine other databases and two trials registers up to December 2021. In addition, we handsearched reference lists of included studies, and contacted authors of published studies to identify additional studies eligible for inclusion, including any unpublished or ongoing trials.


SELECTION CRITERIA
Randomised controlled trials comparing the effects of listening to music with no treatment or treatment as usual (TAU) in adults complaining of sleep difficulties.


DATA COLLECTION AND ANALYSIS
Two review authors independently screened records for eligibility, selected studies for inclusion, extracted data and assessed risk of bias of the included studies. We assessed the certainty of the evidence using GRADE. The primary outcomes were sleep quality, insomnia severity, sleep-onset latency, total sleep time, sleep interruption, sleep efficiency and adverse events. Data on the predefined outcome measures were included in meta-analyses when consistently reported by at least two studies that were homogeneous in terms of participants, interventions and outcomes. We undertook meta-analyses using random-effects models.


MAIN RESULTS
We included 13 studies (eight studies new to this update) comprising 1007 participants. The studies examined the effect of listening to prerecorded music daily, for 25 to 60 minutes, for a period of three days to three months. The risk of bias within the studies varied, with all studies being at high risk of performance bias, because of limited possibilities to blind participants to the music intervention. Some studies were at high risk of detection bias or other bias. Four studies reported funding from national research councils, three studies reported financial support from university sources and one study reported a grant from a private foundation. Five studies did not report any financial support. At the end of the intervention, we found moderate-certainty evidence for improved sleep quality measured with the Pittsburgh Sleep Quality Index (PSQI) in themusic groups compared to no intervention or TAU (mean difference (MD) -2.79, 95\% confidence interval (CI) -3.86 to -1.72; 10 studies, 708 participants). The PSQI scale ranges from 0 to 21 with higher scores indicating poorer sleep. The size of the effect indicates an increase in sleep quality of the size of about one standard deviation in favour of the intervention. We found no clear evidence of a difference in the effects of listening to music compared to no treatment or TAU on insomnia severity (MD -6.96, 95\% CI -15.21 to 1.28; 2 studies, 63 participants; very low-certainty evidence). We found low-certainty evidence that, compared to no treatment or TAU, listening to music may reduce problems with sleep-onset latency (MD -0.60, 95\% CI -0.83 to -0.37; 3 studies, 197 participants), total sleep time (MD -0.69, 95\% CI -1.16 to -0.23; 3 studies, 197 participants) and sleep efficiency (MD -0.96, 95\% CI -1.38 to -0.54; 3 studies, 197 participants), but may have no effect on perceived sleep interruption (MD -0.53, 95\% CI -1.47 to 0.40; 3 studies, 197 participants). In addition, three studies (136 participants) included objective measures of sleep-onset latency, total sleep time, sleep efficiency and sleep interruption and showed that listening to music may not improve these outcomes compared to no treatment or TAU. None of the included studies reported any adverse events.


AUTHORS' CONCLUSIONS
The findings of this review provide evidence that music may be effective for improving subjective sleep quality in adults with symptoms of insomnia. More research is needed to establish the effect of listening to music on other aspects of sleep as well as the daytime consequences of insomnia.},
	language = {en},
	number = {8},
	urldate = {2023-11-22},
	journal = {Cochrane Database of Systematic Reviews},
	author = {Jespersen, Kira V and Pando-Naude, Victor and Koenig, Julian and Jennum, Poul and Vuust, Peter},
	editor = {{Cochrane Developmental, Psychosocial and Learning Problems Group}},
	month = aug,
	year = {2022},
}

@article{scarratt_audio_2023,
	title = {The audio features of sleep music: {Universal} and subgroup characteristics},
	volume = {18},
	issn = {1932-6203},
	shorttitle = {The audio features of sleep music},
	doi = {10.1371/journal.pone.0278813},
	abstract = {Throughout history, lullabies have been used to help children sleep, and today, with the increasing accessibility of recorded music, many people report listening to music as a tool to improve sleep. Nevertheless, we know very little about this common human habit. In this study, we elucidated the characteristics of music associated with sleep by extracting audio features from a large number of tracks (N = 225,626) retrieved from sleep playlists at the global streaming platform Spotify. Compared to music in general, we found that sleep music was softer and slower; it was more often instrumental (i.e. without lyrics) and played on acoustic instruments. Yet, a large amount of variation was present in sleep music, which clustered into six distinct subgroups. Strikingly, three of the subgroups included popular tracks that were faster, louder, and more energetic than average sleep music. The findings reveal previously unknown aspects of the audio features of sleep music and highlight the individual variation in the choice of music used for sleep. By using digital traces, we were able to determine the universal and subgroup characteristics of sleep music in a unique, global dataset, advancing our understanding of how humans use music to regulate their behaviour in everyday life.},
	language = {eng},
	number = {1},
	journal = {PloS One},
	author = {Scarratt, Rebecca Jane and Heggli, Ole Adrian and Vuust, Peter and Jespersen, Kira Vibe},
	year = {2023},
	pmid = {36652415},
	pmcid = {PMC9847986},
	keywords = {Auditory Perception, Child, Habits, Humans, Music, Music Therapy, Sleep},
	pages = {e0278813},
}

@techreport{scarratt_music_2021,
	type = {preprint},
	title = {The music that people use to sleep: universal and subgroup characteristics},
	shorttitle = {The music that people use to sleep},
	url = {https://osf.io/5mbyv},
	abstract = {Sleep problems are increasing in modern society. Throughout history, lullabies have been used to soothe the sleep of children, and today, with the increasing accessibility of recorded music, many people report listening to music as a tool to improve sleep. Nevertheless, we know very little about this common human habit. In this study, we elucidate the characteristics of music used for sleep by extracting the features of a large number of tracks (N = 225,927) from 989 sleep playlists retrieved from the global streaming platform Spotify. We found that compared to music in general, music used for sleep is softer and slower; it is more often instrumental (i.e. without lyrics) and played on acoustic instruments. Yet, a large amount of variation was found to be present in sleep music, which clustered into six distinct subgroups. Strikingly, three of these subgroups included popular mainstream tracks that are faster, louder, and more energetic than average sleep music. The findings reveal previously unknown aspects of sleep music and highlight the individual variation in the choice of music for facilitating sleep. By using digital traces, we were able to determine the universal and subgroup characteristics of sleep music in a unique, global dataset. This study can inform the clinical use of music and advance our understanding of how music is used to regulate human behaviour in everyday life.},
	language = {en},
	urldate = {2023-11-22},
	institution = {PsyArXiv},
	author = {Scarratt, Rebecca Jane and Heggli, Ole Adrian and Vuust, Peter and Jespersen, Kira Vibe},
	month = jun,
	year = {2021},
	doi = {10.31234/osf.io/5mbyv},
}

@article{mnih_brief_nodate,
	title = {A {Brief} {Tour} of {Generative} {Modelling}},
	language = {en},
	author = {Mnih, Andriy},
}

@article{talluri_denoising_nodate,
	title = {Denoising {Diffusion} {Probability} {Models} for {Image} {Inpainting}},
	language = {en},
	author = {Talluri, Pranav},
}

@inproceedings{ueno_exhaustive_2019,
	address = {Larnaca, Cyprus},
	title = {Exhaustive {Study} of {Hierarchical} {AllReduce} {Patterns} for {Large} {Messages} {Between} {GPUs}},
	isbn = {978-1-72810-912-1},
	url = {https://ieeexplore.ieee.org/document/8752949/},
	doi = {10.1109/CCGRID.2019.00057},
	abstract = {Data-parallel distributed deep learning requires an AllReduce operation between all GPUs with message sizes in the order of hundreds of megabytes. The popular implementation of AllReduce for deep learning is the Ring-AllReduce, but this method suffers from latency when using thousands of GPUs. There have been efforts to reduce this latency by combining the ring with more latency-optimal hierarchical methods. In the present work, we consider these hierarchical communication methods as a general hierarchical Ring-AllReduce with a pure Ring-AllReduce on one end and Rabenseifner’s algorithm on the other end of the spectrum. We exhaustively test the various combinations of hierarchical partitioning of processes on the ABCI system in Japan on up to 2048 GPUs. We develop a performance model for this generalized hierarchical Ring-AllReduce and show the lower-bound of the effective bandwidth achievable for the hierarchical NCCL communication on thousands of GPUs. Our measurements agree well with our performance model. We also ﬁnd that the optimal large-scale process hierarchy contains the optimal small-scale process hierarchy so the search space for the optimal communication will be reduced.},
	language = {en},
	urldate = {2023-11-20},
	booktitle = {2019 19th {IEEE}/{ACM} {International} {Symposium} on {Cluster}, {Cloud} and {Grid} {Computing} ({CCGRID})},
	publisher = {IEEE},
	author = {Ueno, Yuichiro and Yokota, Rio},
	month = may,
	year = {2019},
	pages = {430--439},
}

@misc{zheng_asynchronous_2020,
	title = {Asynchronous {Stochastic} {Gradient} {Descent} with {Delay} {Compensation}},
	url = {http://arxiv.org/abs/1609.08326},
	doi = {10.48550/arXiv.1609.08326},
	abstract = {With the fast development of deep learning, it has become common to learn big neural networks using massive training data. Asynchronous Stochastic Gradient Descent (ASGD) is widely adopted to fulfill this task for its efficiency, which is, however, known to suffer from the problem of delayed gradients. That is, when a local worker adds its gradient to the global model, the global model may have been updated by other workers and this gradient becomes "delayed". We propose a novel technology to compensate this delay, so as to make the optimization behavior of ASGD closer to that of sequential SGD. This is achieved by leveraging Taylor expansion of the gradient function and efficient approximation to the Hessian matrix of the loss function. We call the new algorithm Delay Compensated ASGD (DC-ASGD). We evaluated the proposed algorithm on CIFAR-10 and ImageNet datasets, and the experimental results demonstrate that DC-ASGD outperforms both synchronous SGD and asynchronous SGD, and nearly approaches the performance of sequential SGD.},
	urldate = {2023-11-20},
	publisher = {arXiv},
	author = {Zheng, Shuxin and Meng, Qi and Wang, Taifeng and Chen, Wei and Yu, Nenghai and Ma, Zhi-Ming and Liu, Tie-Yan},
	month = feb,
	year = {2020},
	note = {arXiv:1609.08326 [cs]},
	keywords = {Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning},
}

@article{qarib_comparative_2016,
	title = {A comparative study of signal processing methods for structural health monitoring},
	volume = {18},
	doi = {10.21595/jve.2016.17218},
	abstract = {In this paper four non-parametric and five parametric signal processing techniques are reviewed and their performances are compared through application to a sample exponentially damped synthetic signal with closely-spaced frequencies representing the ambient response of structures. The non-parametric methods are Fourier transform, periodogram estimate of power spectral density, wavelet transform, and empirical mode decomposition with Hilbert spectral analysis (Hilbert-Huang transform). The parametric methods are pseudospectrum estimate using the multiple signal categorization (MUSIC), empirical wavelet transform, approximate Prony method, matrix pencil method, and the estimation of signal parameters by rotational invariance technique (ESPRIT) method. The performances of different methods are studied statistically using the Monte Carlo simulation and the results are presented in terms of average errors of multiple sample analyses.},
	journal = {Journal of Vibroengineering},
	author = {Qarib, Hossein and Adeli, Hojjat},
	month = jun,
	year = {2016},
}

@misc{noauthor_comparative_nodate,
	title = {A comparative study of multiple music signal noise reduction algorithms in music processing - {Songqing} {Ma}, 2023},
	url = {https://journals.sagepub.com/doi/full/10.1177/09574565231179732},
	urldate = {2023-11-17},
}

@article{cunha_sergio_scene2wav_2021,
	title = {{Scene2Wav}: a deep convolutional sequence-to-conditional {SampleRNN} for emotional scene musicalization},
	volume = {80},
	shorttitle = {{Scene2Wav}},
	doi = {10.1007/s11042-020-09636-5},
	abstract = {This paper presents Scene2Wav, a novel deep convolutional model proposed to handle the task of music generation from emotionally annotated video. This is important because when paired with the appropriate audio, the resulting music video is able to enhance the emotional effect it has on viewers. The challenge lies in transforming the video to audio domain and generating music. Our proposed encoder Scene2Wav uses a convolutional sequence encoder to embed dynamic emotional visual features from low-level features in the colour space, namely Hue, Saturation and Value. The decoder Scene2Wav is a proposed conditional SampleRNN which uses that emotional visual feature embedding as condition to generate novel emotional music. The entire model is fine-tuned in an end-to-end training fashion to generate a music signal evoking the intended emotional response from the listener. By taking into consideration the emotional and generative aspect of it, this work is a significant contribution to the field of Human-Computer Interaction. It is also a stepping stone towards the creation of an AI movie and/or drama director, which is able to automatically generate appropriate music for trailers and movies. Based on experimental results, this model can effectively generate music that is preferred to the user when compared to the baseline model and able to evoke correct emotions.},
	journal = {Multimedia Tools and Applications},
	author = {Cunha Sergio, Gwenaelle and Lee, Minho},
	month = jan,
	year = {2021},
	pages = {1--20},
}

@article{russell_circumplex_1980,
	title = {A {Circumplex} {Model} of {Affect}},
	volume = {39},
	doi = {10.1037/h0077714},
	abstract = {Factor-analytic evidence has led most psychologists to describe affect as a set of dimensions, such as displeasure, distress, depression, excitement, and so on, with each dimension varying independently of the others. However, there is other evidence that rather than being independent, these affective dimensions are interrelated in a highly systematic fashion. The evidence suggests that these interrelationships can be represented by a spatial model in which affective concepts fall in a circle in the following order: pleasure (0), excitement (45), arousal (90), distress (135), displeasure (180), depression (225), sleepiness (270), and relaxation (315). This model was offered both as a way psychologists can represent the structure of affective experience, as assessed through self-report, and as a representation of the cognitive structure that laymen utilize in conceptualizing affect. Supportive evidence was obtained by scaling 28 emotion-denoting adjectives in 4 different ways: R. T. Ross's (1938) technique for a circular ordering of variables, a multidimensional scaling procedure based on perceived similarity among the terms, a unidimensional scaling on hypothesized pleasure–displeasure and degree-of-arousal dimensions, and a principal-components analysis of 343 Ss' self-reports of their current affective states. (70 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)},
	journal = {Journal of Personality and Social Psychology},
	author = {Russell, James},
	month = dec,
	year = {1980},
	pages = {1161--1178},
}

@inproceedings{ho_denoising_2020,
	title = {Denoising {Diffusion} {Probabilistic} {Models}},
	volume = {33},
	url = {https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html},
	abstract = {We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.},
	urldate = {2023-11-17},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
	year = {2020},
	pages = {6840--6851},
}

@misc{ho_denoising_2020-1,
	title = {Denoising {Diffusion} {Probabilistic} {Models}},
	url = {http://arxiv.org/abs/2006.11239},
	doi = {10.48550/arXiv.2006.11239},
	abstract = {We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at https://github.com/hojonathanho/diffusion},
	urldate = {2023-11-17},
	publisher = {arXiv},
	author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
	month = dec,
	year = {2020},
	note = {arXiv:2006.11239 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{huang_mulan_2022-1,
	title = {{MuLan}: {A} {Joint} {Embedding} of {Music} {Audio} and {Natural} {Language}},
	shorttitle = {{MuLan}},
	url = {http://arxiv.org/abs/2208.12415},
	doi = {10.48550/arXiv.2208.12415},
	abstract = {Music tagging and content-based retrieval systems have traditionally been constructed using pre-defined ontologies covering a rigid set of music attributes or text queries. This paper presents MuLan: a first attempt at a new generation of acoustic models that link music audio directly to unconstrained natural language music descriptions. MuLan takes the form of a two-tower, joint audio-text embedding model trained using 44 million music recordings (370K hours) and weakly-associated, free-form text annotations. Through its compatibility with a wide range of music genres and text styles (including conventional music tags), the resulting audio-text representation subsumes existing ontologies while graduating to true zero-shot functionalities. We demonstrate the versatility of the MuLan embeddings with a range of experiments including transfer learning, zero-shot music tagging, language understanding in the music domain, and cross-modal retrieval applications.},
	urldate = {2023-11-17},
	publisher = {arXiv},
	author = {Huang, Qingqing and Jansen, Aren and Lee, Joonseok and Ganti, Ravi and Li, Judith Yue and Ellis, Daniel P. W.},
	month = aug,
	year = {2022},
	note = {arXiv:2208.12415 [cs, eess, stat]},
	keywords = {Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning},
}

@misc{noauthor_audio_2023,
	title = {Audio {AI} {Timeline}},
	url = {https://github.com/archinetai/audio-ai-timeline},
	abstract = {A timeline of the latest AI models for audio generation, starting in 2023!},
	urldate = {2023-11-17},
	publisher = {Ȧrchinet},
	month = nov,
	year = {2023},
	note = {original-date: 2023-01-29T09:58:56Z},
	keywords = {artificial-intelligence, audio-generation, machine-learning},
}

@article{bradt_music_2013,
	title = {Music interventions for preoperative anxiety},
	volume = {2013},
	issn = {1469-493X},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9758540/},
	doi = {10.1002/14651858.CD006908.pub2},
	abstract = {Can music interventions replace sedatives for reduction of preoperative anxiety?
        , People awaiting surgical procedures often experience high levels of anxiety. Such anxiety may result in negative bodily responses, such as increased blood pressure and heart rate, leading to slower wound healing and increased risk of infection. High anxiety may also affect the start of anaesthesia and slow down postoperative recovery. To reduce patient anxiety, sedatives and anti‐anxiety drugs are regularly administered before surgery. However, these often have negative side effects, such as causing drowsiness and breathing difficulties, and may interact with anaesthetic drugs to prolong patient recovery and discharge. Therefore, increasing attention is being paid to music therapy and music medicine interventions, amongst other non‐pharmacological interventions, for reduction of preoperative anxiety. Interventions are categorized as 'music medicine' when passive listening to pre‐recorded music is offered by medical personnel. In contrast, music therapy requires the implementation of a music intervention by a trained music therapist, the presence of a therapeutic process, and the use of personally tailored music experiences. A systematic review was needed to gauge the efficacy of both music therapy and music medicine interventions for reduction of preoperative anxiety., The review included 26 trials with a total of 2051 participants. The findings suggested that music listening may have a beneficial effect on preoperative anxiety. Most trials presented some methodological weakness. Therefore, these results need to be interpreted with caution. However, these findings are consistent with the findings of three other Cochrane systematic reviews on the use of music interventions for anxiety reduction in medical patients. Therefore, we conclude that music interventions may provide a viable alternative to sedatives and anti‐anxiety drugs for reducing preoperative anxiety.},
	number = {6},
	urldate = {2023-11-17},
	journal = {The Cochrane Database of Systematic Reviews},
	author = {Bradt, Joke and Dileo, Cheryl and Shim, Minjung},
	month = jun,
	year = {2013},
	pmid = {23740695},
	pmcid = {PMC9758540},
	pages = {CD006908},
}

@article{jasemi_effects_2016,
	title = {The {Effects} of {Music} {Therapy} on {Anxiety} and {Depression} of {Cancer} {Patients}},
	volume = {22},
	issn = {0973-1075},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5072238/},
	doi = {10.4103/0973-1075.191823},
	abstract = {Background and Purpose:
Cancer patients often suffer from anxiety and depression. Various methods are used to alleviate anxiety and depression, but most of them have side effects. Music therapy can be used as a noninvasive method to reduce anxiety and depression. This study aimed to examine the effect of music therapy on anxiety and depression in patients with cancer.

Materials and Methods:
This quasi-experimental study was conducted attaching hospitals in Urmia city. A total number of sixty patients with depression and anxiety were recruited using random sampling method and divided into two groups of control and intervention. Patients in intervention group listened to light music at least 20 min per day for 3 days. The degree of patients’ anxiety and depression was assessed by Hospital Anxiety and Depression Scale at baseline and 3 days after music therapy. Data were analyzed by SPSS version 13 using t-test, Pearson, and ANOVA tests.

Results:
The results showed no significant differences between demographic variable of intervention and control groups. Our findings indicated a significant decrease in the level of depression and anxiety among intervention group. There were significant relationships between anxiety, depression, and sex (P {\textless} 0.001, r = 0.42) as well as education (P = 0.003, r = 0.37).

Conclusion:
This study revealed positive effects of music therapy on decreasing level of depression and anxiety in patients with cancer. Therefore, it is recommended to include music therapy in the nursing care.},
	number = {4},
	urldate = {2023-11-17},
	journal = {Indian Journal of Palliative Care},
	author = {Jasemi, Madineh and Aazami, Sanaz and Zabihi, Roghaieh Esmaili},
	year = {2016},
	pmid = {27803568},
	pmcid = {PMC5072238},
	pages = {455--458},
}

@misc{moliner_diffusion-based_2023,
	title = {Diffusion-{Based} {Audio} {Inpainting}},
	url = {http://arxiv.org/abs/2305.15266},
	doi = {10.48550/arXiv.2305.15266},
	abstract = {Audio inpainting aims to reconstruct missing segments in corrupted recordings. Most of existing methods produce plausible reconstructions when the gap lengths are short, but struggle to reconstruct gaps larger than about 100 ms. This paper explores recent advancements in deep learning and, particularly, diffusion models, for the task of audio inpainting. The proposed method uses an unconditionally trained generative model, which can be conditioned in a zero-shot fashion for audio inpainting, and is able to regenerate gaps of any size. An improved deep neural network architecture based on the constant-Q transform, which allows the model to exploit pitch-equivariant symmetries in audio, is also presented. The performance of the proposed algorithm is evaluated through objective and subjective metrics for the task of reconstructing short to mid-sized gaps, up to 300 ms. The results of a formal listening test show that the proposed method delivers comparable performance against the compared baselines for short gaps, such as 50 ms, while retaining a good audio quality and outperforming the baselines for wider gaps that are up to 300 ms long. The method presented in this paper can be applied to restoring sound recordings that suffer from severe local disturbances or dropouts, which must be reconstructed.},
	urldate = {2023-11-17},
	publisher = {arXiv},
	author = {Moliner, Eloi and Välimäki, Vesa},
	month = sep,
	year = {2023},
	note = {arXiv:2305.15266 [cs, eess]},
	keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{lugmayr_repaint_2022,
	title = {{RePaint}: {Inpainting} using {Denoising} {Diffusion} {Probabilistic} {Models}},
	shorttitle = {{RePaint}},
	url = {http://arxiv.org/abs/2201.09865},
	doi = {10.48550/arXiv.2201.09865},
	abstract = {Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pretrained unconditional DDPM as the generative prior. To condition the generation process, we only alter the reverse diffusion iterations by sampling the unmasked regions using the given image information. Since this technique does not modify or condition the original DDPM network itself, the model produces high-quality and diverse output images for any inpainting form. We validate our method for both faces and general-purpose image inpainting using standard and extreme masks. RePaint outperforms state-of-the-art Autoregressive, and GAN approaches for at least five out of six mask distributions. Github Repository: git.io/RePaint},
	urldate = {2023-11-16},
	publisher = {arXiv},
	author = {Lugmayr, Andreas and Danelljan, Martin and Romero, Andres and Yu, Fisher and Timofte, Radu and Van Gool, Luc},
	month = aug,
	year = {2022},
	note = {arXiv:2201.09865 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{noauthor_transforming_2023,
	title = {Transforming the future of music creation},
	url = {https://deepmind.google/discover/blog/transforming-the-future-of-music-creation/},
	abstract = {Announcing our most advanced music generation model and two new AI experiments, designed to open a new playground for creativity},
	language = {en},
	urldate = {2023-11-16},
	journal = {Google DeepMind},
	month = nov,
	year = {2023},
}

@misc{yang_midinet_2017,
	title = {{MidiNet}: {A} {Convolutional} {Generative} {Adversarial} {Network} for {Symbolic}-domain {Music} {Generation}},
	shorttitle = {{MidiNet}},
	url = {http://arxiv.org/abs/1703.10847},
	doi = {10.48550/arXiv.1703.10847},
	abstract = {Most existing neural network models for music generation use recurrent neural networks. However, the recent WaveNet model proposed by DeepMind shows that convolutional neural networks (CNNs) can also generate realistic musical waveforms in the audio domain. Following this light, we investigate using CNNs for generating melody (a series of MIDI notes) one bar after another in the symbolic domain. In addition to the generator, we use a discriminator to learn the distributions of melodies, making it a generative adversarial network (GAN). Moreover, we propose a novel conditional mechanism to exploit available prior knowledge, so that the model can generate melodies either from scratch, by following a chord sequence, or by conditioning on the melody of previous bars (e.g. a priming melody), among other possibilities. The resulting model, named MidiNet, can be expanded to generate music with multiple MIDI channels (i.e. tracks). We conduct a user study to compare the melody of eight-bar long generated by MidiNet and by Google's MelodyRNN models, each time using the same priming melody. Result shows that MidiNet performs comparably with MelodyRNN models in being realistic and pleasant to listen to, yet MidiNet's melodies are reported to be much more interesting.},
	urldate = {2023-11-16},
	publisher = {arXiv},
	author = {Yang, Li-Chia and Chou, Szu-Yu and Yang, Yi-Hsuan},
	month = jul,
	year = {2017},
	note = {arXiv:1703.10847 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Sound},
}

@inproceedings{jeong_graph_2019,
	title = {Graph {Neural} {Network} for {Music} {Score} {Data} and {Modeling} {Expressive} {Piano} {Performance}},
	url = {https://proceedings.mlr.press/v97/jeong19a.html},
	abstract = {Music score is often handled as one-dimensional sequential data. Unlike words in a text document, notes in music score can be played simultaneously by the polyphonic nature and each of them has its own duration. In this paper, we represent the unique form of musical score using graph neural network and apply it for rendering expressive piano performance from the music score. Specifically, we design the model using note-level gated graph neural network and measure-level hierarchical attention network with bidirectional long short-term memory with an iterative feedback method. In addition, to model different styles of performance for a given input score, we employ a variational auto-encoder. The result of the listening test shows that our proposed model generated more human-like performances compared to a baseline model and a hierarchical attention network model that handles music score as a word-like sequence.},
	language = {en},
	urldate = {2023-11-16},
	booktitle = {Proceedings of the 36th {International} {Conference} on {Machine} {Learning}},
	publisher = {PMLR},
	author = {Jeong, Dasaem and Kwon, Taegyun and Kim, Yoojin and Nam, Juhan},
	month = may,
	year = {2019},
	note = {ISSN: 2640-3498},
	pages = {3060--3070},
}

@misc{ji_comprehensive_2020,
	title = {A {Comprehensive} {Survey} on {Deep} {Music} {Generation}: {Multi}-level {Representations}, {Algorithms}, {Evaluations}, and {Future} {Directions}},
	shorttitle = {A {Comprehensive} {Survey} on {Deep} {Music} {Generation}},
	url = {http://arxiv.org/abs/2011.06801},
	doi = {10.48550/arXiv.2011.06801},
	abstract = {The utilization of deep learning techniques in generating various contents (such as image, text, etc.) has become a trend. Especially music, the topic of this paper, has attracted widespread attention of countless researchers.The whole process of producing music can be divided into three stages, corresponding to the three levels of music generation: score generation produces scores, performance generation adds performance characteristics to the scores, and audio generation converts scores with performance characteristics into audio by assigning timbre or generates music in audio format directly. Previous surveys have explored the network models employed in the field of automatic music generation. However, the development history, the model evolution, as well as the pros and cons of same music generation task have not been clearly illustrated. This paper attempts to provide an overview of various composition tasks under different music generation levels, covering most of the currently popular music generation tasks using deep learning. In addition, we summarize the datasets suitable for diverse tasks, discuss the music representations, the evaluation methods as well as the challenges under different levels, and finally point out several future directions.},
	urldate = {2023-11-16},
	publisher = {arXiv},
	author = {Ji, Shulei and Luo, Jing and Yang, Xinyu},
	month = nov,
	year = {2020},
	note = {arXiv:2011.06801 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{ji_comprehensive_2020-1,
	title = {A {Comprehensive} {Survey} on {Deep} {Music} {Generation}: {Multi}-level {Representations}, {Algorithms}, {Evaluations}, and {Future} {Directions}},
	shorttitle = {A {Comprehensive} {Survey} on {Deep} {Music} {Generation}},
	url = {http://arxiv.org/abs/2011.06801},
	abstract = {The utilization of deep learning techniques in generating various contents (such as image, text, etc.) has become a trend. Especially music, the topic of this paper, has attracted widespread attention of countless researchers.The whole process of producing music can be divided into three stages, corresponding to the three levels of music generation: score generation produces scores, performance generation adds performance characteristics to the scores, and audio generation converts scores with performance characteristics into audio by assigning timbre or generates music in audio format directly. Previous surveys have explored the network models employed in the field of automatic music generation. However, the development history, the model evolution, as well as the pros and cons of same music generation task have not been clearly illustrated. This paper attempts to provide an overview of various composition tasks under different music generation levels, covering most of the currently popular music generation tasks using deep learning. In addition, we summarize the datasets suitable for diverse tasks, discuss the music representations, the evaluation methods as well as the challenges under different levels, and finally point out several future directions.},
	urldate = {2023-11-16},
	publisher = {arXiv},
	author = {Ji, Shulei and Luo, Jing and Yang, Xinyu},
	month = nov,
	year = {2020},
	note = {arXiv:2011.06801 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{zou_melons_2021,
	title = {{MELONS}: generating melody with long-term structure using transformers and structure graph},
	shorttitle = {{MELONS}},
	url = {http://arxiv.org/abs/2110.05020},
	doi = {10.48550/arXiv.2110.05020},
	abstract = {The creation of long melody sequences requires effective expression of coherent musical structure. However, there is no clear representation of musical structure. Recent works on music generation have suggested various approaches to deal with the structural information of music, but generating a full-song melody with clear long-term structure remains a challenge. In this paper, we propose MELONS, a melody generation framework based on a graph representation of music structure which consists of eight types of bar-level relations. MELONS adopts a multi-step generation method with transformer-based networks by factoring melody generation into two sub-problems: structure generation and structure conditional melody generation. Experimental results show that MELONS can produce structured melodies with high quality and rich contents.},
	urldate = {2023-11-16},
	publisher = {arXiv},
	author = {Zou, Yi and Zou, Pei and Zhao, Yi and Zhang, Kaixiang and Zhang, Ran and Wang, Xiaorui},
	month = nov,
	year = {2021},
	note = {arXiv:2110.05020 [cs, eess]},
	keywords = {Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@article{bradt_music_2013-1,
	title = {Music interventions for preoperative anxiety},
	volume = {2013},
	issn = {1469-493X},
	doi = {10.1002/14651858.CD006908.pub2},
	abstract = {BACKGROUND: Patients awaiting surgical procedures often experience significant anxiety. Such anxiety may result in negative physiological manifestations, slower wound healing, increased risk of infection, and may complicate the induction of anaesthesia and impede postoperative recovery. To reduce patient anxiety, sedatives and anti-anxiety drugs are regularly administered before surgery. However, these often have negative side effects and may prolong patient recovery. Therefore, increasing attention is being paid to a variety of non-pharmacological interventions for reduction of preoperative anxiety such as music therapy and music medicine interventions. Interventions are categorized as 'music medicine' when passive listening to pre-recorded music is offered by medical personnel. In contrast, music therapy requires the implementation of a music intervention by a trained music therapist, the presence of a therapeutic process, and the use of personally tailored music experiences. A systematic review was needed to gauge the efficacy of both music therapy and music medicine interventions for reduction of preoperative anxiety.
OBJECTIVES: To examine the effects of music interventions with standard care versus standard care alone on preoperative anxiety in surgical patients.
SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library 2012, Issue 7), MEDLINE (1950 to August 2012), CINAHL (1980 to August 2012), AMED (1985 to April 2011; we no longer had access to AMED after this date), EMBASE (1980 to August 2012), PsycINFO (1967 to August 2012), LILACS (1982 to August 2012), Science Citation Index (1980 to August 2012), the specialist music therapy research database (March 1 2008; database is no longer functional), CAIRSS for Music (to August 2012), Proquest Digital Dissertations (1980 to August 2012), ClinicalTrials.gov (2000 to August 2012), Current Controlled Trials (1998 to August 2012), and the National Research Register (2000 to September 2007). We handsearched music therapy journals and reference lists, and contacted relevant experts to identify unpublished manuscripts. There was no language restriction.
SELECTION CRITERIA: We included all randomized and quasi-randomized trials that compared music interventions and standard care with standard care alone for reducing preoperative anxiety in surgical patients.
DATA COLLECTION AND ANALYSIS: Two review authors independently extracted the data and assessed the risk of bias. We contacted authors to obtain missing data where needed. Where possible, results were presented in meta analyses using mean differences and standardized mean differences. Post-test scores were used. In cases of significant baseline differences, we used change scores.
MAIN RESULTS: We included 26 trials (2051 participants). All studies used listening to pre-recorded music. The results suggested that music listening may have a beneficial effect on preoperative anxiety. Specifically, music listening resulted, on average, in an anxiety reduction that was 5.72 units greater (95\% CI -7.27 to -4.17, P {\textless} 0.00001) than that in the standard care group as measured by the Stait-Trait Anxiety Inventory (STAI-S), and -0.60 standardized units (95\% CI -0.90 to -0.31, P {\textless} 0.0001) on other anxiety scales. The results also suggested a small effect on heart rate and diastolic blood pressure, but no support was found for reductions in systolic blood pressure, respiratory rate, and skin temperature. Most trials were assessed to be at high risk of bias because of lack of blinding. Blinding of outcome assessors is often impossible in music therapy and music medicine studies that use subjective outcomes, unless in studies in which the music intervention is compared to another treatment intervention. Because of the high risk of bias, these results need to be interpreted with caution.None of the studies included wound healing, infection rate, time to discharge, or patient satisfaction as outcome variables. One large study found that music listening was more effective than the sedative midazolam in reducing preoperative anxiety and equally effective in reducing physiological responses. No adverse effects were identified.
AUTHORS' CONCLUSIONS: This systematic review indicates that music listening may have a beneficial effect on preoperative anxiety. These findings are consistent with the findings of three other Cochrane systematic reviews on the use of music interventions for anxiety reduction in medical patients. Therefore, we conclude that music interventions may provide a viable alternative to sedatives and anti-anxiety drugs for reducing preoperative anxiety.},
	language = {eng},
	number = {6},
	journal = {The Cochrane Database of Systematic Reviews},
	author = {Bradt, Joke and Dileo, Cheryl and Shim, Minjung},
	month = jun,
	year = {2013},
	pmid = {23740695},
	pmcid = {PMC9758540},
	keywords = {Anxiety, Blood Pressure, Heart Rate, Humans, Music Therapy, Preoperative Care, Randomized Controlled Trials as Topic, Surgical Procedures, Operative},
	pages = {CD006908},
}

@article{loewy_music_2020,
	title = {Music {Therapy} as a {Potential} {Intervention} for {Sleep} {Improvement}},
	volume = {12},
	issn = {1179-1608},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954684/},
	doi = {10.2147/NSS.S194938},
	abstract = {Sleep deficiency is linked to chronic health problems, such as heart disease, kidney disease, high blood pressure, diabetes, stroke, obesity, and depression. Healthcare practitioners are increasingly paying close attention to sleep and its impact on health and wellness as a measure of critical vitality. Sleep’s impact on neurologic function, and cognitive endurance affect capacity throughout the lifespan. This article will address recent findings related to the potential of music to induce sleep in illness and wellness. Music therapy research findings and its efficacy as a potent cost-effective intervention will be highlighted.},
	urldate = {2023-11-16},
	journal = {Nature and Science of Sleep},
	author = {Loewy, Joanne},
	month = jan,
	year = {2020},
	pmid = {32021519},
	pmcid = {PMC6954684},
	pages = {1--9},
}

@article{sheynin_emu_nodate,
	title = {Emu {Edit}: {Precise} {Image} {Editing} via {Recognition} and {Generation} {Tasks}},
	language = {en},
	author = {Sheynin, Shelly and Polyak, Adam and Singer, Uriel and Kirstain, Yuval and Zohar, Amit and Ashual, Oron and Parikh, Devi and Taigman, Yaniv},
}

@misc{velickovic_graph_2018,
	title = {Graph {Attention} {Networks}},
	url = {http://arxiv.org/abs/1710.10903},
	doi = {10.48550/arXiv.1710.10903},
	abstract = {We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training).},
	urldate = {2023-11-16},
	publisher = {arXiv},
	author = {Veličković, Petar and Cucurull, Guillem and Casanova, Arantxa and Romero, Adriana and Liò, Pietro and Bengio, Yoshua},
	month = feb,
	year = {2018},
	note = {arXiv:1710.10903 [cs, stat]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Social and Information Networks, Statistics - Machine Learning},
}

@inproceedings{derrow-pinion_eta_2021,
	title = {{ETA} {Prediction} with {Graph} {Neural} {Networks} in {Google} {Maps}},
	url = {http://arxiv.org/abs/2108.11482},
	doi = {10.1145/3459637.3481916},
	abstract = {Travel-time prediction constitutes a task of high importance in transportation networks, with web mapping services like Google Maps regularly serving vast quantities of travel time queries from users and enterprises alike. Further, such a task requires accounting for complex spatiotemporal interactions (modelling both the topological properties of the road network and anticipating events -- such as rush hours -- that may occur in the future). Hence, it is an ideal target for graph representation learning at scale. Here we present a graph neural network estimator for estimated time of arrival (ETA) which we have deployed in production at Google Maps. While our main architecture consists of standard GNN building blocks, we further detail the usage of training schedule methods such as MetaGradients in order to make our model robust and production-ready. We also provide prescriptive studies: ablating on various architectural decisions and training regimes, and qualitative analyses on real-world situations where our model provides a competitive edge. Our GNN proved powerful when deployed, significantly reducing negative ETA outcomes in several regions compared to the previous production baseline (40+\% in cities like Sydney).},
	urldate = {2023-11-16},
	booktitle = {Proceedings of the 30th {ACM} {International} {Conference} on {Information} \& {Knowledge} {Management}},
	author = {Derrow-Pinion, Austin and She, Jennifer and Wong, David and Lange, Oliver and Hester, Todd and Perez, Luis and Nunkesser, Marc and Lee, Seongjae and Guo, Xueying and Wiltshire, Brett and Battaglia, Peter W. and Gupta, Vishal and Li, Ang and Xu, Zhongwen and Sanchez-Gonzalez, Alvaro and Li, Yujia and Veličković, Petar},
	month = oct,
	year = {2021},
	note = {arXiv:2108.11482 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Social and Information Networks},
	pages = {3767--3776},
}

@article{chen_effect_2021,
	title = {Effect of music therapy on improving sleep quality in older adults: {A} systematic review and meta-analysis},
	volume = {69},
	issn = {1532-5415},
	shorttitle = {Effect of music therapy on improving sleep quality in older adults},
	doi = {10.1111/jgs.17149},
	abstract = {OBJECTIVES: Poor sleep quality is a common issue among older adults; it can lead to a poor quality of life and impairments in cognitive function and physical health. This study aimed to conduct a systematic review and meta-analysis of the effect of listening to music on sleep quality in older adults.
DESIGN: Systematic review and meta-analyses.
SETTING: Five databases, including Embase, Ovid Medline, Cochrane Library, Scopus, and the Index to Taiwan Periodical Literature System, were searched to identify studies assessing the efficacy of music therapy in older adults aged 60 years and older published through February 20, 2021.
PARTICIPANTS: Adults aged 60 years and older.
MEASUREMENTS: We searched English- and Chinese-language studies of randomized control trials. All studies were reviewed by two independent investigators. The primary sleep outcome was the Pittsburgh sleep quality index. The Cochrane Collaboration tool was used to assess the risk of bias, and Review Manager 5.3 software was used to conduct the meta-analysis.
RESULTS: Five randomized control trials were included in the meta-analysis. Older adults who listened to music experienced significantly better sleep quality than those who did not listen to music [mean difference (MD): -1.96, 95\% CI -2.23 to -1.73, P = 0.003]. The subgroup analysis revealed that older adults who listened to sedative music obtained a more effective improvement in sleep quality than those who listened to rhythm-centered music (MD: -2.35, 95\% CI -3.59 to -1.10, P = 0.0002). Furthermore, listening to music for longer than 4 weeks (MD: -2.61, 95\% CI -4.72 to -0.50, P = 0.02) was to be effective at improving sleep quality.
CONCLUSIONS: Music therapy is safe and easy to administer and can effectively improve sleep quality among older adults, particularly those listening to more sedative music for at least a four-week duration.},
	language = {eng},
	number = {7},
	journal = {Journal of the American Geriatrics Society},
	author = {Chen, Chia-Te and Tung, Heng-Hsin and Fang, Ching-Ju and Wang, Jiun-Ling and Ko, Nai-Ying and Chang, Ying-Ju and Chen, Yen-Chin},
	month = jul,
	year = {2021},
	pmid = {33880759},
	keywords = {Aged, Aged, 80 and over, Female, Humans, Male, Music, Music Therapy, Quality of Life, Randomized Controlled Trials as Topic, Sleep, Sleep Initiation and Maintenance Disorders, Treatment Outcome, meta-analysis, music, older adults, sleep quality, systematic review},
	pages = {1925--1932},
}

@misc{zhang_understanding_2017,
	title = {Understanding deep learning requires rethinking generalization},
	url = {http://arxiv.org/abs/1611.03530},
	doi = {10.48550/arXiv.1611.03530},
	abstract = {Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance. Conventional wisdom attributes small generalization error either to properties of the model family, or to the regularization techniques used during training. Through extensive systematic experiments, we show how these traditional approaches fail to explain why large neural networks generalize well in practice. Specifically, our experiments establish that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a random labeling of the training data. This phenomenon is qualitatively unaffected by explicit regularization, and occurs even if we replace the true images by completely unstructured random noise. We corroborate these experimental findings with a theoretical construction showing that simple depth two neural networks already have perfect finite sample expressivity as soon as the number of parameters exceeds the number of data points as it usually does in practice. We interpret our experimental findings by comparison with traditional models.},
	urldate = {2023-11-15},
	publisher = {arXiv},
	author = {Zhang, Chiyuan and Bengio, Samy and Hardt, Moritz and Recht, Benjamin and Vinyals, Oriol},
	month = feb,
	year = {2017},
	note = {arXiv:1611.03530 [cs]},
	keywords = {Computer Science - Machine Learning},
}

@misc{toneva_empirical_2019,
	title = {An {Empirical} {Study} of {Example} {Forgetting} during {Deep} {Neural} {Network} {Learning}},
	url = {http://arxiv.org/abs/1812.05159},
	doi = {10.48550/arXiv.1812.05159},
	abstract = {Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single classification tasks. Our goal is to understand whether a related phenomenon occurs when data does not undergo a clear distributional shift. We define a `forgetting event' to have occurred when an individual training example transitions from being classified correctly to incorrectly over the course of learning. Across several benchmark data sets, we find that: (i) certain examples are forgotten with high frequency, and some not at all; (ii) a data set's (un)forgettable examples generalize across neural architectures; and (iii) based on forgetting dynamics, a significant fraction of examples can be omitted from the training data set while still maintaining state-of-the-art generalization performance.},
	urldate = {2023-11-15},
	publisher = {arXiv},
	author = {Toneva, Mariya and Sordoni, Alessandro and Combes, Remi Tachet des and Trischler, Adam and Bengio, Yoshua and Gordon, Geoffrey J.},
	month = nov,
	year = {2019},
	note = {arXiv:1812.05159 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{ferreira_learning_2021,
	title = {Learning to {Generate} {Music} {With} {Sentiment}},
	url = {http://arxiv.org/abs/2103.06125},
	doi = {10.48550/arXiv.2103.06125},
	abstract = {Deep Learning models have shown very promising results in automatically composing polyphonic music pieces. However, it is very hard to control such models in order to guide the compositions towards a desired goal. We are interested in controlling a model to automatically generate music with a given sentiment. This paper presents a generative Deep Learning model that can be directed to compose music with a given sentiment. Besides music generation, the same model can be used for sentiment analysis of symbolic music. We evaluate the accuracy of the model in classifying sentiment of symbolic music using a new dataset of video game soundtracks. Results show that our model is able to obtain good prediction accuracy. A user study shows that human subjects agreed that the generated music has the intended sentiment, however negative pieces can be ambiguous.},
	urldate = {2023-11-15},
	publisher = {arXiv},
	author = {Ferreira, Lucas N. and Whitehead, Jim},
	month = mar,
	year = {2021},
	note = {arXiv:2103.06125 [cs, eess]},
	keywords = {Computer Science - Information Retrieval, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{noauthor_evaluation_nodate,
	title = {Evaluation of {Musical} {Creativity} and {Musical} {Metacreation} {Systems} {\textbar} {Computers} in {Entertainment}},
	url = {https://dl.acm.org/doi/10.1145/2967506},
	urldate = {2023-11-15},
}

@article{yin_deep_2023,
	title = {Deep learning’s shallow gains: a comparative evaluation of algorithms for automatic music generation},
	volume = {112},
	issn = {1573-0565},
	shorttitle = {Deep learning’s shallow gains},
	url = {https://doi.org/10.1007/s10994-023-06309-w},
	doi = {10.1007/s10994-023-06309-w},
	abstract = {Deep learning methods are recognised as state-of-the-art for many applications of machine learning. Recently, deep learning methods have emerged as a solution to the task of automatic music generation (AMG) using symbolic tokens in a target style, but their superiority over non-deep learning methods has not been demonstrated. Here, we conduct a listening study to comparatively evaluate several music generation systems along six musical dimensions: stylistic success, aesthetic pleasure, repetition or self-reference, melody, harmony, and rhythm. A range of models, both deep learning algorithms and other methods, are used to generate 30-s excerpts in the style of Classical string quartets and classical piano improvisations. Fifty participants with relatively high musical knowledge rate unlabelled samples of computer-generated and human-composed excerpts for the six musical dimensions. We use non-parametric Bayesian hypothesis testing to interpret the results, allowing the possibility of finding meaningful non-differences between systems’ performance. We find that the strongest deep learning method, a reimplemented version of Music Transformer, has equivalent performance to a non-deep learning method, MAIA Markov, demonstrating that to date, deep learning does not outperform other methods for AMG. We also find there still remains a significant gap between any algorithmic method and human-composed excerpts.},
	language = {en},
	number = {5},
	urldate = {2023-11-15},
	journal = {Machine Learning},
	author = {Yin, Zongyu and Reuben, Federico and Stepney, Susan and Collins, Tom},
	month = may,
	year = {2023},
	keywords = {Comparative evaluation, Deep learning, Listening study, Markov model, Music generation, Non-parametric Bayesian hypothesis testing},
	pages = {1785--1822},
}

@inproceedings{linzen_how_2020,
	address = {Online},
	title = {How {Can} {We} {Accelerate} {Progress} {Towards} {Human}-like {Linguistic} {Generalization}?},
	url = {https://aclanthology.org/2020.acl-main.465},
	doi = {10.18653/v1/2020.acl-main.465},
	abstract = {This position paper describes and critiques the Pretraining-Agnostic Identically Distributed (PAID) evaluation paradigm, which has become a central tool for measuring progress in natural language understanding. This paradigm consists of three stages: (1) pre-training of a word prediction model on a corpus of arbitrary size; (2) fine-tuning (transfer learning) on a training set representing a classification task; (3) evaluation on a test set drawn from the same distribution as that training set. This paradigm favors simple, low-bias architectures, which, first, can be scaled to process vast amounts of data, and second, can capture the fine-grained statistical properties of a particular data set, regardless of whether those properties are likely to generalize to examples of the task outside the data set. This contrasts with humans, who learn language from several orders of magnitude less data than the systems favored by this evaluation paradigm, and generalize to new tasks in a consistent way. We advocate for supplementing or replacing PAID with paradigms that reward architectures that generalize as quickly and robustly as humans.},
	urldate = {2023-11-15},
	booktitle = {Proceedings of the 58th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics}},
	publisher = {Association for Computational Linguistics},
	author = {Linzen, Tal},
	editor = {Jurafsky, Dan and Chai, Joyce and Schluter, Natalie and Tetreault, Joel},
	month = jul,
	year = {2020},
	pages = {5210--5217},
}

@misc{yogatama_learning_2019,
	title = {Learning and {Evaluating} {General} {Linguistic} {Intelligence}},
	url = {http://arxiv.org/abs/1901.11373},
	doi = {10.48550/arXiv.1901.11373},
	abstract = {We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic conventions to adapt to new tasks quickly. Using this definition, we analyze state-of-the-art natural language understanding models and conduct an extensive empirical investigation to evaluate them against these criteria through a series of experiments that assess the task-independence of the knowledge being acquired by the learning process. In addition to task performance, we propose a new evaluation metric based on an online encoding of the test data that quantifies how quickly an existing agent (model) learns a new task. Our results show that while the field has made impressive progress in terms of model architectures that generalize to many tasks, these models still require a lot of in-domain training examples (e.g., for fine tuning, training task-specific modules), and are prone to catastrophic forgetting. Moreover, we find that far from solving general tasks (e.g., document question answering), our models are overfitting to the quirks of particular datasets (e.g., SQuAD). We discuss missing components and conjecture on how to make progress toward general linguistic intelligence.},
	urldate = {2023-11-15},
	publisher = {arXiv},
	author = {Yogatama, Dani and d'Autume, Cyprien de Masson and Connor, Jerome and Kocisky, Tomas and Chrzanowski, Mike and Kong, Lingpeng and Lazaridou, Angeliki and Ling, Wang and Yu, Lei and Dyer, Chris and Blunsom, Phil},
	month = jan,
	year = {2019},
	note = {arXiv:1901.11373 [cs, stat]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{noauthor_pdf_nodate-1,
	title = {[{PDF}] {FTFT}: efficient and robust {Fine}-{Tuning} by {transFerring} {Training} dynamics {\textbar} {Semantic} {Scholar}},
	url = {https://www.semanticscholar.org/paper/FTFT%3A-efficient-and-robust-Fine-Tuning-by-Training-Du-Gatt/84c11ef286da2611d7dabde2549f7a206e8a8db9},
	urldate = {2023-11-15},
}

@misc{noauthor_pdf_nodate-2,
	title = {[{PDF}] {Curating} {Datasets} for {Better} {Performance} with {Example} {Training} {Dynamics} {\textbar} {Semantic} {Scholar}},
	url = {https://www.semanticscholar.org/paper/Curating-Datasets-for-Better-Performance-with-Sar-Shalom-Schwartz/c8e0076c38901382e27b4c843b8a763a310619e9},
	urldate = {2023-11-15},
}

@misc{modarressi_guide_2023,
	title = {Guide the {Learner}: {Controlling} {Product} of {Experts} {Debiasing} {Method} {Based} on {Token} {Attribution} {Similarities}},
	shorttitle = {Guide the {Learner}},
	url = {http://arxiv.org/abs/2302.02852},
	doi = {10.48550/arXiv.2302.02852},
	abstract = {Several proposals have been put forward in recent years for improving out-of-distribution (OOD) performance through mitigating dataset biases. A popular workaround is to train a robust model by re-weighting training examples based on a secondary biased model. Here, the underlying assumption is that the biased model resorts to shortcut features. Hence, those training examples that are correctly predicted by the biased model are flagged as being biased and are down-weighted during the training of the main model. However, assessing the importance of an instance merely based on the predictions of the biased model may be too naive. It is possible that the prediction of the main model can be derived from another decision-making process that is distinct from the behavior of the biased model. To circumvent this, we introduce a fine-tuning strategy that incorporates the similarity between the main and biased model attribution scores in a Product of Experts (PoE) loss function to further improve OOD performance. With experiments conducted on natural language inference and fact verification benchmarks, we show that our method improves OOD results while maintaining in-distribution (ID) performance.},
	urldate = {2023-11-15},
	publisher = {arXiv},
	author = {Modarressi, Ali and Amirkhani, Hossein and Pilehvar, Mohammad Taher},
	month = feb,
	year = {2023},
	note = {arXiv:2302.02852 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{hernandez-olivan_survey_2022,
	title = {A {Survey} on {Artificial} {Intelligence} for {Music} {Generation}: {Agents}, {Domains} and {Perspectives}},
	shorttitle = {A {Survey} on {Artificial} {Intelligence} for {Music} {Generation}},
	url = {http://arxiv.org/abs/2210.13944},
	abstract = {Music is one of the Gardner's intelligences in his theory of multiple intelligences. How humans perceive and understand music is still being studied and is crucial to develop artificial intelligence models that imitate such processes. Music generation with Artificial Intelligence is an emerging field that is gaining much attention in the recent years. In this paper, we describe how humans compose music and how new AI systems could imitate such process by comparing past and recent advances in the field with music composition techniques. To understand how AI models and algorithms generate music and the potential applications that might appear in the future, we explore, analyze and describe the agents that take part of the music generation process: the datasets, models, interfaces, the users and the generated music. We mention possible applications that might benefit from this field and we also propose new trends and future research directions that could be explored in the future.},
	urldate = {2023-11-15},
	publisher = {arXiv},
	author = {Hernandez-Olivan, Carlos and Hernandez-Olivan, Javier and Beltran, Jose R.},
	month = nov,
	year = {2022},
	note = {arXiv:2210.13944 [cs, eess]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@inproceedings{liu_deep_2021,
	address = {Online and Punta Cana, Dominican Republic},
	title = {Deep {Attention} {Diffusion} {Graph} {Neural} {Networks} for {Text} {Classification}},
	url = {https://aclanthology.org/2021.emnlp-main.642},
	doi = {10.18653/v1/2021.emnlp-main.642},
	abstract = {Text classification is a fundamental task with broad applications in natural language processing. Recently, graph neural networks (GNNs) have attracted much attention due to their powerful representation ability. However, most existing methods for text classification based on GNNs consider only one-hop neighborhoods and low-frequency information within texts, which cannot fully utilize the rich context information of documents. Moreover, these models suffer from over-smoothing issues if many graph layers are stacked. In this paper, a Deep Attention Diffusion Graph Neural Network (DADGNN) model is proposed to learn text representations, bridging the chasm of interaction difficulties between a word and its distant neighbors. Experimental results on various standard benchmark datasets demonstrate the superior performance of the present approach.},
	urldate = {2023-11-15},
	booktitle = {Proceedings of the 2021 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Liu, Yonghao and Guan, Renchu and Giunchiglia, Fausto and Liang, Yanchun and Feng, Xiaoyue},
	editor = {Moens, Marie-Francine and Huang, Xuanjing and Specia, Lucia and Yih, Scott Wen-tau},
	month = nov,
	year = {2021},
	pages = {8142--8152},
}

@misc{gao_e3_2023,
	title = {E3 {TTS}: {Easy} {End}-to-{End} {Diffusion}-based {Text} to {Speech}},
	shorttitle = {E3 {TTS}},
	url = {http://arxiv.org/abs/2311.00945},
	doi = {10.48550/arXiv.2311.00945},
	abstract = {We propose Easy End-to-End Diffusion-based Text to Speech, a simple and efficient end-to-end text-to-speech model based on diffusion. E3 TTS directly takes plain text as input and generates an audio waveform through an iterative refinement process. Unlike many prior work, E3 TTS does not rely on any intermediate representations like spectrogram features or alignment information. Instead, E3 TTS models the temporal structure of the waveform through the diffusion process. Without relying on additional conditioning information, E3 TTS could support flexible latent structure within the given audio. This enables E3 TTS to be easily adapted for zero-shot tasks such as editing without any additional training. Experiments show that E3 TTS can generate high-fidelity audio, approaching the performance of a state-of-the-art neural TTS system. Audio samples are available at https://e3tts.github.io.},
	urldate = {2023-11-14},
	publisher = {arXiv},
	author = {Gao, Yuan and Morioka, Nobuyuki and Zhang, Yu and Chen, Nanxin},
	month = nov,
	year = {2023},
	note = {arXiv:2311.00945 [cs, eess]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{gao_e3_2023-1,
	title = {E3 {TTS}: {Easy} {End}-to-{End} {Diffusion}-based {Text} to {Speech}},
	shorttitle = {E3 {TTS}},
	url = {http://arxiv.org/abs/2311.00945},
	doi = {10.48550/arXiv.2311.00945},
	abstract = {We propose Easy End-to-End Diffusion-based Text to Speech, a simple and efficient end-to-end text-to-speech model based on diffusion. E3 TTS directly takes plain text as input and generates an audio waveform through an iterative refinement process. Unlike many prior work, E3 TTS does not rely on any intermediate representations like spectrogram features or alignment information. Instead, E3 TTS models the temporal structure of the waveform through the diffusion process. Without relying on additional conditioning information, E3 TTS could support flexible latent structure within the given audio. This enables E3 TTS to be easily adapted for zero-shot tasks such as editing without any additional training. Experiments show that E3 TTS can generate high-fidelity audio, approaching the performance of a state-of-the-art neural TTS system. Audio samples are available at https://e3tts.github.io.},
	urldate = {2023-11-14},
	publisher = {arXiv},
	author = {Gao, Yuan and Morioka, Nobuyuki and Zhang, Yu and Chen, Nanxin},
	month = nov,
	year = {2023},
	note = {arXiv:2311.00945 [cs, eess]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{chua_predicting_2022,
	title = {Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses},
	shorttitle = {Predicting emotion from music videos},
	url = {http://arxiv.org/abs/2202.10453},
	doi = {10.48550/arXiv.2202.10453},
	abstract = {Although media content is increasingly produced, distributed, and consumed in multiple combinations of modalities, how individual modalities contribute to the perceived emotion of a media item remains poorly understood. In this paper we present MusicVideos (MuVi), a novel dataset for affective multimedia content analysis to study how the auditory and visual modalities contribute to the perceived emotion of media. The data were collected by presenting music videos to participants in three conditions: music, visual, and audiovisual. Participants annotated the music videos for valence and arousal over time, as well as the overall emotion conveyed. We present detailed descriptive statistics for key measures in the dataset and the results of feature importance analyses for each condition. Finally, we propose a novel transfer learning architecture to train Predictive models Augmented with Isolated modality Ratings (PAIR) and demonstrate the potential of isolated modality ratings for enhancing multimodal emotion recognition. Our results suggest that perceptions of arousal are influenced primarily by auditory information, while perceptions of valence are more subjective and can be influenced by both visual and auditory information. The dataset is made publicly available.},
	urldate = {2023-11-14},
	publisher = {arXiv},
	author = {Chua, Phoebe and Makris, Dimos and Herremans, Dorien and Roig, Gemma and Agres, Kat},
	month = feb,
	year = {2022},
	note = {arXiv:2202.10453 [cs, eess]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@article{carnovalini_computational_2020,
	title = {Computational {Creativity} and {Music} {Generation} {Systems}: {An} {Introduction} to the {State} of the {Art}},
	volume = {3},
	issn = {2624-8212},
	shorttitle = {Computational {Creativity} and {Music} {Generation} {Systems}},
	url = {https://www.frontiersin.org/articles/10.3389/frai.2020.00014},
	abstract = {Computational Creativity is a multidisciplinary field that tries to obtain creative behaviors from computers. One of its most prolific subfields is that of Music Generation (also called Algorithmic Composition or Musical Metacreation), that uses computational means to compose music. Due to the multidisciplinary nature of this research field, it is sometimes hard to define precise goals and to keep track of what problems can be considered solved by state-of-the-art systems and what instead needs further developments. With this survey, we try to give a complete introduction to those who wish to explore Computational Creativity and Music Generation. To do so, we first give a picture of the research on the definition and the evaluation of creativity, both human and computational, needed to understand how computational means can be used to obtain creative behaviors and its importance within Artificial Intelligence studies. We then review the state of the art of Music Generation Systems, by citing examples for all the main approaches to music generation, and by listing the open challenges that were identified by previous reviews on the subject. For each of these challenges, we cite works that have proposed solutions, describing what still needs to be done and some possible directions for further research.},
	urldate = {2023-11-14},
	journal = {Frontiers in Artificial Intelligence},
	author = {Carnovalini, Filippo and Rodà, Antonio},
	year = {2020},
}

@inproceedings{daly_identifying_2015,
	title = {Identifying music-induced emotions from {EEG} for use in brain-computer music interfacing},
	url = {https://ieeexplore.ieee.org/document/7344685},
	doi = {10.1109/ACII.2015.7344685},
	abstract = {Brain-computer music interfaces (BCMI) provide a method to modulate an individuals affective state via the selection or generation of music according to their current affective state. Potential applications of such systems may include entertainment of therapeutic applications. We outline a proposed design for such a BCMI and seek a method for automatically differentiating different music induced affective states. Band-power features are explored for use in automatically identifying music-induced affective states. Additionally, a linear discriminant analysis classifier and a support vector machine are evaluated with respect to their ability to classify music induced affective states from the electroencephalogram recorded during a BCMI calibration task. Accuracies of up to 79.5\% (p {\textless}; 0.001) are achieved with the support vector machine.},
	urldate = {2023-11-14},
	booktitle = {2015 {International} {Conference} on {Affective} {Computing} and {Intelligent} {Interaction} ({ACII})},
	author = {Daly, Ian and Malik, Asad and Weaver, James and Hwang, Faustina and Nasuto, Slawmoir J. and Williams, Duncan and Kirke, Alexis and Miranda, Eduardo},
	month = sep,
	year = {2015},
	note = {ISSN: 2156-8111},
	pages = {923--929},
}

@article{noauthor_listen_nodate,
	title = {Listen to your {Mind}'s ({He}){Art}: {A} {System} for {Affective} {Music} {Generation} via {Brain}-{Computer} {Interface}},
	shorttitle = {Listen to your {Mind}'s ({He}){Art}},
	url = {https://zenodo.org/records/5044984},
	doi = {10.5281/zenodo.5044984},
	abstract = {We present an approach to the problem of real-time generation of music, driven by the affective state of the user, estimated from their electroencephalogram (EEG). This work is aimed at exploring strategies for real-time music generation applications using sensor data. Applications can range from responsive music for x-reality to art installations, and music generation as feedback in pedagogical contexts. We developed a Brain-Computer Interface in the open-source platform OpenViBE. It manages communication with the EEG device and computes the relevant features. A benchmark dataset is used to evaluate the performance of supervised learning methods on the binary classification task of valence and arousal. We also assessed the performance using a reduced number of electrodes and frequency-bands, in order to address the problems of lower budgets and noisy environments. Then, we address the requirements for a real-time music generation model and propose a modification to Magenta’s MusicVAE, introducing a parameter for controlling inter-batch memory. In the end, we discuss possible strategies to map desired music features to a model’s native input features. We present a Probabilistic Graphical Model to model the mapping from valence/arousal to MusicVAE’s latent variables. We also address dataset dimensionality problems proposing three probabilistic solutions.},
	language = {en},
	urldate = {2023-11-14},
}

@article{gannouni_emotion_2021,
	title = {Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification},
	volume = {11},
	copyright = {2021 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-021-86345-5},
	doi = {10.1038/s41598-021-86345-5},
	abstract = {Recognizing emotions using biological brain signals requires accurate and efficient signal processing and feature extraction methods. Existing methods use several techniques to extract useful features from a fixed number of electroencephalography (EEG) channels. The primary objective of this study was to improve the performance of emotion recognition using brain signals by applying a novel and adaptive channel selection method that acknowledges that brain activity has a unique behavior that differs from one person to another and one emotional state to another. Moreover, we propose identifying epochs, which are the instants at which excitation is maximum, during the emotion to improve the system’s accuracy. We used the zero-time windowing method to extract instantaneous spectral information using the numerator group-delay function to accurately detect the epochs in each emotional state. Different classification scheme were defined using QDC and RNN and evaluated using the DEAP database. The experimental results showed that the proposed method is highly competitive compared with existing studies of multi-class emotion recognition. The average accuracy rate exceeded 89\%. Compared with existing algorithms dealing with 9 emotions, the proposed method enhanced the accuracy rate by 8\%. Moreover, experiment shows that the proposed system outperforms similar approaches discriminating between 3 and 4 emotions only. We also found that the proposed method works well, even when applying conventional classification algorithms.},
	language = {en},
	number = {1},
	urldate = {2023-11-14},
	journal = {Scientific Reports},
	author = {Gannouni, Sofien and Aledaily, Arwa and Belwafi, Kais and Aboalsamh, Hatim},
	month = mar,
	year = {2021},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Computational biology and bioinformatics, Neuroscience},
	pages = {7071},
}

@article{scirea_affective_2017,
	title = {Affective evolutionary music composition with {MetaCompose}},
	volume = {18},
	issn = {1573-7632},
	url = {https://doi.org/10.1007/s10710-017-9307-y},
	doi = {10.1007/s10710-017-9307-y},
	abstract = {This paper describes the MetaCompose music generator,a compositional, extensible framework for affective music composition. In this context ‘affective’ refers to the music generator’s ability to express emotional information. The main purpose of MetaCompose is to create music in real-time that can express different mood-states, which we achieve through a unique combination of a graph traversal-based chord sequence generator, a search-based melody generator, a pattern-based accompaniment generator, and a theory for mood expression. Melody generation uses a novel evolutionary technique combining FI-2POP with multi-objective optimization. This allows us to explore a Pareto front of diverse solutions that are creatively equivalent under the terms of a multi-criteria objective function. Two quantitative user studies were performed to evaluate the system: one focusing on the music generation technique, and the other that explores valence expression,via the introduction of dissonances. The results of these studies demonstrate (i) that each part of the generation system improves the perceived quality of the music produced, and (ii) how valence expression via dissonance produces the perceived affective state. This system, which can reliably generate affect-expressive music, can subsequently be integrated in any kind of interactive application (e.g., games) to create an adaptive and dynamic soundtrack.},
	language = {en},
	number = {4},
	urldate = {2023-11-14},
	journal = {Genetic Programming and Evolvable Machines},
	author = {Scirea, Marco and Togelius, Julian and Eklund, Peter and Risi, Sebastian},
	month = dec,
	year = {2017},
	keywords = {Affective music, Creative computing, Evolutionary computing, Genetic algorithm, Music generation},
	pages = {433--465},
}

@article{herremans_functional_2018,
	title = {A {Functional} {Taxonomy} of {Music} {Generation} {Systems}},
	volume = {50},
	issn = {0360-0300, 1557-7341},
	url = {https://dl.acm.org/doi/10.1145/3108242},
	doi = {10.1145/3108242},
	abstract = {Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with reference to existing systems. The taxonomy organizes systems according to the purposes for which they were designed. It also reveals the inter-relatedness amongst the systems. This design-centered approach contrasts with predominant methods-based surveys and facilitates the identification of grand challenges to set the stage for new breakthroughs.},
	language = {en},
	number = {5},
	urldate = {2023-11-14},
	journal = {ACM Computing Surveys},
	author = {Herremans, Dorien and Chuan, Ching-Hua and Chew, Elaine},
	month = sep,
	year = {2018},
	pages = {1--30},
}

@misc{benchetrit_brain_2023,
	title = {Brain decoding: toward real-time reconstruction of visual perception},
	shorttitle = {Brain decoding},
	url = {http://arxiv.org/abs/2310.19812},
	doi = {10.48550/arXiv.2310.19812},
	abstract = {In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity. Visual perception, in particular, can now be decoded from functional Magnetic Resonance Imaging (fMRI) with remarkable fidelity. This neuroimaging technique, however, suffers from a limited temporal resolution (\${\textbackslash}approx\$0.5 Hz) and thus fundamentally constrains its real-time usage. Here, we propose an alternative approach based on magnetoencephalography (MEG), a neuroimaging device capable of measuring brain activity with high temporal resolution (\${\textbackslash}approx\$5,000 Hz). For this, we develop an MEG decoding model trained with both contrastive and regression objectives and consisting of three modules: i) pretrained embeddings obtained from the image, ii) an MEG module trained end-to-end and iii) a pretrained image generator. Our results are threefold: Firstly, our MEG decoder shows a 7X improvement of image-retrieval over classic linear decoders. Second, late brain responses to images are best decoded with DINOv2, a recent foundational image model. Third, image retrievals and generations both suggest that MEG signals primarily contain high-level visual features, whereas the same approach applied to 7T fMRI also recovers low-level features. Overall, these results provide an important step towards the decoding - in real time - of the visual processes continuously unfolding within the human brain.},
	urldate = {2023-11-14},
	publisher = {arXiv},
	author = {Benchetrit, Yohann and Banville, Hubert and King, Jean-Rémi},
	month = oct,
	year = {2023},
	note = {arXiv:2310.19812 [cs, eess, q-bio]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Neurons and Cognition},
}

@misc{noauthor_what_2023,
	title = {What is {Residual} {Vector} {Quantization}?},
	url = {https://www.assemblyai.com/blog/what-is-residual-vector-quantization/},
	abstract = {Neural Audio Compression methods based on Residual Vector Quantization are reshaping the landscape of modern audio codecs. In this guide, learn the basic ideas behind RVQ and how it enhances Neural Compression.},
	language = {en},
	urldate = {2023-11-13},
	journal = {News, Tutorials, AI Research},
	month = sep,
	year = {2023},
}

@misc{defossez_music_2021,
	title = {Music {Source} {Separation} in the {Waveform} {Domain}},
	url = {http://arxiv.org/abs/1911.13254},
	doi = {10.48550/arXiv.1911.13254},
	abstract = {Source separation for music is the task of isolating contributions, or stems, from different instruments recorded individually and arranged together to form a song. Such components include voice, bass, drums and any other accompaniments.Contrarily to many audio synthesis tasks where the best performances are achieved by models that directly generate the waveform, the state-of-the-art in source separation for music is to compute masks on the magnitude spectrum. In this paper, we compare two waveform domain architectures. We first adapt Conv-Tasnet, initially developed for speech source separation,to the task of music source separation. While Conv-Tasnet beats many existing spectrogram-domain methods, it suffersfrom significant artifacts, as shown by human evaluations. We propose instead Demucs, a novel waveform-to-waveform model,with a U-Net structure and bidirectional LSTM.Experiments on the MusDB dataset show that, with proper data augmentation, Demucs beats allexisting state-of-the-art architectures, including Conv-Tasnet, with 6.3 SDR on average, (and up to 6.8 with 150 extra training songs, even surpassing the IRM oracle for the bass source).Using recent development in model quantization, Demucs can be compressed down to 120MBwithout any loss of accuracy.We also provide human evaluations, showing that Demucs benefit from a large advantagein terms of the naturalness of the audio. However, it suffers from some bleeding,especially between the vocals and other source.},
	urldate = {2023-11-13},
	publisher = {arXiv},
	author = {Défossez, Alexandre and Usunier, Nicolas and Bottou, Léon and Bach, Francis},
	month = apr,
	year = {2021},
	note = {arXiv:1911.13254 [cs, eess, stat]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning},
}

@misc{noauthor_self-supervised_nodate,
	title = {Self-{Supervised} {VQ}-{VAE} for {One}-{Shot} {Music} {Style} {Transfer} {\textbar} {IEEE} {Conference} {Publication} {\textbar} {IEEE} {Xplore}},
	url = {https://ieeexplore.ieee.org/abstract/document/9414235},
	urldate = {2023-11-13},
}

@misc{wu_large-scale_2023,
	title = {Large-scale {Contrastive} {Language}-{Audio} {Pretraining} with {Feature} {Fusion} and {Keyword}-to-{Caption} {Augmentation}},
	url = {http://arxiv.org/abs/2211.06687},
	abstract = {Contrastive learning has shown remarkable success in the field of multimodal representation learning. In this paper, we propose a pipeline of contrastive language-audio pretraining to develop an audio representation by combining audio data with natural language descriptions. To accomplish this target, we first release LAION-Audio-630K, a large collection of 633,526 audio-text pairs from different data sources. Second, we construct a contrastive language-audio pretraining model by considering different audio encoders and text encoders. We incorporate the feature fusion mechanism and keyword-to-caption augmentation into the model design to further enable the model to process audio inputs of variable lengths and enhance the performance. Third, we perform comprehensive experiments to evaluate our model across three tasks: text-to-audio retrieval, zero-shot audio classification, and supervised audio classification. The results demonstrate that our model achieves superior performance in text-to-audio retrieval task. In audio classification tasks, the model achieves state-of-the-art performance in the zero-shot setting and is able to obtain performance comparable to models' results in the non-zero-shot setting. LAION-Audio-630K and the proposed model are both available to the public.},
	urldate = {2023-11-13},
	publisher = {arXiv},
	author = {Wu, Yusong and Chen, Ke and Zhang, Tianyu and Hui, Yuchen and Berg-Kirkpatrick, Taylor and Dubnov, Shlomo},
	month = apr,
	year = {2023},
	note = {arXiv:2211.06687 [cs, eess]},
	keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{noauthor_recent_2023,
	title = {Recent developments in {Generative} {AI} for {Audio}},
	url = {https://www.assemblyai.com/blog/recent-developments-in-generative-ai-for-audio/},
	abstract = {The spotlight has been on language and images for Generative AI, but there's been a lot of recent progress in the audio domain. Learn everything you need to know about generative audio models in this article.},
	language = {en},
	urldate = {2023-11-13},
	journal = {News, Tutorials, AI Research},
	month = jun,
	year = {2023},
}

@misc{kawar_imagic_2023,
	title = {Imagic: {Text}-{Based} {Real} {Image} {Editing} with {Diffusion} {Models}},
	shorttitle = {Imagic},
	url = {http://arxiv.org/abs/2210.09276},
	doi = {10.48550/arXiv.2210.09276},
	abstract = {Text-conditioned image editing has recently attracted considerable interest. However, most methods are currently either limited to specific editing types (e.g., object overlay, style transfer), or apply to synthetically generated images, or require multiple input images of a common object. In this paper we demonstrate, for the very first time, the ability to apply complex (e.g., non-rigid) text-guided semantic edits to a single real image. For example, we can change the posture and composition of one or multiple objects inside an image, while preserving its original characteristics. Our method can make a standing dog sit down or jump, cause a bird to spread its wings, etc. -- each within its single high-resolution natural image provided by the user. Contrary to previous work, our proposed method requires only a single input image and a target text (the desired edit). It operates on real images, and does not require any additional inputs (such as image masks or additional views of the object). Our method, which we call "Imagic", leverages a pre-trained text-to-image diffusion model for this task. It produces a text embedding that aligns with both the input image and the target text, while fine-tuning the diffusion model to capture the image-specific appearance. We demonstrate the quality and versatility of our method on numerous inputs from various domains, showcasing a plethora of high quality complex semantic image edits, all within a single unified framework.},
	urldate = {2023-11-13},
	publisher = {arXiv},
	author = {Kawar, Bahjat and Zada, Shiran and Lang, Oran and Tov, Omer and Chang, Huiwen and Dekel, Tali and Mosseri, Inbar and Irani, Michal},
	month = mar,
	year = {2023},
	note = {arXiv:2210.09276 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{kim_diffusionclip_2022,
	title = {{DiffusionCLIP}: {Text}-{Guided} {Diffusion} {Models} for {Robust} {Image} {Manipulation}},
	shorttitle = {{DiffusionCLIP}},
	url = {http://arxiv.org/abs/2110.02711},
	doi = {10.48550/arXiv.2110.02711},
	abstract = {Recently, GAN inversion methods combined with Contrastive Language-Image Pretraining (CLIP) enables zero-shot image manipulation guided by text prompts. However, their applications to diverse real images are still difficult due to the limited GAN inversion capability. Specifically, these approaches often have difficulties in reconstructing images with novel poses, views, and highly variable contents compared to the training data, altering object identity, or producing unwanted image artifacts. To mitigate these problems and enable faithful manipulation of real images, we propose a novel method, dubbed DiffusionCLIP, that performs text-driven image manipulation using diffusion models. Based on full inversion capability and high-quality image generation power of recent diffusion models, our method performs zero-shot image manipulation successfully even between unseen domains and takes another step towards general application by manipulating images from a widely varying ImageNet dataset. Furthermore, we propose a novel noise combination method that allows straightforward multi-attribute manipulation. Extensive experiments and human evaluation confirmed robust and superior manipulation performance of our methods compared to the existing baselines. Code is available at https://github.com/gwang-kim/DiffusionCLIP.git.},
	urldate = {2023-11-13},
	publisher = {arXiv},
	author = {Kim, Gwanghyun and Kwon, Taesung and Ye, Jong Chul},
	month = aug,
	year = {2022},
	note = {arXiv:2110.02711 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@misc{bermano_state---art_2022,
	title = {State-of-the-{Art} in the {Architecture}, {Methods} and {Applications} of {StyleGAN}},
	url = {http://arxiv.org/abs/2202.14020},
	doi = {10.48550/arXiv.2202.14020},
	abstract = {Generative Adversarial Networks (GANs) have established themselves as a prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks. This state-of-the-art report covers the StyleGAN architecture, and the ways it has been employed since its conception, while also analyzing its severe limitations. It aims to be of use for both newcomers, who wish to get a grasp of the field, and for more experienced readers that might benefit from seeing current research trends and existing tools laid out. Among StyleGAN's most interesting aspects is its learned latent space. Despite being learned with no supervision, it is surprisingly well-behaved and remarkably disentangled. Combined with StyleGAN's visual quality, these properties gave rise to unparalleled editing capabilities. However, the control offered by StyleGAN is inherently limited to the generator's learned distribution, and can only be applied to images generated by StyleGAN itself. Seeking to bring StyleGAN's latent control to real-world scenarios, the study of GAN inversion and latent space embedding has quickly gained in popularity. Meanwhile, this same study has helped shed light on the inner workings and limitations of StyleGAN. We map out StyleGAN's impressive story through these investigations, and discuss the details that have made StyleGAN the go-to generator. We further elaborate on the visual priors StyleGAN constructs, and discuss their use in downstream discriminative tasks. Looking forward, we point out StyleGAN's limitations and speculate on current trends and promising directions for future research, such as task and target specific fine-tuning.},
	urldate = {2023-11-13},
	publisher = {arXiv},
	author = {Bermano, Amit H. and Gal, Rinon and Alaluf, Yuval and Mokady, Ron and Nitzan, Yotam and Tov, Omer and Patashnik, Or and Cohen-Or, Daniel},
	month = feb,
	year = {2022},
	note = {arXiv:2202.14020 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics, Computer Science - Machine Learning},
}

@misc{bar-tal_text2live_2022,
	title = {{Text2LIVE}: {Text}-{Driven} {Layered} {Image} and {Video} {Editing}},
	shorttitle = {{Text2LIVE}},
	url = {http://arxiv.org/abs/2204.02491},
	doi = {10.48550/arXiv.2204.02491},
	abstract = {We present a method for zero-shot, text-driven appearance manipulation in natural images and videos. Given an input image or video and a target text prompt, our goal is to edit the appearance of existing objects (e.g., object's texture) or augment the scene with visual effects (e.g., smoke, fire) in a semantically meaningful manner. We train a generator using an internal dataset of training examples, extracted from a single input (image or video and target text prompt), while leveraging an external pre-trained CLIP model to establish our losses. Rather than directly generating the edited output, our key idea is to generate an edit layer (color+opacity) that is composited over the original input. This allows us to constrain the generation process and maintain high fidelity to the original input via novel text-driven losses that are applied directly to the edit layer. Our method neither relies on a pre-trained generator nor requires user-provided edit masks. We demonstrate localized, semantic edits on high-resolution natural images and videos across a variety of objects and scenes.},
	urldate = {2023-11-13},
	publisher = {arXiv},
	author = {Bar-Tal, Omer and Ofri-Amar, Dolev and Fridman, Rafail and Kasten, Yoni and Dekel, Tali},
	month = may,
	year = {2022},
	note = {arXiv:2204.02491 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{nichol_glide_2022,
	title = {{GLIDE}: {Towards} {Photorealistic} {Image} {Generation} and {Editing} with {Text}-{Guided} {Diffusion} {Models}},
	shorttitle = {{GLIDE}},
	url = {http://arxiv.org/abs/2112.10741},
	doi = {10.48550/arXiv.2112.10741},
	abstract = {Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing. We train a smaller model on a filtered dataset and release the code and weights at https://github.com/openai/glide-text2im.},
	urldate = {2023-11-13},
	publisher = {arXiv},
	author = {Nichol, Alex and Dhariwal, Prafulla and Ramesh, Aditya and Shyam, Pranav and Mishkin, Pamela and McGrew, Bob and Sutskever, Ilya and Chen, Mark},
	month = mar,
	year = {2022},
	note = {arXiv:2112.10741 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics, Computer Science - Machine Learning},
}

@misc{wang_imagen_2023,
	title = {Imagen {Editor} and {EditBench}: {Advancing} and {Evaluating} {Text}-{Guided} {Image} {Inpainting}},
	shorttitle = {Imagen {Editor} and {EditBench}},
	url = {http://arxiv.org/abs/2212.06909},
	doi = {10.48550/arXiv.2212.06909},
	abstract = {Text-guided image editing can have a transformative impact in supporting creative applications. A key challenge is to generate edits that are faithful to input text prompts, while consistent with input images. We present Imagen Editor, a cascaded diffusion model built, by fine-tuning Imagen on text-guided image inpainting. Imagen Editor's edits are faithful to the text prompts, which is accomplished by using object detectors to propose inpainting masks during training. In addition, Imagen Editor captures fine details in the input image by conditioning the cascaded pipeline on the original high resolution image. To improve qualitative and quantitative evaluation, we introduce EditBench, a systematic benchmark for text-guided image inpainting. EditBench evaluates inpainting edits on natural and generated images exploring objects, attributes, and scenes. Through extensive human evaluation on EditBench, we find that object-masking during training leads to across-the-board improvements in text-image alignment -- such that Imagen Editor is preferred over DALL-E 2 and Stable Diffusion -- and, as a cohort, these models are better at object-rendering than text-rendering, and handle material/color/size attributes better than count/shape attributes.},
	urldate = {2023-11-13},
	publisher = {arXiv},
	author = {Wang, Su and Saharia, Chitwan and Montgomery, Ceslee and Pont-Tuset, Jordi and Noy, Shai and Pellegrini, Stefano and Onoe, Yasumasa and Laszlo, Sarah and Fleet, David J. and Soricut, Radu and Baldridge, Jason and Norouzi, Mohammad and Anderson, Peter and Chan, William},
	month = apr,
	year = {2023},
	note = {arXiv:2212.06909 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition},
}

@misc{hsiao_compound_2021,
	title = {Compound {Word} {Transformer}: {Learning} to {Compose} {Full}-{Song} {Music} over {Dynamic} {Directed} {Hypergraphs}},
	shorttitle = {Compound {Word} {Transformer}},
	url = {http://arxiv.org/abs/2101.02402},
	abstract = {To apply neural sequence models such as the Transformers to music generation tasks, one has to represent a piece of music by a sequence of tokens drawn from a finite set of pre-defined vocabulary. Such a vocabulary usually involves tokens of various types. For example, to describe a musical note, one needs separate tokens to indicate the note's pitch, duration, velocity (dynamics), and placement (onset time) along the time grid. While different types of tokens may possess different properties, existing models usually treat them equally, in the same way as modeling words in natural languages. In this paper, we present a conceptually different approach that explicitly takes into account the type of the tokens, such as note types and metric types. And, we propose a new Transformer decoder architecture that uses different feed-forward heads to model tokens of different types. With an expansion-compression trick, we convert a piece of music to a sequence of compound words by grouping neighboring tokens, greatly reducing the length of the token sequences. We show that the resulting model can be viewed as a learner over dynamic directed hypergraphs. And, we employ it to learn to compose expressive Pop piano music of full-song length (involving up to 10K individual tokens per song), both conditionally and unconditionally. Our experiment shows that, compared to state-of-the-art models, the proposed model converges 5--10 times faster at training (i.e., within a day on a single GPU with 11 GB memory), and with comparable quality in the generated music.},
	urldate = {2023-11-13},
	publisher = {arXiv},
	author = {Hsiao, Wen-Yi and Liu, Jen-Yu and Yeh, Yin-Cheng and Yang, Yi-Hsuan},
	month = jan,
	year = {2021},
	note = {arXiv:2101.02402 [cs, eess]
version: 1},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{dhariwal_jukebox_2020-1,
	title = {Jukebox: {A} {Generative} {Model} for {Music}},
	shorttitle = {Jukebox},
	url = {http://arxiv.org/abs/2005.00341},
	doi = {10.48550/arXiv.2005.00341},
	abstract = {We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multi-scale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples at https://jukebox.openai.com, along with model weights and code at https://github.com/openai/jukebox},
	urldate = {2023-11-13},
	publisher = {arXiv},
	author = {Dhariwal, Prafulla and Jun, Heewoo and Payne, Christine and Kim, Jong Wook and Radford, Alec and Sutskever, Ilya},
	month = apr,
	year = {2020},
	note = {arXiv:2005.00341 [cs, eess, stat]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning},
}

@article{li_melodydiffusion_2023,
	title = {{MelodyDiffusion}: {Chord}-{Conditioned} {Melody} {Generation} {Using} a {Transformer}-{Based} {Diffusion} {Model}},
	volume = {11},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2227-7390},
	shorttitle = {{MelodyDiffusion}},
	url = {https://www.mdpi.com/2227-7390/11/8/1915},
	doi = {10.3390/math11081915},
	abstract = {Artificial intelligence, particularly machine learning, has begun to permeate various real-world applications and is continually being explored in automatic music generation. The approaches to music generation can be broadly divided into two categories: rule-based and data-driven methods. Rule-based approaches rely on substantial prior knowledge and may struggle to handle large datasets, whereas data-driven approaches can solve these problems and have become increasingly popular. However, data-driven approaches still face challenges such as the difficulty of considering long-distance dependencies when handling discrete-sequence data and convergence during model training. Although the diffusion model has been introduced as a generative model to solve the convergence problem in generative adversarial networks, it has not yet been applied to discrete-sequence data. This paper proposes a transformer-based diffusion model known as MelodyDiffusion to handle discrete musical data and realize chord-conditioned melody generation. MelodyDiffusion replaces the U-nets used in traditional diffusion models with transformers to consider the long-distance dependencies using attention and parallel mechanisms. Moreover, a transformer-based encoder is designed to extract contextual information from chords as a condition to guide melody generation. MelodyDiffusion can automatically generate diverse melodies based on the provided chords in practical applications. The evaluation experiments, in which Hits@k was used as a metric to evaluate the restored melodies, demonstrate that the large-scale version of MelodyDiffusion achieves an accuracy of 72.41\% (k = 1).},
	language = {en},
	number = {8},
	urldate = {2023-11-13},
	journal = {Mathematics},
	author = {Li, Shuyu and Sung, Yunsick},
	month = jan,
	year = {2023},
	note = {Number: 8
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {conditional generation, diffusion model, melody generation, transformer},
	pages = {1915},
}

@misc{lu_meloform_2022,
	title = {{MeloForm}: {Generating} {Melody} with {Musical} {Form} based on {Expert} {Systems} and {Neural} {Networks}},
	shorttitle = {{MeloForm}},
	url = {http://arxiv.org/abs/2208.14345},
	doi = {10.48550/arXiv.2208.14345},
	abstract = {Human usually composes music by organizing elements according to the musical form to express music ideas. However, for neural network-based music generation, it is difficult to do so due to the lack of labelled data on musical form. In this paper, we develop MeloForm, a system that generates melody with musical form using expert systems and neural networks. Specifically, 1) we design an expert system to generate a melody by developing musical elements from motifs to phrases then to sections with repetitions and variations according to pre-given musical form; 2) considering the generated melody is lack of musical richness, we design a Transformer based refinement model to improve the melody without changing its musical form. MeloForm enjoys the advantages of precise musical form control by expert systems and musical richness learning via neural models. Both subjective and objective experimental evaluations demonstrate that MeloForm generates melodies with precise musical form control with 97.79\% accuracy, and outperforms baseline systems in terms of subjective evaluation score by 0.75, 0.50, 0.86 and 0.89 in structure, thematic, richness and overall quality, without any labelled musical form data. Besides, MeloForm can support various kinds of forms, such as verse and chorus form, rondo form, variational form, sonata form, etc.},
	urldate = {2023-11-13},
	publisher = {arXiv},
	author = {Lu, Peiling and Tan, Xu and Yu, Botao and Qin, Tao and Zhao, Sheng and Liu, Tie-Yan},
	month = aug,
	year = {2022},
	note = {arXiv:2208.14345 [cs, eess]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{hyun_commu_2022,
	title = {{ComMU}: {Dataset} for {Combinatorial} {Music} {Generation}},
	shorttitle = {{ComMU}},
	url = {http://arxiv.org/abs/2211.09385},
	doi = {10.48550/arXiv.2211.09385},
	abstract = {Commercial adoption of automatic music composition requires the capability of generating diverse and high-quality music suitable for the desired context (e.g., music for romantic movies, action games, restaurants, etc.). In this paper, we introduce combinatorial music generation, a new task to create varying background music based on given conditions. Combinatorial music generation creates short samples of music with rich musical metadata, and combines them to produce a complete music. In addition, we introduce ComMU, the first symbolic music dataset consisting of short music samples and their corresponding 12 musical metadata for combinatorial music generation. Notable properties of ComMU are that (1) dataset is manually constructed by professional composers with an objective guideline that induces regularity, and (2) it has 12 musical metadata that embraces composers' intentions. Our results show that we can generate diverse high-quality music only with metadata, and that our unique metadata such as track-role and extended chord quality improves the capacity of the automatic composition. We highly recommend watching our video before reading the paper (https://pozalabs.github.io/ComMU).},
	urldate = {2023-11-13},
	publisher = {arXiv},
	author = {Hyun, Lee and Kim, Taehyun and Kang, Hyolim and Ki, Minjoo and Hwang, Hyeonchan and Park, Kwanho and Han, Sharang and Kim, Seon Joo},
	month = nov,
	year = {2022},
	note = {arXiv:2211.09385 [cs, eess]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{atassi_generating_2023,
	title = {Generating symbolic music using diffusion models},
	url = {http://arxiv.org/abs/2303.08385},
	doi = {10.48550/arXiv.2303.08385},
	abstract = {Denoising Diffusion Probabilistic models have emerged as simple yet very powerful generative models. Unlike other generative models, diffusion models do not suffer from mode collapse or require a discriminator to generate high-quality samples. In this paper, a diffusion model that uses a binomial prior distribution to generate piano rolls is proposed. The paper also proposes an efficient method to train the model and generate samples. The generated music has coherence at time scales up to the length of the training piano roll segments. The paper demonstrates how this model is conditioned on the input and can be used to harmonize a given melody, complete an incomplete piano roll, or generate a variation of a given piece. The code is publicly shared to encourage the use and development of the method by the community.},
	urldate = {2023-11-13},
	publisher = {arXiv},
	author = {Atassi, Lilac},
	month = may,
	year = {2023},
	note = {arXiv:2303.08385 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{wang_pop909_2020,
	title = {{POP909}: {A} {Pop}-song {Dataset} for {Music} {Arrangement} {Generation}},
	shorttitle = {{POP909}},
	url = {http://arxiv.org/abs/2008.07142},
	doi = {10.48550/arXiv.2008.07142},
	abstract = {Music arrangement generation is a subtask of automatic music generation, which involves reconstructing and re-conceptualizing a piece with new compositional techniques. Such a generation process inevitably requires reference from the original melody, chord progression, or other structural information. Despite some promising models for arrangement, they lack more refined data to achieve better evaluations and more practical results. In this paper, we propose POP909, a dataset which contains multiple versions of the piano arrangements of 909 popular songs created by professional musicians. The main body of the dataset contains the vocal melody, the lead instrument melody, and the piano accompaniment for each song in MIDI format, which are aligned to the original audio files. Furthermore, we provide the annotations of tempo, beat, key, and chords, where the tempo curves are hand-labeled and others are done by MIR algorithms. Finally, we conduct several baseline experiments with this dataset using standard deep music generation algorithms.},
	urldate = {2023-11-13},
	publisher = {arXiv},
	author = {Wang, Ziyu and Chen, Ke and Jiang, Junyan and Zhang, Yiyi and Xu, Maoran and Dai, Shuqi and Gu, Xianbin and Xia, Gus},
	month = aug,
	year = {2020},
	note = {arXiv:2008.07142 [cs, eess]},
	keywords = {Computer Science - Information Retrieval, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{maina_msanii_2023,
	title = {Msanii: {High} {Fidelity} {Music} {Synthesis} on a {Shoestring} {Budget}},
	shorttitle = {Msanii},
	url = {http://arxiv.org/abs/2301.06468},
	doi = {10.48550/arXiv.2301.06468},
	abstract = {In this paper, we present Msanii, a novel diffusion-based model for synthesizing long-context, high-fidelity music efficiently. Our model combines the expressiveness of mel spectrograms, the generative capabilities of diffusion models, and the vocoding capabilities of neural vocoders. We demonstrate the effectiveness of Msanii by synthesizing tens of seconds (190 seconds) of stereo music at high sample rates (44.1 kHz) without the use of concatenative synthesis, cascading architectures, or compression techniques. To the best of our knowledge, this is the first work to successfully employ a diffusion-based model for synthesizing such long music samples at high sample rates. Our demo can be found https://kinyugo.github.io/msanii-demo and our code https://github.com/Kinyugo/msanii .},
	urldate = {2023-11-13},
	publisher = {arXiv},
	author = {Maina, Kinyugo},
	month = jan,
	year = {2023},
	note = {arXiv:2301.06468 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, I.2.7},
}

@misc{noauthor_home_nodate,
	title = {Home},
	url = {https://kinyugo.github.io/msanii-demo/},
	abstract = {This is the demo website for the paper: “Msanii: High Fidelity Music Synthesis on a Shoestring Budget”.},
	language = {en},
	urldate = {2023-11-13},
	journal = {Msanii paper demo},
}

@misc{noauthor_vall-e_nodate,
	title = {{VALL}-{E} ({X})},
	url = {https://www.microsoft.com/en-us/research/project/vall-e-x/},
	abstract = {VALL-E is a neural codec language model using discrete codes derived from an off-the-shelf neural audio codec model, and regard TTS as a conditional language modeling task rather. VALL-E emerges in-context learning capabilities and can be used to synthesize high-quality personalized speech with only a 3-second enrolled recording of an unseen speaker as a prompt. We also extend VALL-E and train a multi-lingual conditional codec language model. VALL-E X can generate high-quality speech in the target language via just one speech utterance in the source language as a prompt while preserving the unseen speaker’s voice, emotion, and acoustic environment.},
	language = {en-US},
	urldate = {2023-11-13},
	journal = {Microsoft Research},
}

@misc{wang_neural_2023,
	title = {Neural {Codec} {Language} {Models} are {Zero}-{Shot} {Text} to {Speech} {Synthesizers}},
	url = {http://arxiv.org/abs/2301.02111},
	doi = {10.48550/arXiv.2301.02111},
	abstract = {We introduce a language modeling approach for text to speech synthesis (TTS). Specifically, we train a neural codec language model (called Vall-E) using discrete codes derived from an off-the-shelf neural audio codec model, and regard TTS as a conditional language modeling task rather than continuous signal regression as in previous work. During the pre-training stage, we scale up the TTS training data to 60K hours of English speech which is hundreds of times larger than existing systems. Vall-E emerges in-context learning capabilities and can be used to synthesize high-quality personalized speech with only a 3-second enrolled recording of an unseen speaker as an acoustic prompt. Experiment results show that Vall-E significantly outperforms the state-of-the-art zero-shot TTS system in terms of speech naturalness and speaker similarity. In addition, we find Vall-E could preserve the speaker's emotion and acoustic environment of the acoustic prompt in synthesis. See https://aka.ms/valle for demos of our work.},
	urldate = {2023-11-13},
	publisher = {arXiv},
	author = {Wang, Chengyi and Chen, Sanyuan and Wu, Yu and Zhang, Ziqiang and Zhou, Long and Liu, Shujie and Chen, Zhuo and Liu, Yanqing and Wang, Huaming and Li, Jinyu and He, Lei and Zhao, Sheng and Wei, Furu},
	month = jan,
	year = {2023},
	note = {arXiv:2301.02111 [cs, eess]},
	keywords = {Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{bai_accelerating_2023,
	title = {Accelerating {Diffusion}-{Based} {Text}-to-{Audio} {Generation} with {Consistency} {Distillation}},
	url = {http://arxiv.org/abs/2309.10740},
	doi = {10.48550/arXiv.2309.10740},
	abstract = {Diffusion models power a vast majority of text-to-audio (TTA) generation methods. Unfortunately, these models suffer from slow inference speed due to iterative queries to the underlying denoising network, thus unsuitable for scenarios with inference time or computational constraints. This work modifies the recently proposed consistency distillation framework to train TTA models that require only a single neural network query. In addition to incorporating classifier-free guidance into the distillation process, we leverage the availability of generated audio during distillation training to fine-tune the consistency TTA model with novel loss functions in the audio space, such as the CLAP score. Our objective and subjective evaluation results on the AudioCaps dataset show that consistency models retain diffusion models' high generation quality and diversity while reducing the number of queries by a factor of 400.},
	urldate = {2023-11-13},
	publisher = {arXiv},
	author = {Bai, Yatong and Dang, Trung and Tran, Dung and Koishida, Kazuhito and Sojoudi, Somayeh},
	month = sep,
	year = {2023},
	note = {arXiv:2309.10740 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{noauthor_audio_2023-1,
	title = {Audio {AI} {Timeline}},
	url = {https://github.com/archinetai/audio-ai-timeline},
	abstract = {A timeline of the latest AI models for audio generation, starting in 2023!},
	urldate = {2023-11-13},
	publisher = {Ȧrchinet},
	month = nov,
	year = {2023},
	note = {original-date: 2023-01-29T09:58:56Z},
	keywords = {artificial-intelligence, audio-generation, machine-learning},
}

@misc{verma_neural_2018,
	title = {Neural {Style} {Transfer} for {Audio} {Spectograms}},
	url = {http://arxiv.org/abs/1801.01589},
	doi = {10.48550/arXiv.1801.01589},
	abstract = {There has been fascinating work on creating artistic transformations of images by Gatys. This was revolutionary in how we can in some sense alter the 'style' of an image while generally preserving its 'content'. In our work, we present a method for creating new sounds using a similar approach, treating it as a style-transfer problem, starting from a random-noise input signal and iteratively using back-propagation to optimize the sound to conform to filter-outputs from a pre-trained neural architecture of interest. For demonstration, we investigate two different tasks, resulting in bandwidth expansion/compression, and timbral transfer from singing voice to musical instruments. A feature of our method is that a single architecture can generate these different audio-style-transfer types using the same set of parameters which otherwise require different complex hand-tuned diverse signal processing pipelines.},
	urldate = {2023-11-12},
	publisher = {arXiv},
	author = {Verma, Prateek and Smith, Julius O.},
	month = jan,
	year = {2018},
	note = {arXiv:1801.01589 [cs, eess]},
	keywords = {Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{borsos_audiolm_2023,
	title = {{AudioLM}: a {Language} {Modeling} {Approach} to {Audio} {Generation}},
	shorttitle = {{AudioLM}},
	url = {http://arxiv.org/abs/2209.03143},
	doi = {10.48550/arXiv.2209.03143},
	abstract = {We introduce AudioLM, a framework for high-quality audio generation with long-term consistency. AudioLM maps the input audio to a sequence of discrete tokens and casts audio generation as a language modeling task in this representation space. We show how existing audio tokenizers provide different trade-offs between reconstruction quality and long-term structure, and we propose a hybrid tokenization scheme to achieve both objectives. Namely, we leverage the discretized activations of a masked language model pre-trained on audio to capture long-term structure and the discrete codes produced by a neural audio codec to achieve high-quality synthesis. By training on large corpora of raw audio waveforms, AudioLM learns to generate natural and coherent continuations given short prompts. When trained on speech, and without any transcript or annotation, AudioLM generates syntactically and semantically plausible speech continuations while also maintaining speaker identity and prosody for unseen speakers. Furthermore, we demonstrate how our approach extends beyond speech by generating coherent piano music continuations, despite being trained without any symbolic representation of music.},
	urldate = {2023-11-12},
	publisher = {arXiv},
	author = {Borsos, Zalán and Marinier, Raphaël and Vincent, Damien and Kharitonov, Eugene and Pietquin, Olivier and Sharifi, Matt and Roblek, Dominik and Teboul, Olivier and Grangier, David and Tagliasacchi, Marco and Zeghidour, Neil},
	month = jul,
	year = {2023},
	note = {arXiv:2209.03143 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{copet_simple_2023,
	title = {Simple and {Controllable} {Music} {Generation}},
	url = {http://arxiv.org/abs/2306.05284},
	doi = {10.48550/arXiv.2306.05284},
	abstract = {We tackle the task of conditional music generation. We introduce MusicGen, a single Language Model (LM) that operates over several streams of compressed discrete music representation, i.e., tokens. Unlike prior work, MusicGen is comprised of a single-stage transformer LM together with efficient token interleaving patterns, which eliminates the need for cascading several models, e.g., hierarchically or upsampling. Following this approach, we demonstrate how MusicGen can generate high-quality samples, both mono and stereo, while being conditioned on textual description or melodic features, allowing better controls over the generated output. We conduct extensive empirical evaluation, considering both automatic and human studies, showing the proposed approach is superior to the evaluated baselines on a standard text-to-music benchmark. Through ablation studies, we shed light over the importance of each of the components comprising MusicGen. Music samples, code, and models are available at https://github.com/facebookresearch/audiocraft},
	urldate = {2023-11-12},
	publisher = {arXiv},
	author = {Copet, Jade and Kreuk, Felix and Gat, Itai and Remez, Tal and Kant, David and Synnaeve, Gabriel and Adi, Yossi and Défossez, Alexandre},
	month = nov,
	year = {2023},
	note = {arXiv:2306.05284 [cs, eess]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{noauthor_ai-based_nodate,
	title = {{AI}-{Based} {Affective} {Music} {Generation} {Systems}: {A} {Review} of {Methods}, and {Challenges}},
	shorttitle = {{AI}-{Based} {Affective} {Music} {Generation} {Systems}},
	url = {https://ar5iv.labs.arxiv.org/html/2301.06890},
	abstract = {Music is a powerful medium for altering the emotional state of the listener. In recent years, with significant advancement in computing capabilities, artificial intelligence-based (AI-based) approaches have become popu…},
	language = {en},
	urldate = {2023-11-12},
	journal = {ar5iv},
}

@article{cifka_groove2groove_2020,
	title = {{Groove2Groove}: {One}-{Shot} {Music} {Style} {Transfer} with {Supervision} from {Synthetic} {Data}},
	volume = {28},
	shorttitle = {{Groove2Groove}},
	url = {https://hal.science/hal-02923548},
	doi = {10.1109/TASLP.2020.3019642},
	abstract = {Style transfer is the process of changing the style of an image, video, audio clip or musical piece so as to match the style of a given example. Even though the task has interesting practical applications within the music industry, it has so far received little attention from the audio and music processing community. In this paper, we present Groove2Groove, a one-shot style transfer method for symbolic music, focusing on the case of accompaniment styles in popular music and jazz. We propose an encoder-decoder neural network for the task, along with a synthetic data generation scheme to supply it with parallel training examples. This synthetic parallel data allows us to tackle the style transfer problem using end-to-end supervised learning, employing powerful techniques used in natural language processing. We experimentally demonstrate the performance of the model on style transfer using existing and newly proposed metrics, and also explore the possibility of style interpolation.},
	urldate = {2023-11-12},
	journal = {IEEE/ACM Transactions on Audio, Speech and Language Processing},
	author = {Cífka, Ondřej and Şimşekli, Umut and Richard, Gael},
	year = {2020},
	note = {Publisher: Institute of Electrical and Electronics Engineers},
	keywords = {deep learning, recurrent neural networks, style transfer, symbolic music, synthetic data},
	pages = {2638--2650},
}

@misc{noauthor_groove2groove_nodate,
	title = {{Groove2Groove}},
	url = {https://groove2groove.telecom-paris.fr},
	abstract = {One-shot style transfer for music accompaniments},
	language = {en-US},
	urldate = {2023-11-12},
	journal = {Groove2Groove},
}

@article{tamer_high-resolution_2023,
	title = {High-resolution violin transcription using weak labels},
	copyright = {© Nazif Can Tamer, Yigitcan Özer, Meinard Müller, Xavier Serra. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).},
	url = {http://repositori.upf.edu/handle/10230/58121},
	abstract = {A descriptive transcription of a violin performance requires detecting not only the notes but also the fine-grained pitch variations, such as vibrato. Most existing deep learning methods for music transcription do not capture these variations and often need frame-level annotations, which are scarce for the violin. In this paper, we propose a novel method for high-resolution violin transcription that can leverage piece-level weak labels for training. Our conformer-based model works on the raw audio waveform and transcribes violin notes and their corresponding pitch deviations with 5.8 ms frame resolution and 10-cent frequency resolution. We demonstrate that our method (1) outperforms generic systems in the proxy tasks of violin transcription and pitch estimation, and (2) can automatically generate new training labels by aligning its feature representations with unseen scores. We share our model along with 34 hours of score-aligned solo violin performance dataset, notably including the 24 Paganini Caprices.},
	language = {eng},
	urldate = {2023-11-12},
	author = {Tamer, Nazif Can and Özer, Yigitcan and Müller, Meinard and Serra, Xavier},
	month = oct,
	year = {2023},
	note = {Accepted: 2023-10-24T12:12:12Z},
}

@phdthesis{ewert_signal_2012,
	type = {Thesis},
	title = {Signal {Processing} {Methods} for {Music} {Synchronization}, {Audio} {Matching}, and {Source} {Separation}},
	copyright = {In Copyright},
	url = {https://bonndoc.ulb.uni-bonn.de/xmlui/handle/20.500.11811/5410},
	abstract = {The field of music information retrieval (MIR) aims at developing techniques and tools for organizing, understanding, and searching multimodal information in large music collections in a robust, efficient and intelligent manner. In this context, this thesis presents novel, content-based methods for music synchronization, audio matching, and source separation.{\textless}br /{\textgreater} In general, music synchronization denotes a procedure which, for a given position in one representation of a piece of music, determines the corresponding position within another representation. Here, the thesis presents three complementary synchronization approaches, which improve upon previous methods in terms of robustness, reliability, and accuracy. The first approach employs a late-fusion strategy based on multiple, conceptually different alignment techniques to identify those music passages that allow for reliable alignment results. The second approach is based on the idea of employing musical structure analysis methods in the context of synchronization to derive reliable synchronization results even in the presence of structural differences between the versions to be aligned. Finally, the third approach employs several complementary strategies for increasing the accuracy and time resolution of synchronization results. {\textless}br /{\textgreater} Given a short query audio clip, the goal of audio matching is to automatically retrieve all musically similar excerpts in different versions and arrangements of the same underlying piece of music. In this context, chroma-based audio features are a well-established tool as they possess a high degree of invariance to variations in timbre. This thesis describes a novel procedure for making chroma features even more robust to changes in timbre while keeping their discriminative power. Here, the idea is to identify and discard timbre-related information using techniques inspired by the well-known MFCC features, which are usually employed in speech processing. {\textless}br /{\textgreater} Given a monaural music recording, the goal of source separation is to extract musically meaningful sound sources corresponding, for example, to a melody, an instrument, or a drum track from the recording. To facilitate this complex task, one can exploit additional information provided by a musical score. Based on this idea, this thesis presents two novel, conceptually different approaches to source separation. Using score information provided by a given MIDI file, the first approach employs a parametric model to describe a given audio recording of a piece of music. The resulting model is then used to extract sound sources as specified by the score. As a computationally less demanding and easier to implement alternative, the second approach employs the additional score information to guide a decomposition based on non-negative matrix factorization (NMF).},
	language = {eng},
	urldate = {2023-11-12},
	school = {Universitäts- und Landesbibliothek Bonn},
	author = {Ewert, Sebastian},
	month = nov,
	year = {2012},
	note = {Accepted: 2020-04-18T03:04:35Z},
}

@misc{kawar_imagic_2023,
	title = {Imagic: {Text}-{Based} {Real} {Image} {Editing} with {Diffusion} {Models}},
	shorttitle = {Imagic},
	url = {http://arxiv.org/abs/2210.09276},
	doi = {10.48550/arXiv.2210.09276},
	abstract = {Text-conditioned image editing has recently attracted considerable interest. However, most methods are currently either limited to specific editing types (e.g., object overlay, style transfer), or apply to synthetically generated images, or require multiple input images of a common object. In this paper we demonstrate, for the very first time, the ability to apply complex (e.g., non-rigid) text-guided semantic edits to a single real image. For example, we can change the posture and composition of one or multiple objects inside an image, while preserving its original characteristics. Our method can make a standing dog sit down or jump, cause a bird to spread its wings, etc. -- each within its single high-resolution natural image provided by the user. Contrary to previous work, our proposed method requires only a single input image and a target text (the desired edit). It operates on real images, and does not require any additional inputs (such as image masks or additional views of the object). Our method, which we call "Imagic", leverages a pre-trained text-to-image diffusion model for this task. It produces a text embedding that aligns with both the input image and the target text, while fine-tuning the diffusion model to capture the image-specific appearance. We demonstrate the quality and versatility of our method on numerous inputs from various domains, showcasing a plethora of high quality complex semantic image edits, all within a single unified framework.},
	urldate = {2023-11-12},
	publisher = {arXiv},
	author = {Kawar, Bahjat and Zada, Shiran and Lang, Oran and Tov, Omer and Chang, Huiwen and Dekel, Tali and Mosseri, Inbar and Irani, Michal},
	month = mar,
	year = {2023},
	note = {arXiv:2210.09276 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{noauthor_brain2music_nodate,
	title = {{Brain2Music}},
	url = {https://google-research.github.io/seanet/brain2music/},
	urldate = {2023-11-12},
}

@misc{noauthor_towards_nodate,
	title = {Towards a {Real}-{Time} {Decoding} of {Images} from {Brain} {Activity}},
	url = {https://ai.meta.com/blog/brain-ai-image-decoding-meg-magnetoencephalography/},
	abstract = {Using magnetoencephalography (MEG), Meta showcases an AI system capable of decoding the unfolding of visual representations in the brain with an unprecedented temporal resolution.},
	language = {en},
	urldate = {2023-11-12},
	journal = {Meta AI},
}

@misc{noauthor_nitish_nodate,
	title = {Nitish {Srivastava}, {Geoffrey} {Hinton}, {Alex} {Krizhevsky}, {Ilya} {Sutskever}, and {Ruslan} {Salakhutdinov}. 2014. {Dropout}: a simple way to prevent neural networks from overfitting. {The} journal of machine learning research, 15(1):1929–1958. - {Google} {Search}},
	url = {https://www.google.com/search?q=Nitish+Srivastava%2C+Geoffrey+Hinton%2C+Alex+Krizhevsky%2C+Ilya+Sutskever%2C+and+Ruslan+Salakhutdinov.+2014.+Dropout%3A+a+simple+way+to+prevent+neural+networks+from+overfitting.+The+journal+of+machine+learning+research%2C+15(1)%3A1929%E2%80%931958.&rlz=1C1CHBD_en-GBGB1001GB1001&oq=Nitish+Srivastava%2C+Geoffrey+Hinton%2C+Alex+Krizhevsky%2C+Ilya+Sutskever%2C+and+Ruslan+Salakhutdinov.+2014.+Dropout%3A+a+simple+way+to+prevent+neural+networks+from+overfitting.+The+journal+of+machine+learning+research%2C+15(1)%3A1929%E2%80%931958.&gs_lcrp=EgZjaHJvbWUyBggAEEUYOdIBBzIyOWowajeoAgCwAgA&sourceid=chrome&ie=UTF-8},
	urldate = {2023-11-11},
}

@misc{loshchilov_online_2016,
	title = {Online {Batch} {Selection} for {Faster} {Training} of {Neural} {Networks}},
	url = {http://arxiv.org/abs/1511.06343},
	doi = {10.48550/arXiv.1511.06343},
	abstract = {Deep neural networks are commonly trained using stochastic non-convex optimization procedures, which are driven by gradient information estimated on fractions (batches) of the dataset. While it is commonly accepted that batch size is an important parameter for offline tuning, the benefits of online selection of batches remain poorly understood. We investigate online batch selection strategies for two state-of-the-art methods of stochastic gradient-based optimization, AdaDelta and Adam. As the loss function to be minimized for the whole dataset is an aggregation of loss functions of individual datapoints, intuitively, datapoints with the greatest loss should be considered (selected in a batch) more frequently. However, the limitations of this intuition and the proper control of the selection pressure over time are open questions. We propose a simple strategy where all datapoints are ranked w.r.t. their latest known loss value and the probability to be selected decays exponentially as a function of rank. Our experimental results on the MNIST dataset suggest that selecting batches speeds up both AdaDelta and Adam by a factor of about 5.},
	urldate = {2023-11-11},
	publisher = {arXiv},
	author = {Loshchilov, Ilya and Hutter, Frank},
	month = apr,
	year = {2016},
	note = {arXiv:1511.06343 [cs, math]},
	keywords = {Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Mathematics - Optimization and Control},
}

@inproceedings{singh_transient_2023,
	title = {The {Transient} {Nature} of {Emergent} {In}-{Context} {Learning} in {Transformers}},
	url = {https://openreview.net/forum?id=Of0GBzow8P},
	abstract = {Transformer neural networks can exhibit a surprising capacity for in-context learning (ICL), despite not being explicitly trained for it. Prior work has provided a deeper understanding of how ICL emerges in transformers, e.g., through the lens of mechanistic interpretability, Bayesian inference, or by examining the distributional properties of training data. However, in each of these cases, ICL is treated largely as a persistent phenomenon; namely, once ICL emerges, it is assumed to persist asymptotically. Here, we show that the emergence of ICL during transformer training is, in fact, often transient. We train transformers on synthetic data designed so that both ICL or in-weights learning (IWL) strategies can lead to correct predictions. We find that ICL first emerges, then disappears and gives way to IWL, all while the training loss decreases, indicating an asymptotic preference for IWL. The transient nature of ICL is observed in transformers across a range of model sizes and datasets, raising the question of how much to “overtrain” transformers when seeking compact, cheaper-to-run models. We find that L2 regularization may offer a path to more persistent ICL that removes the need for early stopping based on ICL-style validation tasks.},
	language = {en},
	urldate = {2023-11-11},
	author = {Singh, Aaditya K. and Chan, Stephanie C. Y. and Moskovitz, Ted and Grant, Erin and Saxe, Andrew M. and Hill, Felix},
	month = nov,
	year = {2023},
}

@misc{wang_glue_2019,
	title = {{GLUE}: {A} {Multi}-{Task} {Benchmark} and {Analysis} {Platform} for {Natural} {Language} {Understanding}},
	shorttitle = {{GLUE}},
	url = {http://arxiv.org/abs/1804.07461},
	doi = {10.48550/arXiv.1804.07461},
	abstract = {For natural language understanding (NLU) technology to be maximally useful, both practically and as a scientific object of study, it must be general: it must be able to process language in a way that is not exclusively tailored to any one specific task or dataset. In pursuit of this objective, we introduce the General Language Understanding Evaluation benchmark (GLUE), a tool for evaluating and analyzing the performance of models across a diverse range of existing NLU tasks. GLUE is model-agnostic, but it incentivizes sharing knowledge across tasks because certain tasks have very limited training data. We further provide a hand-crafted diagnostic test suite that enables detailed linguistic analysis of NLU models. We evaluate baselines based on current methods for multi-task and transfer learning and find that they do not immediately give substantial improvements over the aggregate performance of training a separate model per task, indicating room for improvement in developing general and robust NLU systems.},
	urldate = {2023-11-11},
	publisher = {arXiv},
	author = {Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
	month = feb,
	year = {2019},
	note = {arXiv:1804.07461 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{toneva_empirical_2019,
	title = {An {Empirical} {Study} of {Example} {Forgetting} during {Deep} {Neural} {Network} {Learning}},
	url = {http://arxiv.org/abs/1812.05159},
	doi = {10.48550/arXiv.1812.05159},
	abstract = {Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single classification tasks. Our goal is to understand whether a related phenomenon occurs when data does not undergo a clear distributional shift. We define a `forgetting event' to have occurred when an individual training example transitions from being classified correctly to incorrectly over the course of learning. Across several benchmark data sets, we find that: (i) certain examples are forgotten with high frequency, and some not at all; (ii) a data set's (un)forgettable examples generalize across neural architectures; and (iii) based on forgetting dynamics, a significant fraction of examples can be omitted from the training data set while still maintaining state-of-the-art generalization performance.},
	urldate = {2023-11-11},
	publisher = {arXiv},
	author = {Toneva, Mariya and Sordoni, Alessandro and Combes, Remi Tachet des and Trischler, Adam and Bengio, Yoshua and Gordon, Geoffrey J.},
	month = nov,
	year = {2019},
	note = {arXiv:1812.05159 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{xing_walk_2018,
	title = {A {Walk} with {SGD}},
	url = {http://arxiv.org/abs/1802.08770},
	doi = {10.48550/arXiv.1802.08770},
	abstract = {We present novel empirical observations regarding how stochastic gradient descent (SGD) navigates the loss landscape of over-parametrized deep neural networks (DNNs). These observations expose the qualitatively different roles of learning rate and batch-size in DNN optimization and generalization. Specifically we study the DNN loss surface along the trajectory of SGD by interpolating the loss surface between parameters from consecutive {\textbackslash}textit\{iterations\} and tracking various metrics during training. We find that the loss interpolation between parameters before and after each training iteration's update is roughly convex with a minimum ({\textbackslash}textit\{valley floor\}) in between for most of the training. Based on this and other metrics, we deduce that for most of the training update steps, SGD moves in valley like regions of the loss surface by jumping from one valley wall to another at a height above the valley floor. This 'bouncing between walls at a height' mechanism helps SGD traverse larger distance for small batch sizes and large learning rates which we find play qualitatively different roles in the dynamics. While a large learning rate maintains a large height from the valley floor, a small batch size injects noise facilitating exploration. We find this mechanism is crucial for generalization because the valley floor has barriers and this exploration above the valley floor allows SGD to quickly travel far away from the initialization point (without being affected by barriers) and find flatter regions, corresponding to better generalization.},
	urldate = {2023-11-11},
	publisher = {arXiv},
	author = {Xing, Chen and Arpit, Devansh and Tsirigotis, Christos and Bengio, Yoshua},
	month = may,
	year = {2018},
	note = {arXiv:1802.08770 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{sener_active_2018,
	title = {Active {Learning} for {Convolutional} {Neural} {Networks}: {A} {Core}-{Set} {Approach}},
	shorttitle = {Active {Learning} for {Convolutional} {Neural} {Networks}},
	url = {http://arxiv.org/abs/1708.00489},
	doi = {10.48550/arXiv.1708.00489},
	abstract = {Convolutional neural networks (CNNs) have been successfully applied to many recognition and learning tasks using a universal recipe; training a deep model on a very large dataset of supervised examples. However, this approach is rather restrictive in practice since collecting a large set of labeled images is very expensive. One way to ease this problem is coming up with smart ways for choosing images to be labelled from a very large collection (ie. active learning). Our empirical study suggests that many of the active learning heuristics in the literature are not effective when applied to CNNs in batch setting. Inspired by these limitations, we define the problem of active learning as core-set selection, ie. choosing set of points such that a model learned over the selected subset is competitive for the remaining data points. We further present a theoretical result characterizing the performance of any selected subset using the geometry of the datapoints. As an active learning algorithm, we choose the subset which is expected to yield best result according to our characterization. Our experiments show that the proposed method significantly outperforms existing approaches in image classification experiments by a large margin.},
	urldate = {2023-11-10},
	publisher = {arXiv},
	author = {Sener, Ozan and Savarese, Silvio},
	month = jun,
	year = {2018},
	note = {arXiv:1708.00489 [cs, stat]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning},
}

@inproceedings{joshi_multi-class_2009,
	title = {Multi-class active learning for image classification},
	url = {https://ieeexplore.ieee.org/abstract/document/5206627},
	doi = {10.1109/CVPR.2009.5206627},
	abstract = {One of the principal bottlenecks in applying learning techniques to classification problems is the large amount of labeled training data required. Especially for images and video, providing training data is very expensive in terms of human time and effort. In this paper we propose an active learning approach to tackle the problem. Instead of passively accepting random training examples, the active learning algorithm iteratively selects unlabeled examples for the user to label, so that human effort is focused on labeling the most “useful” examples. Our method relies on the idea of uncertainty sampling, in which the algorithm selects unlabeled examples that it finds hardest to classify. Specifically, we propose an uncertainty measure that generalizes margin-based uncertainty to the multi-class case and is easy to compute, so that active learning can handle a large number of classes and large data sizes efficiently. We demonstrate results for letter and digit recognition on datasets from the UCI repository, object recognition results on the Caltech-101 dataset, and scene categorization results on a dataset of 13 natural scene categories. The proposed method gives large reductions in the number of training examples required over random selection to achieve similar classification accuracy, with little computational overhead.},
	urldate = {2023-11-10},
	booktitle = {2009 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}},
	author = {Joshi, Ajay J. and Porikli, Fatih and Papanikolopoulos, Nikolaos},
	month = jun,
	year = {2009},
	note = {ISSN: 1063-6919},
	pages = {2372--2379},
}

@misc{bras_adversarial_2020,
	title = {Adversarial {Filters} of {Dataset} {Biases}},
	url = {http://arxiv.org/abs/2002.04108},
	doi = {10.48550/arXiv.2002.04108},
	abstract = {Large neural models have demonstrated human-level performance on language and vision benchmarks, while their performance degrades considerably on adversarial or out-of-distribution samples. This raises the question of whether these models have learned to solve a dataset rather than the underlying task by overfitting to spurious dataset biases. We investigate one recently proposed approach, AFLite, which adversarially filters such dataset biases, as a means to mitigate the prevalent overestimation of machine performance. We provide a theoretical understanding for AFLite, by situating it in the generalized framework for optimum bias reduction. We present extensive supporting evidence that AFLite is broadly applicable for reduction of measurable dataset biases, and that models trained on the filtered datasets yield better generalization to out-of-distribution tasks. Finally, filtering results in a large drop in model performance (e.g., from 92\% to 62\% for SNLI), while human performance still remains high. Our work thus shows that such filtered datasets can pose new research challenges for robust generalization by serving as upgraded benchmarks.},
	urldate = {2023-11-10},
	publisher = {arXiv},
	author = {Bras, Ronan Le and Swayamdipta, Swabha and Bhagavatula, Chandra and Zellers, Rowan and Peters, Matthew E. and Sabharwal, Ashish and Choi, Yejin},
	month = jul,
	year = {2020},
	note = {arXiv:2002.04108 [cs, stat]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{toneva_empirical_2019-1,
	title = {An {Empirical} {Study} of {Example} {Forgetting} during {Deep} {Neural} {Network} {Learning}},
	url = {http://arxiv.org/abs/1812.05159},
	doi = {10.48550/arXiv.1812.05159},
	abstract = {Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single classification tasks. Our goal is to understand whether a related phenomenon occurs when data does not undergo a clear distributional shift. We define a `forgetting event' to have occurred when an individual training example transitions from being classified correctly to incorrectly over the course of learning. Across several benchmark data sets, we find that: (i) certain examples are forgotten with high frequency, and some not at all; (ii) a data set's (un)forgettable examples generalize across neural architectures; and (iii) based on forgetting dynamics, a significant fraction of examples can be omitted from the training data set while still maintaining state-of-the-art generalization performance.},
	urldate = {2023-11-10},
	publisher = {arXiv},
	author = {Toneva, Mariya and Sordoni, Alessandro and Combes, Remi Tachet des and Trischler, Adam and Bengio, Yoshua and Gordon, Geoffrey J.},
	month = nov,
	year = {2019},
	note = {arXiv:1812.05159 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{cohen_data_2020,
	title = {Data {Maps}: {Datasets} {Can} {Be} {Distilled} {Too}},
	shorttitle = {Data {Maps}},
	url = {https://towardsdatascience.com/data-maps-datasets-can-be-distilled-too-1991c3c260d6},
	abstract = {TL;DR: This post is about the paper Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics, alongside implementation…},
	language = {en},
	urldate = {2023-11-09},
	journal = {Medium},
	author = {Cohen, Elior},
	month = oct,
	year = {2020},
}

@misc{huang_audiogpt_2023,
	title = {{AudioGPT}: {Understanding} and {Generating} {Speech}, {Music}, {Sound}, and {Talking} {Head}},
	shorttitle = {{AudioGPT}},
	url = {http://arxiv.org/abs/2304.12995},
	doi = {10.48550/arXiv.2304.12995},
	abstract = {Large language models (LLMs) have exhibited remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. Despite the recent success, current LLMs are not capable of processing complex audio information or conducting spoken conversations (like Siri or Alexa). In this work, we propose a multi-modal AI system named AudioGPT, which complements LLMs (i.e., ChatGPT) with 1) foundation models to process complex audio information and solve numerous understanding and generation tasks; and 2) the input/output interface (ASR, TTS) to support spoken dialogue. With an increasing demand to evaluate multi-modal LLMs of human intention understanding and cooperation with foundation models, we outline the principles and processes and test AudioGPT in terms of consistency, capability, and robustness. Experimental results demonstrate the capabilities of AudioGPT in solving AI tasks with speech, music, sound, and talking head understanding and generation in multi-round dialogues, which empower humans to create rich and diverse audio content with unprecedented ease. Our system is publicly available at {\textbackslash}url\{https://github.com/AIGC-Audio/AudioGPT\}.},
	urldate = {2023-11-08},
	publisher = {arXiv},
	author = {Huang, Rongjie and Li, Mingze and Yang, Dongchao and Shi, Jiatong and Chang, Xuankai and Ye, Zhenhui and Wu, Yuning and Hong, Zhiqing and Huang, Jiawei and Liu, Jinglin and Ren, Yi and Zhao, Zhou and Watanabe, Shinji},
	month = apr,
	year = {2023},
	note = {arXiv:2304.12995 [cs, eess]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{huang_noise2music_2023,
	title = {{Noise2Music}: {Text}-conditioned {Music} {Generation} with {Diffusion} {Models}},
	shorttitle = {{Noise2Music}},
	url = {http://arxiv.org/abs/2302.03917},
	doi = {10.48550/arXiv.2302.03917},
	abstract = {We introduce Noise2Music, where a series of diffusion models is trained to generate high-quality 30-second music clips from text prompts. Two types of diffusion models, a generator model, which generates an intermediate representation conditioned on text, and a cascader model, which generates high-fidelity audio conditioned on the intermediate representation and possibly the text, are trained and utilized in succession to generate high-fidelity music. We explore two options for the intermediate representation, one using a spectrogram and the other using audio with lower fidelity. We find that the generated audio is not only able to faithfully reflect key elements of the text prompt such as genre, tempo, instruments, mood, and era, but goes beyond to ground fine-grained semantics of the prompt. Pretrained large language models play a key role in this story -- they are used to generate paired text for the audio of the training set and to extract embeddings of the text prompts ingested by the diffusion models. Generated examples: https://google-research.github.io/noise2music},
	urldate = {2023-11-08},
	publisher = {arXiv},
	author = {Huang, Qingqing and Park, Daniel S. and Wang, Tao and Denk, Timo I. and Ly, Andy and Chen, Nanxin and Zhang, Zhengdong and Zhang, Zhishuai and Yu, Jiahui and Frank, Christian and Engel, Jesse and Le, Quoc V. and Chan, William and Chen, Zhifeng and Han, Wei},
	month = mar,
	year = {2023},
	note = {arXiv:2302.03917 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{agostinelli_musiclm_2023,
	title = {{MusicLM}: {Generating} {Music} {From} {Text}},
	shorttitle = {{MusicLM}},
	url = {http://arxiv.org/abs/2301.11325},
	doi = {10.48550/arXiv.2301.11325},
	abstract = {We introduce MusicLM, a model generating high-fidelity music from text descriptions such as "a calming violin melody backed by a distorted guitar riff". MusicLM casts the process of conditional music generation as a hierarchical sequence-to-sequence modeling task, and it generates music at 24 kHz that remains consistent over several minutes. Our experiments show that MusicLM outperforms previous systems both in audio quality and adherence to the text description. Moreover, we demonstrate that MusicLM can be conditioned on both text and a melody in that it can transform whistled and hummed melodies according to the style described in a text caption. To support future research, we publicly release MusicCaps, a dataset composed of 5.5k music-text pairs, with rich text descriptions provided by human experts.},
	urldate = {2023-11-08},
	publisher = {arXiv},
	author = {Agostinelli, Andrea and Denk, Timo I. and Borsos, Zalán and Engel, Jesse and Verzetti, Mauro and Caillon, Antoine and Huang, Qingqing and Jansen, Aren and Roberts, Adam and Tagliasacchi, Marco and Sharifi, Matt and Zeghidour, Neil and Frank, Christian},
	month = jan,
	year = {2023},
	note = {arXiv:2301.11325 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{noauthor_papers_nodate,
	title = {Papers with {Code} - {JEN}-1: {Text}-{Guided} {Universal} {Music} {Generation} with {Omnidirectional} {Diffusion} {Models}},
	shorttitle = {Papers with {Code} - {JEN}-1},
	url = {https://paperswithcode.com/paper/jen-1-text-guided-universal-music-generation},
	abstract = {�� SOTA for Text-to-Music Generation on MusicCaps (FAD VGG metric)},
	language = {en},
	urldate = {2023-11-08},
}

@misc{kreuk_audiogen_2023,
	title = {{AudioGen}: {Textually} {Guided} {Audio} {Generation}},
	shorttitle = {{AudioGen}},
	url = {http://arxiv.org/abs/2209.15352},
	doi = {10.48550/arXiv.2209.15352},
	abstract = {We tackle the problem of generating audio samples conditioned on descriptive text captions. In this work, we propose AaudioGen, an auto-regressive generative model that generates audio samples conditioned on text inputs. AudioGen operates on a learnt discrete audio representation. The task of text-to-audio generation poses multiple challenges. Due to the way audio travels through a medium, differentiating ``objects'' can be a difficult task (e.g., separating multiple people simultaneously speaking). This is further complicated by real-world recording conditions (e.g., background noise, reverberation, etc.). Scarce text annotations impose another constraint, limiting the ability to scale models. Finally, modeling high-fidelity audio requires encoding audio at high sampling rate, leading to extremely long sequences. To alleviate the aforementioned challenges we propose an augmentation technique that mixes different audio samples, driving the model to internally learn to separate multiple sources. We curated 10 datasets containing different types of audio and text annotations to handle the scarcity of text-audio data points. For faster inference, we explore the use of multi-stream modeling, allowing the use of shorter sequences while maintaining a similar bitrate and perceptual quality. We apply classifier-free guidance to improve adherence to text. Comparing to the evaluated baselines, AudioGen outperforms over both objective and subjective metrics. Finally, we explore the ability of the proposed method to generate audio continuation conditionally and unconditionally. Samples: https://felixkreuk.github.io/audiogen},
	urldate = {2023-11-08},
	publisher = {arXiv},
	author = {Kreuk, Felix and Synnaeve, Gabriel and Polyak, Adam and Singer, Uriel and Défossez, Alexandre and Copet, Jade and Parikh, Devi and Taigman, Yaniv and Adi, Yossi},
	month = mar,
	year = {2023},
	note = {arXiv:2209.15352 [cs, eess]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{kreuk_audiogen_2023-1,
	title = {{AudioGen}: {Textually} {Guided} {Audio} {Generation}},
	shorttitle = {{AudioGen}},
	url = {http://arxiv.org/abs/2209.15352},
	doi = {10.48550/arXiv.2209.15352},
	abstract = {We tackle the problem of generating audio samples conditioned on descriptive text captions. In this work, we propose AaudioGen, an auto-regressive generative model that generates audio samples conditioned on text inputs. AudioGen operates on a learnt discrete audio representation. The task of text-to-audio generation poses multiple challenges. Due to the way audio travels through a medium, differentiating ``objects'' can be a difficult task (e.g., separating multiple people simultaneously speaking). This is further complicated by real-world recording conditions (e.g., background noise, reverberation, etc.). Scarce text annotations impose another constraint, limiting the ability to scale models. Finally, modeling high-fidelity audio requires encoding audio at high sampling rate, leading to extremely long sequences. To alleviate the aforementioned challenges we propose an augmentation technique that mixes different audio samples, driving the model to internally learn to separate multiple sources. We curated 10 datasets containing different types of audio and text annotations to handle the scarcity of text-audio data points. For faster inference, we explore the use of multi-stream modeling, allowing the use of shorter sequences while maintaining a similar bitrate and perceptual quality. We apply classifier-free guidance to improve adherence to text. Comparing to the evaluated baselines, AudioGen outperforms over both objective and subjective metrics. Finally, we explore the ability of the proposed method to generate audio continuation conditionally and unconditionally. Samples: https://felixkreuk.github.io/audiogen},
	urldate = {2023-11-08},
	publisher = {arXiv},
	author = {Kreuk, Felix and Synnaeve, Gabriel and Polyak, Adam and Singer, Uriel and Défossez, Alexandre and Copet, Jade and Parikh, Devi and Taigman, Yaniv and Adi, Yossi},
	month = mar,
	year = {2023},
	note = {arXiv:2209.15352 [cs, eess]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{noauthor_papers_nodate-1,
	title = {Papers with {Code} - {Music} {Understanding} {LLaMA}: {Advancing} {Text}-to-{Music} {Generation} with {Question} {Answering} and {Captioning}},
	shorttitle = {Papers with {Code} - {Music} {Understanding} {LLaMA}},
	url = {https://paperswithcode.com/paper/music-understanding-llama-advancing-text-to},
	abstract = {�� SOTA for Music Question Answering on MusicQA Dataset (BLEU metric)},
	language = {en},
	urldate = {2023-11-08},
}

@article{daly_neural_2020,
	title = {Neural and physiological data from participants listening to affective music},
	volume = {7},
	copyright = {2020 The Author(s)},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/s41597-020-0507-6},
	doi = {10.1038/s41597-020-0507-6},
	abstract = {Music provides a means of communicating affective meaning. However, the neurological mechanisms by which music induces affect are not fully understood. Our project sought to investigate this through a series of experiments into how humans react to affective musical stimuli and how physiological and neurological signals recorded from those participants change in accordance with self-reported changes in affect. In this paper, the datasets recorded over the course of this project are presented, including details of the musical stimuli, participant reports of their felt changes in affective states as they listened to the music, and concomitant recordings of physiological and neurological activity. We also include non-identifying meta data on our participant populations for purposes of further exploratory analysis. This data provides a large and valuable novel resource for researchers investigating emotion, music, and how they affect our neural and physiological activity.},
	language = {en},
	number = {1},
	urldate = {2023-11-08},
	journal = {Scientific Data},
	author = {Daly, Ian and Nicolaou, Nicoletta and Williams, Duncan and Hwang, Faustina and Kirke, Alexis and Miranda, Eduardo and Nasuto, Slawomir J.},
	month = jun,
	year = {2020},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Cognitive neuroscience, Emotion},
	pages = {177},
}

@misc{zhang_shufflenet_2017,
	title = {{ShuffleNet}: {An} {Extremely} {Efficient} {Convolutional} {Neural} {Network} for {Mobile} {Devices}},
	shorttitle = {{ShuffleNet}},
	url = {http://arxiv.org/abs/1707.01083},
	doi = {10.48550/arXiv.1707.01083},
	abstract = {We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8\%) than recent MobileNet on ImageNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves {\textasciitilde}13x actual speedup over AlexNet while maintaining comparable accuracy.},
	urldate = {2023-11-08},
	publisher = {arXiv},
	author = {Zhang, Xiangyu and Zhou, Xinyu and Lin, Mengxiao and Sun, Jian},
	month = dec,
	year = {2017},
	note = {arXiv:1707.01083 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{huang_audiogpt_2023-1,
	title = {{AudioGPT}: {Understanding} and {Generating} {Speech}, {Music}, {Sound}, and {Talking} {Head}},
	shorttitle = {{AudioGPT}},
	url = {http://arxiv.org/abs/2304.12995},
	doi = {10.48550/arXiv.2304.12995},
	abstract = {Large language models (LLMs) have exhibited remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. Despite the recent success, current LLMs are not capable of processing complex audio information or conducting spoken conversations (like Siri or Alexa). In this work, we propose a multi-modal AI system named AudioGPT, which complements LLMs (i.e., ChatGPT) with 1) foundation models to process complex audio information and solve numerous understanding and generation tasks; and 2) the input/output interface (ASR, TTS) to support spoken dialogue. With an increasing demand to evaluate multi-modal LLMs of human intention understanding and cooperation with foundation models, we outline the principles and processes and test AudioGPT in terms of consistency, capability, and robustness. Experimental results demonstrate the capabilities of AudioGPT in solving AI tasks with speech, music, sound, and talking head understanding and generation in multi-round dialogues, which empower humans to create rich and diverse audio content with unprecedented ease. Our system is publicly available at {\textbackslash}url\{https://github.com/AIGC-Audio/AudioGPT\}.},
	urldate = {2023-11-08},
	publisher = {arXiv},
	author = {Huang, Rongjie and Li, Mingze and Yang, Dongchao and Shi, Jiatong and Chang, Xuankai and Ye, Zhenhui and Wu, Yuning and Hong, Zhiqing and Huang, Jiawei and Liu, Jinglin and Ren, Yi and Zhao, Zhou and Watanabe, Shinji},
	month = apr,
	year = {2023},
	note = {arXiv:2304.12995 [cs, eess]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{engineering_llark_2023,
	title = {{LLark}: {A} {Multimodal} {Foundation} {Model} for {Music}},
	shorttitle = {{LLark}},
	url = {https://research.atspotify.com/2023/10/llark-a-multimodal-foundation-model-for-music/},
	abstract = {Every day, music is enjoyed, created, and discovered by billions of people around the globe – and yet, existing AI systems largely struggle to model the nuances that make music different from other forms of audio.},
	language = {en-US},
	urldate = {2023-11-08},
	journal = {Spotify Research},
	author = {Engineering, Spotify},
	month = oct,
	year = {2023},
}

@misc{noauthor_musicgen_nodate,
	title = {{MusicGen}: {Simple} and {Controllable} {Music} {Generation}},
	url = {https://ai.honu.io/papers/musicgen/},
	urldate = {2023-11-07},
}

@misc{noauthor_papers_nodate-2,
	title = {Papers with {Code} - {Machine} {Learning} {Datasets}},
	url = {https://paperswithcode.com/datasets?o=newest&task=music-generation&mod=audio},
	abstract = {14 datasets • 110697 papers with code.},
	language = {en},
	urldate = {2023-11-07},
}

@misc{zhang_vis2mus_2022,
	title = {{Vis2Mus}: {Exploring} {Multimodal} {Representation} {Mapping} for {Controllable} {Music} {Generation}},
	shorttitle = {{Vis2Mus}},
	url = {http://arxiv.org/abs/2211.05543},
	doi = {10.48550/arXiv.2211.05543},
	abstract = {In this study, we explore the representation mapping from the domain of visual arts to the domain of music, with which we can use visual arts as an effective handle to control music generation. Unlike most studies in multimodal representation learning that are purely data-driven, we adopt an analysis-by-synthesis approach that combines deep music representation learning with user studies. Such an approach enables us to discover {\textbackslash}textit\{interpretable\} representation mapping without a huge amount of paired data. In particular, we discover that visual-to-music mapping has a nice property similar to equivariant. In other words, we can use various image transformations, say, changing brightness, changing contrast, style transfer, to control the corresponding transformations in the music domain. In addition, we released the Vis2Mus system as a controllable interface for symbolic music generation.},
	urldate = {2023-11-07},
	publisher = {arXiv},
	author = {Zhang, Runbang and Zhang, Yixiao and Shao, Kai and Shan, Ying and Xia, Gus},
	month = nov,
	year = {2022},
	note = {arXiv:2211.05543 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{raina_minimum_2023,
	title = {Minimum {Bayes}' {Risk} {Decoding} for {System} {Combination} of {Grammatical} {Error} {Correction} {Systems}},
	url = {http://arxiv.org/abs/2309.06520},
	doi = {10.48550/arXiv.2309.06520},
	abstract = {For sequence-to-sequence tasks it is challenging to combine individual system outputs. Further, there is also often a mismatch between the decoding criterion and the one used for assessment. Minimum Bayes' Risk (MBR) decoding can be used to combine system outputs in a manner that encourages better alignment with the final assessment criterion. This paper examines MBR decoding for Grammatical Error Correction (GEC) systems, where performance is usually evaluated in terms of edits and an associated F-score. Hence, we propose a novel MBR loss function directly linked to this form of criterion. Furthermore, an approach to expand the possible set of candidate sentences is described. This builds on a current max-voting combination scheme, as well as individual edit-level selection. Experiments on three popular GEC datasets and with state-of-the-art GEC systems demonstrate the efficacy of the proposed MBR approach. Additionally, the paper highlights how varying reward metrics within the MBR decoding framework can provide control over precision, recall, and the F-score in combined GEC systems.},
	urldate = {2023-11-07},
	publisher = {arXiv},
	author = {Raina, Vyas and Gales, Mark},
	month = oct,
	year = {2023},
	note = {arXiv:2309.06520 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

@inproceedings{lee_pix2struct_2023,
	title = {{Pix2Struct}: {Screenshot} {Parsing} as {Pretraining} for {Visual} {Language} {Understanding}},
	shorttitle = {{Pix2Struct}},
	url = {https://proceedings.mlr.press/v202/lee23g.html},
	abstract = {Visually-situated language is ubiquitous—sources range from textbooks with diagrams to web pages with images and tables, to mobile apps with buttons and forms. Perhaps due to this diversity, previous work has typically relied on domain-specific recipes with limited sharing of the underlying data, model architectures, and objectives. We present Pix2Struct, a pretrained image-to-text model for purely visual language understanding, which can be finetuned on tasks containing visually-situated language. Pix2Struct is pretrained by learning to parse masked screenshots of web pages into simplified HTML. The web, with its richness of visual elements cleanly reflected in the HTML structure, provides a large source of pretraining data well suited to the diversity of downstream tasks. Intuitively, this objective subsumes common pretraining signals such as OCR, language modeling, and image captioning. In addition to the novel pretraining strategy, we introduce a variable-resolution input representation and a more flexible integration of language and vision inputs, where language prompts such as questions are rendered directly on top of the input image. For the first time, we show that a single pretrained model can achieve state-of-the-art results in six out of nine tasks across four domains: documents, illustrations, user interfaces, and natural images.},
	language = {en},
	urldate = {2023-11-07},
	booktitle = {Proceedings of the 40th {International} {Conference} on {Machine} {Learning}},
	publisher = {PMLR},
	author = {Lee, Kenton and Joshi, Mandar and Turc, Iulia Raluca and Hu, Hexiang and Liu, Fangyu and Eisenschlos, Julian Martin and Khandelwal, Urvashi and Shaw, Peter and Chang, Ming-Wei and Toutanova, Kristina},
	month = jul,
	year = {2023},
	note = {ISSN: 2640-3498},
	pages = {18893--18912},
}

@misc{noauthor_musiclm_nodate,
	title = {{MusicLM}},
	url = {https://google-research.github.io/seanet/musiclm/examples/},
	urldate = {2023-11-07},
}

@misc{mittal_symbolic_2021,
	title = {Symbolic {Music} {Generation} with {Diffusion} {Models}},
	url = {http://arxiv.org/abs/2103.16091},
	doi = {10.48550/arXiv.2103.16091},
	abstract = {Score-based generative models and diffusion probabilistic models have been successful at generating high-quality samples in continuous domains such as images and audio. However, due to their Langevin-inspired sampling mechanisms, their application to discrete and sequential data has been limited. In this work, we present a technique for training diffusion models on sequential data by parameterizing the discrete domain in the continuous latent space of a pre-trained variational autoencoder. Our method is non-autoregressive and learns to generate sequences of latent embeddings through the reverse process and offers parallel generation with a constant number of iterative refinement steps. We apply this technique to modeling symbolic music and show strong unconditional generation and post-hoc conditional infilling results compared to autoregressive language models operating over the same continuous embeddings.},
	urldate = {2023-11-07},
	publisher = {arXiv},
	author = {Mittal, Gautam and Engel, Jesse and Hawthorne, Curtis and Simon, Ian},
	month = nov,
	year = {2021},
	note = {arXiv:2103.16091 [cs, eess, stat]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning},
}

@misc{gong_diffuseq_2023,
	title = {{DiffuSeq}: {Sequence} to {Sequence} {Text} {Generation} with {Diffusion} {Models}},
	shorttitle = {{DiffuSeq}},
	url = {http://arxiv.org/abs/2210.08933},
	doi = {10.48550/arXiv.2210.08933},
	abstract = {Recently, diffusion models have emerged as a new paradigm for generative models. Despite the success in domains using continuous signals such as vision and audio, adapting diffusion models to natural language is under-explored due to the discrete nature of texts, especially for conditional generation. We tackle this challenge by proposing DiffuSeq: a diffusion model designed for sequence-to-sequence (Seq2Seq) text generation tasks. Upon extensive evaluation over a wide range of Seq2Seq tasks, we find DiffuSeq achieving comparable or even better performance than six established baselines, including a state-of-the-art model that is based on pre-trained language models. Apart from quality, an intriguing property of DiffuSeq is its high diversity during generation, which is desired in many Seq2Seq tasks. We further include a theoretical analysis revealing the connection between DiffuSeq and autoregressive/non-autoregressive models. Bringing together theoretical analysis and empirical evidence, we demonstrate the great potential of diffusion models in complex conditional language generation tasks. Code is available at {\textbackslash}url\{https://github.com/Shark-NLP/DiffuSeq\}},
	urldate = {2023-11-07},
	publisher = {arXiv},
	author = {Gong, Shansan and Li, Mukai and Feng, Jiangtao and Wu, Zhiyong and Kong, Lingpeng},
	month = feb,
	year = {2023},
	note = {arXiv:2210.08933 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{garcia-ferrero_this_2023,
	title = {This is not a {Dataset}: {A} {Large} {Negation} {Benchmark} to {Challenge} {Large} {Language} {Models}},
	shorttitle = {This is not a {Dataset}},
	url = {http://arxiv.org/abs/2310.15941},
	doi = {10.48550/arXiv.2310.15941},
	abstract = {Although large language models (LLMs) have apparently acquired a certain level of grammatical knowledge and the ability to make generalizations, they fail to interpret negation, a crucial step in Natural Language Processing. We try to clarify the reasons for the sub-optimal performance of LLMs understanding negation. We introduce a large semi-automatically generated dataset of circa 400,000 descriptive sentences about commonsense knowledge that can be true or false in which negation is present in about 2/3 of the corpus in different forms. We have used our dataset with the largest available open LLMs in a zero-shot approach to grasp their generalization and inference capability and we have also fine-tuned some of the models to assess whether the understanding of negation can be trained. Our findings show that, while LLMs are proficient at classifying affirmative sentences, they struggle with negative sentences and lack a deep understanding of negation, often relying on superficial cues. Although fine-tuning the models on negative sentences improves their performance, the lack of generalization in handling negation is persistent, highlighting the ongoing challenges of LLMs regarding negation understanding and generalization. The dataset and code are publicly available.},
	urldate = {2023-11-07},
	publisher = {arXiv},
	author = {García-Ferrero, Iker and Altuna, Begoña and Álvez, Javier and Gonzalez-Dios, Itziar and Rigau, German},
	month = oct,
	year = {2023},
	note = {arXiv:2310.15941 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{yu_seqgan_2017,
	title = {{SeqGAN}: {Sequence} {Generative} {Adversarial} {Nets} with {Policy} {Gradient}},
	shorttitle = {{SeqGAN}},
	url = {http://arxiv.org/abs/1609.05473},
	doi = {10.48550/arXiv.1609.05473},
	abstract = {As a new way of training generative models, Generative Adversarial Nets (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data. However, it has limitations when the goal is for generating sequences of discrete tokens. A major reason lies in that the discrete outputs from the generative model make it difficult to pass the gradient update from the discriminative model to the generative model. Also, the discriminative model can only assess a complete sequence, while for a partially generated sequence, it is non-trivial to balance its current score and the future one once the entire sequence has been generated. In this paper, we propose a sequence generation framework, called SeqGAN, to solve the problems. Modeling the data generator as a stochastic policy in reinforcement learning (RL), SeqGAN bypasses the generator differentiation problem by directly performing gradient policy update. The RL reward signal comes from the GAN discriminator judged on a complete sequence, and is passed back to the intermediate state-action steps using Monte Carlo search. Extensive experiments on synthetic data and real-world tasks demonstrate significant improvements over strong baselines.},
	urldate = {2023-11-06},
	publisher = {arXiv},
	author = {Yu, Lantao and Zhang, Weinan and Wang, Jun and Yu, Yong},
	month = aug,
	year = {2017},
	note = {arXiv:1609.05473 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
}

@misc{guimaraes_objective-reinforced_2018,
	title = {Objective-{Reinforced} {Generative} {Adversarial} {Networks} ({ORGAN}) for {Sequence} {Generation} {Models}},
	url = {http://arxiv.org/abs/1705.10843},
	doi = {10.48550/arXiv.1705.10843},
	abstract = {In unsupervised data generation tasks, besides the generation of a sample based on previous observations, one would often like to give hints to the model in order to bias the generation towards desirable metrics. We propose a method that combines Generative Adversarial Networks (GANs) and reinforcement learning (RL) in order to accomplish exactly that. While RL biases the data generation process towards arbitrary metrics, the GAN component of the reward function ensures that the model still remembers information learned from data. We build upon previous results that incorporated GANs and RL in order to generate sequence data and test this model in several settings for the generation of molecules encoded as text sequences (SMILES) and in the context of music generation, showing for each case that we can effectively bias the generation process towards desired metrics.},
	urldate = {2023-11-06},
	publisher = {arXiv},
	author = {Guimaraes, Gabriel Lima and Sanchez-Lengeling, Benjamin and Outeiral, Carlos and Farias, Pedro Luis Cunha and Aspuru-Guzik, Alán},
	month = feb,
	year = {2018},
	note = {arXiv:1705.10843 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@incollection{hernandez-olivan_music_2023,
	address = {Cham},
	series = {Signals and {Communication} {Technology}},
	title = {Music {Composition} with {Deep} {Learning}: {A} {Review}},
	isbn = {978-3-031-18444-4},
	shorttitle = {Music {Composition} with {Deep} {Learning}},
	url = {https://doi.org/10.1007/978-3-031-18444-4_2},
	abstract = {Generating a complex work of art such as a musical composition requires exhibiting a certain level of creativity. This depends on a variety of factors that are related to the hierarchy of musical language. Music generation has faced challenges by using algorithmic methods and recently is approaching them with deep learning models that are being used in other fields such as computer vision. In this chapter, we place into context the existing relationships between AI-based music composition models and human musical composition and creativity processes. First, we describe the music composition process, and then we give an overview of the recent deep learning models for music generation classifying them according to their relationship with some of the music basic principles: melody, harmony, structure, or music composition processes—instrumentation and orchestration. The relevance of classifying music generation models in those categories helps us to measure and understand how deep learning models deal with the complexity and hierarchy of music. We try to answer some of the most relevant open questions for this task by analyzing the ability of current deep learning models to generate music with creativity or the similarity between AI and human composition processes, among others.},
	language = {en},
	urldate = {2023-11-06},
	booktitle = {Advances in {Speech} and {Music} {Technology}: {Computational} {Aspects} and {Applications}},
	publisher = {Springer International Publishing},
	author = {Hernandez-Olivan, Carlos and Beltrán, José R.},
	editor = {Biswas, Anupam and Wennekes, Emile and Wieczorkowska, Alicja and Laskar, Rabul Hussain},
	year = {2023},
	doi = {10.1007/978-3-031-18444-4_2},
	keywords = {AI-based music, AI-generated music, Deep learning, Machine learning, Music generation, Neural networks},
	pages = {25--50},
}

@misc{noauthor_1_nodate,
	title = {(1) {Sneak} {Peak}: {Music} {Generation} using {Human}-{In}-{The}-{Loop} {Reinforcement} {Learning} {\textbar} {LinkedIn}},
	url = {https://www.linkedin.com/pulse/sneak-peak-music-generation-using-human-in-the-loop-aju-ani-justus/?utm_source=rss&utm_campaign=articles_sitemaps&utm_medium=google_news},
	urldate = {2023-11-06},
}

@article{majumder_music_nodate,
	title = {Music {Recombination} using a {Genetic} {Algorithm}},
	abstract = {This paper presents a new system, based on genetic algorithms, to compose music pieces automatically based on analysis of the exemplar MIDI ﬁles. The aim of this project is to create a new music piece which is based on the information in the source pieces. This system extracts musical features from two MIDI ﬁles and automatically generates a new music piece using a genetic algorithm. The user speciﬁes the length of the piece to create, and the weighting of musical features from each of the MIDI ﬁles to guide the generation. This system will provide the composer a new music piece based on two selected music pieces.},
	language = {en},
	author = {Majumder, Sanjay and Smith, Benjamin D},
}

@article{bellier_music_2023,
	title = {Music can be reconstructed from human auditory cortex activity using nonlinear decoding models},
	volume = {21},
	issn = {1545-7885},
	url = {https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002176},
	doi = {10.1371/journal.pbio.3002176},
	abstract = {Music is core to human experience, yet the precise neural dynamics underlying music perception remain unknown. We analyzed a unique intracranial electroencephalography (iEEG) dataset of 29 patients who listened to a Pink Floyd song and applied a stimulus reconstruction approach previously used in the speech domain. We successfully reconstructed a recognizable song from direct neural recordings and quantified the impact of different factors on decoding accuracy. Combining encoding and decoding analyses, we found a right-hemisphere dominance for music perception with a primary role of the superior temporal gyrus (STG), evidenced a new STG subregion tuned to musical rhythm, and defined an anterior–posterior STG organization exhibiting sustained and onset responses to musical elements. Our findings show the feasibility of applying predictive modeling on short datasets acquired in single patients, paving the way for adding musical elements to brain–computer interface (BCI) applications.},
	language = {en},
	number = {8},
	urldate = {2023-11-06},
	journal = {PLOS Biology},
	author = {Bellier, Ludovic and Llorens, Anaïs and Marciano, Déborah and Gunduz, Aysegul and Schalk, Gerwin and Brunner, Peter and Knight, Robert T.},
	month = aug,
	year = {2023},
	note = {Publisher: Public Library of Science},
	keywords = {Acoustics, Bioacoustics, Left hemisphere, Music cognition, Music perception, Right hemisphere, Sensory perception, Speech},
	pages = {e3002176},
}

@misc{dash_ai-based_2023,
	title = {{AI}-{Based} {Affective} {Music} {Generation} {Systems}: {A} {Review} of {Methods}, and {Challenges}},
	shorttitle = {{AI}-{Based} {Affective} {Music} {Generation} {Systems}},
	url = {http://arxiv.org/abs/2301.06890},
	doi = {10.48550/arXiv.2301.06890},
	abstract = {Music is a powerful medium for altering the emotional state of the listener. In recent years, with significant advancement in computing capabilities, artificial intelligence-based (AI-based) approaches have become popular for creating affective music generation (AMG) systems that are empowered with the ability to generate affective music. Entertainment, healthcare, and sensor-integrated interactive system design are a few of the areas in which AI-based affective music generation (AI-AMG) systems may have a significant impact. Given the surge of interest in this topic, this article aims to provide a comprehensive review of AI-AMG systems. The main building blocks of an AI-AMG system are discussed, and existing systems are formally categorized based on the core algorithm used for music generation. In addition, this article discusses the main musical features employed to compose affective music, along with the respective AI-based approaches used for tailoring them. Lastly, the main challenges and open questions in this field, as well as their potential solutions, are presented to guide future research. We hope that this review will be useful for readers seeking to understand the state-of-the-art in AI-AMG systems, and gain an overview of the methods used for developing them, thereby helping them explore this field in the future.},
	urldate = {2023-11-06},
	publisher = {arXiv},
	author = {Dash, Adyasha and Agres, Kat R.},
	month = jan,
	year = {2023},
	note = {arXiv:2301.06890 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction},
}

@misc{paulus_deep_2017,
	title = {A {Deep} {Reinforced} {Model} for {Abstractive} {Summarization}},
	url = {http://arxiv.org/abs/1705.04304},
	doi = {10.48550/arXiv.1705.04304},
	abstract = {Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences. For longer documents and summaries however these models often include repetitive and incoherent phrases. We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL). Models trained only with supervised learning often exhibit "exposure bias" - they assume ground truth is provided at each step during training. However, when standard word prediction is combined with the global sequence prediction training of RL the resulting summaries become more readable. We evaluate this model on the CNN/Daily Mail and New York Times datasets. Our model obtains a 41.16 ROUGE-1 score on the CNN/Daily Mail dataset, an improvement over previous state-of-the-art models. Human evaluation also shows that our model produces higher quality summaries.},
	urldate = {2023-11-05},
	publisher = {arXiv},
	author = {Paulus, Romain and Xiong, Caiming and Socher, Richard},
	month = nov,
	year = {2017},
	note = {arXiv:1705.04304 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@inproceedings{vlachos_imitation_2017,
	address = {Valencia, Spain},
	title = {Imitation learning for structured prediction in natural language processing},
	url = {https://aclanthology.org/E17-5003},
	abstract = {Imitation learning is a learning paradigm originally developed to learn robotic controllers from demonstrations by humans, e.g. autonomous flight from pilot demonstrations. Recently, algorithms for structured prediction were proposed under this paradigm and have been applied successfully to a number of tasks including syntactic dependency parsing, information extraction, coreference resolution, dynamic feature selection, semantic parsing and natural language generation. Key advantages are the ability to handle large output search spaces and to learn with non-decomposable loss functions. Our aim in this tutorial is to have a unified presentation of the various imitation algorithms for structure prediction, and show how they can be applied to a variety of NLP tasks.All material associated with the tutorial will be made available through https://sheffieldnlp.github.io/ImitationLearningTutorialEACL2017/.},
	urldate = {2023-11-05},
	booktitle = {Proceedings of the 15th {Conference} of the {European} {Chapter} of the {Association} for {Computational} {Linguistics}: {Tutorial} {Abstracts}},
	publisher = {Association for Computational Linguistics},
	author = {Vlachos, Andreas and Lampouras, Gerasimos and Riedel, Sebastian},
	editor = {Klementiev, Alexandre and Specia, Lucia},
	month = apr,
	year = {2017},
}

@inproceedings{vardy_algorithmic_1997,
	address = {El Paso, Texas, United States},
	title = {Algorithmic complexity in coding theory and the minimum distance problem},
	isbn = {978-0-89791-888-6},
	url = {http://portal.acm.org/citation.cfm?doid=258533.258559},
	doi = {10.1145/258533.258559},
	abstract = {We start withan overviewof algorithmiccomplexity problems in coding theory We then show that the problem of computing the minimumdiktanceof a binaryIinwr code is NP-hard,and the correspondingdeci{\textasciitilde}”onproblemis W-complete. This constitutes a proof of the conjecture Bedekamp, McEliece,vanTilborg, dating back to 1978. Extensionsand applicationsof this result to other problemsin codingtheqv are discussed.},
	language = {en},
	urldate = {2023-11-05},
	booktitle = {Proceedings of the twenty-ninth annual {ACM} symposium on {Theory} of computing  - {STOC} '97},
	publisher = {ACM Press},
	author = {Vardy, Alexander},
	year = {1997},
	pages = {92--109},
}

@misc{noauthor_rw_nodate,
	title = {R.{W}. {Hamming} - {Error} {Detecting} and {Error} {Correcting} {Codes} (1/15)},
	url = {https://guest.engelschall.com/~sb/hamming/},
	urldate = {2023-11-05},
}

@article{guruswami_notes_nodate,
	title = {Notes 1: {Introduction}, linear codes},
	language = {en},
	author = {Guruswami, Venkatesan},
}

@inproceedings{koehn_six_2017,
	address = {Vancouver},
	title = {Six {Challenges} for {Neural} {Machine} {Translation}},
	url = {https://aclanthology.org/W17-3204},
	doi = {10.18653/v1/W17-3204},
	abstract = {We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. We show both deficiencies and improvements over the quality of phrase-based statistical machine translation.},
	urldate = {2023-11-04},
	booktitle = {Proceedings of the {First} {Workshop} on {Neural} {Machine} {Translation}},
	publisher = {Association for Computational Linguistics},
	author = {Koehn, Philipp and Knowles, Rebecca},
	editor = {Luong, Thang and Birch, Alexandra and Neubig, Graham and Finch, Andrew},
	month = aug,
	year = {2017},
	pages = {28--39},
}

@article{das_quantifying_nodate,
	title = {Quantifying the impact of {Language} {Similarity} on {Machine} {Translation}},
	language = {en},
	author = {Das, Anish},
}

@misc{goldberg_word2vec_2014,
	title = {word2vec {Explained}: deriving {Mikolov} et al.'s negative-sampling word-embedding method},
	shorttitle = {word2vec {Explained}},
	url = {http://arxiv.org/abs/1402.3722},
	doi = {10.48550/arXiv.1402.3722},
	abstract = {The word2vec software of Tomas Mikolov and colleagues (https://code.google.com/p/word2vec/ ) has gained a lot of traction lately, and provides state-of-the-art word embeddings. The learning models behind the software are described in two research papers. We found the description of the models in these papers to be somewhat cryptic and hard to follow. While the motivations and presentation may be obvious to the neural-networks language-modeling crowd, we had to struggle quite a bit to figure out the rationale behind the equations. This note is an attempt to explain equation (4) (negative sampling) in "Distributed Representations of Words and Phrases and their Compositionality" by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado and Jeffrey Dean.},
	urldate = {2023-11-04},
	publisher = {arXiv},
	author = {Goldberg, Yoav and Levy, Omer},
	month = feb,
	year = {2014},
	note = {arXiv:1402.3722 [cs, stat]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{jin_fedml-he_2023,
	title = {{FedML}-{HE}: {An} {Efficient} {Homomorphic}-{Encryption}-{Based} {Privacy}-{Preserving} {Federated} {Learning} {System}},
	shorttitle = {{FedML}-{HE}},
	url = {http://arxiv.org/abs/2303.10837},
	doi = {10.48550/arXiv.2303.10837},
	abstract = {Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data. However, privacy concerns arise as the aggregated local models on the server may reveal sensitive personal information by inversion attacks. Privacy-preserving methods, such as homomorphic encryption (HE), then become necessary for FL training. Despite HE's privacy advantages, its applications suffer from impractical overheads, especially for foundation models. In this paper, we present FedML-HE, the first practical federated learning system with efficient HE-based secure model aggregation. FedML-HE proposes to selectively encrypt sensitive parameters, significantly reducing both computation and communication overheads during training while providing customizable privacy preservation. Our optimized system demonstrates considerable overhead reduction, particularly for large foundation models (e.g., {\textasciitilde}10x reduction for ResNet-50, and up to {\textasciitilde}40x reduction for BERT), demonstrating the potential for scalable HE-based FL deployment.},
	urldate = {2023-11-03},
	publisher = {arXiv},
	author = {Jin, Weizhao and Yao, Yuhang and Han, Shanshan and Joe-Wong, Carlee and Ravi, Srivatsan and Avestimehr, Salman and He, Chaoyang},
	month = oct,
	year = {2023},
	note = {arXiv:2303.10837 [cs]},
	keywords = {Computer Science - Cryptography and Security, Computer Science - Machine Learning},
}

@article{ma_state---art_2022,
	title = {A state-of-the-art survey on solving non-{IID} data in {Federated} {Learning}},
	volume = {135},
	issn = {0167-739X},
	url = {https://www.sciencedirect.com/science/article/pii/S0167739X22001686},
	doi = {10.1016/j.future.2022.05.003},
	abstract = {Federated Learning (FL) proposed in recent years has received significant attention from researchers in that it can enable multiple clients to cooperatively train global models without revealing private data. This training mode protects users’ privacy without violating the supervision, and aggregates scattered data to exert great potential. However, the data samples on each participating device of FL are usually not independent and identically distributed (IID), which leads to serious statistical heterogeneity challenges for FL. In this article, we analyze and establish the definition of non-IID data problems, and put forward a series of challenges that this problem may bring to FL. We classify existing methods to solve this problem from the researcher’s entry point and subsequent sub-methods, aiming to provide a comprehensive study for solving the problem of non-IID data in FL. Our research shows that non-IID data will not only reduce the performance of the FL model, but also damage the active participation of users in the FL process. Compared with methods based on data-side sharing, enhancement, and selection, it is more common for researchers to improve federated learning algorithms from models, algorithms, and frameworks to solve non-IID problems. To the best of our knowledge, although many efforts have been made to address the problem of non-IID data, there are currently few authoritative systematic reviews in this field and are not up-to-date. In this article, we will fill the gaps in FL and provide researchers with the state-of-the-art research results to solve non-IID problems in FL and promote the further implementation of FL.},
	urldate = {2023-11-03},
	journal = {Future Generation Computer Systems},
	author = {Ma, Xiaodong and Zhu, Jia and Lin, Zhihao and Chen, Shanxuan and Qin, Yangjie},
	month = oct,
	year = {2022},
	keywords = {Federated Learning, Machine learning, Non-IID data, Statistical heterogeneity},
	pages = {244--258},
}

@misc{suzgun_follow_2022,
	title = {Follow the {Wisdom} of the {Crowd}: {Effective} {Text} {Generation} via {Minimum} {Bayes} {Risk} {Decoding}},
	shorttitle = {Follow the {Wisdom} of the {Crowd}},
	url = {http://arxiv.org/abs/2211.07634},
	doi = {10.48550/arXiv.2211.07634},
	abstract = {In open-ended natural-language generation, existing text decoding methods typically struggle to produce text which is both diverse and high-quality. Greedy and beam search are known to suffer from text degeneration and linguistic diversity issues, while temperature, top-k, and nucleus sampling often yield diverse but low-quality outputs. In this work, we present crowd sampling, a family of decoding methods based on Bayesian risk minimization, to address this diversity-quality trade-off. Inspired by the principle of "the wisdom of the crowd," crowd sampling seeks to select a candidate from a pool of candidates that has the least expected risk (i.e., highest expected reward) under a generative model according to a given utility function. Crowd sampling can be seen as a generalization of numerous existing methods, including majority voting, and in practice, it can be used as a drop-in replacement for existing sampling methods. Extensive experiments show that crowd sampling delivers improvements of 3-7 ROUGE and BLEU points across a wide range of tasks, including summarization, data-to-text, translation, and textual style transfer, while achieving new state-of-the-art results on WebNLG and WMT'16.},
	urldate = {2023-11-02},
	publisher = {arXiv},
	author = {Suzgun, Mirac and Melas-Kyriazi, Luke and Jurafsky, Dan},
	month = nov,
	year = {2022},
	note = {arXiv:2211.07634 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{rei_comet_2020,
	title = {{COMET}: {A} {Neural} {Framework} for {MT} {Evaluation}},
	shorttitle = {{COMET}},
	url = {http://arxiv.org/abs/2009.09025},
	doi = {10.48550/arXiv.2009.09025},
	abstract = {We present COMET, a neural framework for training multilingual machine translation evaluation models which obtains new state-of-the-art levels of correlation with human judgements. Our framework leverages recent breakthroughs in cross-lingual pretrained language modeling resulting in highly multilingual and adaptable MT evaluation models that exploit information from both the source input and a target-language reference translation in order to more accurately predict MT quality. To showcase our framework, we train three models with different types of human judgements: Direct Assessments, Human-mediated Translation Edit Rate and Multidimensional Quality Metrics. Our models achieve new state-of-the-art performance on the WMT 2019 Metrics shared task and demonstrate robustness to high-performing systems.},
	urldate = {2023-11-02},
	publisher = {arXiv},
	author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C. and Lavie, Alon},
	month = oct,
	year = {2020},
	note = {arXiv:2009.09025 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@inproceedings{stewart_comet_2020,
	title = {{COMET} - {Deploying} a {New} {State}-of-the-art {MT} {Evaluation} {Metric} in {Production}},
	url = {https://aclanthology.org/2020.amta-user.4},
	abstract = {Craig Stewart, Ricardo Rei, Catarina Farinha, Alon Lavie. Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track). 2020.},
	language = {en-us},
	urldate = {2023-11-02},
	author = {Stewart, Craig and Rei, Ricardo and Farinha, Catarina and Lavie, Alon},
	month = oct,
	year = {2020},
	pages = {78--109},
}

@article{rei_are_nodate,
	title = {Are {References} {Really} {Needed}? {Unbabel}-{IST} 2021 {Submission} for the {Metrics} {Shared} {Task}},
	language = {en},
	author = {Rei, Ricardo and Farinha, Ana C and Zerva, Chrysoula},
}

@inproceedings{rei_cometkiwi_2022,
	address = {Abu Dhabi, United Arab Emirates (Hybrid)},
	title = {{CometKiwi}: {IST}-{Unbabel} 2022 {Submission} for the {Quality} {Estimation} {Shared} {Task}},
	shorttitle = {{CometKiwi}},
	url = {https://aclanthology.org/2022.wmt-1.60},
	abstract = {We present the joint contribution of IST and Unbabel to the WMT 2022 Shared Task on Quality Estimation (QE). Our team participated in all three subtasks: (i) Sentence and Word-level Quality Prediction; (ii) Explainable QE; and (iii) Critical Error Detection. For all tasks we build on top of the COMET framework, connecting it with the predictor-estimator architecture of OpenKiwi, and equipping it with a word-level sequence tagger and an explanation extractor. Our results suggest that incorporating references during pretraining improves performance across several language pairs on downstream tasks, and that jointly training with sentence and word-level objectives yields a further boost. Furthermore, combining attention and gradient information proved to be the top strategy for extracting good explanations of sentence-level QE models. Overall, our submissions achieved the best results for all three tasks for almost all language pairs by a considerable margin.},
	urldate = {2023-11-02},
	booktitle = {Proceedings of the {Seventh} {Conference} on {Machine} {Translation} ({WMT})},
	publisher = {Association for Computational Linguistics},
	author = {Rei, Ricardo and Treviso, Marcos and Guerreiro, Nuno M. and Zerva, Chrysoula and Farinha, Ana C and Maroti, Christine and C. de Souza, José G. and Glushkova, Taisiya and Alves, Duarte and Coheur, Luisa and Lavie, Alon and Martins, André F. T.},
	editor = {Koehn, Philipp and Barrault, Loïc and Bojar, Ondřej and Bougares, Fethi and Chatterjee, Rajen and Costa-jussà, Marta R. and Federmann, Christian and Fishel, Mark and Fraser, Alexander and Freitag, Markus and Graham, Yvette and Grundkiewicz, Roman and Guzman, Paco and Haddow, Barry and Huck, Matthias and Jimeno Yepes, Antonio and Kocmi, Tom and Martins, André and Morishita, Makoto and Monz, Christof and Nagata, Masaaki and Nakazawa, Toshiaki and Negri, Matteo and Névéol, Aurélie and Neves, Mariana and Popel, Martin and Turchi, Marco and Zampieri, Marcos},
	month = dec,
	year = {2022},
	pages = {634--645},
}

@inproceedings{rei_searching_2022,
	address = {Ghent, Belgium},
	title = {Searching for {COMETINHO}: {The} {Little} {Metric} {That} {Could}},
	shorttitle = {Searching for {COMETINHO}},
	url = {https://aclanthology.org/2022.eamt-1.9},
	abstract = {In recent years, several neural fine-tuned machine translation evaluation metrics such as COMET and BLEURT have been proposed. These metrics achieve much higher correlations with human judgments than lexical overlap metrics at the cost of computational efficiency and simplicity, limiting their applications to scenarios in which one has to score thousands of translation hypothesis (e.g. scoring multiple systems or Minimum Bayes Risk decoding). In this paper, we explore optimization techniques, pruning, and knowledge distillation to create more compact and faster COMET versions. Our results show that just by optimizing the code through the use of caching and length batching we can reduce inference time between 39\% and 65\% when scoring multiple systems. Also, we show that pruning COMET can lead to a 21\% model reduction without affecting the model's accuracy beyond 0.01 Kendall tau correlation. Furthermore, we present DISTIL-COMET a lightweight distilled version that is 80\% smaller and 2.128x faster while attaining a performance close to the original model and above strong baselines such as BERTSCORE and PRISM.},
	urldate = {2023-11-02},
	booktitle = {Proceedings of the 23rd {Annual} {Conference} of the {European} {Association} for {Machine} {Translation}},
	publisher = {European Association for Machine Translation},
	author = {Rei, Ricardo and Farinha, Ana C and de Souza, José G.C. and Ramos, Pedro G. and Martins, André F.T. and Coheur, Luisa and Lavie, Alon},
	editor = {Moniz, Helena and Macken, Lieve and Rufener, Andrew and Barrault, Loïc and Costa-jussà, Marta R. and Declercq, Christophe and Koponen, Maarit and Kemp, Ellie and Pilos, Spyridon and Forcada, Mikel L. and Scarton, Carolina and Van den Bogaert, Joachim and Daems, Joke and Tezcan, Arda and Vanroy, Bram and Fonteyne, Margot},
	month = jun,
	year = {2022},
	pages = {61--70},
}

@misc{mutze_combinatorial_2023,
	title = {Combinatorial {Gray} codes-an updated survey},
	url = {http://arxiv.org/abs/2202.01280},
	doi = {10.48550/arXiv.2202.01280},
	abstract = {A combinatorial Gray code for a class of objects is a listing that contains each object from the class exactly once such that any two consecutive objects in the list differ only by a `small change'. Such listings are known for many different combinatorial objects, including bitstrings, combinations, permutations, partitions, triangulations, but also for objects defined with respect to a fixed graph, such as spanning trees, perfect matchings or vertex colorings. This survey provides a comprehensive picture of the state-of-the-art of the research on combinatorial Gray codes. In particular, it gives an update on Savage's influential survey [C. D. Savage. A survey of combinatorial Gray codes. SIAM Rev., 39(4):605-629, 1997.], incorporating many more recent developments. We also elaborate on the connections to closely related problems in graph theory, algebra, order theory, geometry and algorithms, which embeds this research area into a broader context. Lastly, we collect and propose a number of challenging research problems, thus stimulating new research endeavors.},
	urldate = {2023-11-01},
	publisher = {arXiv},
	author = {Mütze, Torsten},
	month = may,
	year = {2023},
	note = {arXiv:2202.01280 [cs, math]},
	keywords = {Computer Science - Discrete Mathematics, Mathematics - Combinatorics},
}

@inproceedings{li_visualizing_2016,
	address = {San Diego, California},
	title = {Visualizing and {Understanding} {Neural} {Models} in {NLP}},
	url = {https://aclanthology.org/N16-1082},
	doi = {10.18653/v1/N16-1082},
	urldate = {2023-11-01},
	booktitle = {Proceedings of the 2016 {Conference} of the {North} {American} {Chapter} of the {Association} for {Computational} {Linguistics}: {Human} {Language} {Technologies}},
	publisher = {Association for Computational Linguistics},
	author = {Li, Jiwei and Chen, Xinlei and Hovy, Eduard and Jurafsky, Dan},
	editor = {Knight, Kevin and Nenkova, Ani and Rambow, Owen},
	month = jun,
	year = {2016},
	pages = {681--691},
}

@misc{chretien_kullback_2012,
	title = {Kullback {Proximal} {Algorithms} for {Maximum} {Likelihood} {Estimation}},
	url = {http://arxiv.org/abs/1201.5907},
	abstract = {Accelerated algorithms for maximum likelihood image reconstruction are essential for emerging applications such as 3D tomography, dynamic tomographic imaging, and other high dimensional inverse problems. In this paper, we introduce and analyze a class of fast and stable sequential optimization methods for computing maximum likelihood estimates and study its convergence properties. These methods are based on a \{{\textbackslash}it proximal point algorithm\} implemented with the Kullback-Liebler (KL) divergence between posterior densities of the complete data as a proximal penalty function. When the proximal relaxation parameter is set to unity one obtains the classical expectation maximization (EM) algorithm. For a decreasing sequence of relaxation parameters, relaxed versions of EM are obtained which can have much faster asymptotic convergence without sacrifice of monotonicity. We present an implementation of the algorithm using Mor{\textbackslash}'\{e\}'s \{{\textbackslash}it Trust Region\} update strategy. For illustration the method is applied to a non-quadratic inverse problem with Poisson distributed data.},
	urldate = {2023-11-01},
	publisher = {arXiv},
	author = {Chrétien, Stéphane and Hero, Alfred O.},
	month = jan,
	year = {2012},
	note = {arXiv:1201.5907 [stat]},
	keywords = {Statistics - Computation},
}

@article{ranathunga_neural_2023,
	title = {Neural {Machine} {Translation} for {Low}-resource {Languages}: {A} {Survey}},
	volume = {55},
	issn = {0360-0300, 1557-7341},
	shorttitle = {Neural {Machine} {Translation} for {Low}-resource {Languages}},
	url = {https://dl.acm.org/doi/10.1145/3567592},
	doi = {10.1145/3567592},
	abstract = {Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since the early 2000s and has already entered a mature phase. While considered the most widely used solution for Machine Translation, its performance on low-resource language pairs remains sub-optimal compared to the high-resource counterparts due to the unavailability of large parallel corpora. Therefore, the implementation of NMT techniques for low-resource language pairs has been receiving the spotlight recently, thus leading to substantial research on this topic. This article presents a detailed survey of research advancements in low-resource language NMT (LRL-NMT) and quantitative analysis to identify the most popular techniques. We provide guidelines to select the possible NMT technique for a given LRL data setting based on our findings. We also present a holistic view of the LRL-NMT research landscape and provide recommendations to enhance the research efforts further.},
	language = {en},
	number = {11},
	urldate = {2023-11-01},
	journal = {ACM Computing Surveys},
	author = {Ranathunga, Surangika and Lee, En-Shiun Annie and Prifti Skenduli, Marjana and Shekhar, Ravi and Alam, Mehreen and Kaur, Rishemjit},
	month = nov,
	year = {2023},
	pages = {1--37},
}

@inproceedings{schlichtkrull_cross-lingual_2017,
	address = {Valencia, Spain},
	title = {Cross-{Lingual} {Dependency} {Parsing} with {Late} {Decoding} for {Truly} {Low}-{Resource} {Languages}},
	url = {https://aclanthology.org/E17-1021},
	abstract = {In cross-lingual dependency annotation projection, information is often lost during transfer because of early decoding. We present an end-to-end graph-based neural network dependency parser that can be trained to reproduce matrices of edge scores, which can be directly projected across word alignments. We show that our approach to cross-lingual dependency parsing is not only simpler, but also achieves an absolute improvement of 2.25\% averaged across 10 languages compared to the previous state of the art.},
	urldate = {2023-11-01},
	booktitle = {Proceedings of the 15th {Conference} of the {European} {Chapter} of the {Association} for {Computational} {Linguistics}: {Volume} 1, {Long} {Papers}},
	publisher = {Association for Computational Linguistics},
	author = {Schlichtkrull, Michael and Søgaard, Anders},
	editor = {Lapata, Mirella and Blunsom, Phil and Koller, Alexander},
	month = apr,
	year = {2017},
	pages = {220--229},
}

@article{park_decoding_2020,
	title = {Decoding {Strategies} for {Improving} {Low}-{Resource} {Machine} {Translation}},
	volume = {9},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2079-9292},
	url = {https://www.mdpi.com/2079-9292/9/10/1562},
	doi = {10.3390/electronics9101562},
	abstract = {Pre-processing and post-processing are significant aspects of natural language processing (NLP) application software. Pre-processing in neural machine translation (NMT) includes subword tokenization to alleviate the problem of unknown words, parallel corpus filtering that only filters data suitable for training, and data augmentation to ensure that the corpus contains sufficient content. Post-processing includes automatic post editing and the application of various strategies during decoding in the translation process. Most recent NLP researches are based on the Pretrain-Finetuning Approach (PFA). However, when small and medium-sized organizations with insufficient hardware attempt to provide NLP services, throughput and memory problems often occur. These difficulties increase when utilizing PFA to process low-resource languages, as PFA requires large amounts of data, and the data for low-resource languages are often insufficient. Utilizing the current research premise that NMT model performance can be enhanced through various pre-processing and post-processing strategies without changing the model, we applied various decoding strategies to Korean–English NMT, which relies on a low-resource language pair. Through comparative experiments, we proved that translation performance could be enhanced without changes to the model. We experimentally examined how performance changed in response to beam size changes and n-gram blocking, and whether performance was enhanced when a length penalty was applied. The results showed that various decoding strategies enhance the performance and compare well with previous Korean–English NMT approaches. Therefore, the proposed methodology can improve the performance of NMT models, without the use of PFA; this presents a new perspective for improving machine translation performance.},
	language = {en},
	number = {10},
	urldate = {2023-11-01},
	journal = {Electronics},
	author = {Park, Chanjun and Yang, Yeongwook and Park, Kinam and Lim, Heuiseok},
	month = oct,
	year = {2020},
	note = {Number: 10
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {Korean–English neural machine translation, decoding strategies, efficiency processing, neural machine translation, post-processing, transformer},
	pages = {1562},
}

@article{he_improved_2016,
	title = {Improved {Neural} {Machine} {Translation} with {SMT} {Features}},
	volume = {30},
	copyright = {Copyright (c)},
	issn = {2374-3468},
	url = {https://ojs.aaai.org/index.php/AAAI/article/view/9983},
	doi = {10.1609/aaai.v30i1.9983},
	abstract = {Neural machine translation (NMT) conducts end-to-end translation with a source language encoder and a target language decoder, making promising translation performance. However, as a newly emerged approach, the method has some limitations. An NMT system usually has to apply a vocabulary of certain size to avoid the time-consuming training and decoding, thus it causes a serious out-of-vocabulary problem. Furthermore, the decoder lacks a mechanism to guarantee all the source words to be translated and usually favors short translations, resulting in fluent but inadequate translations. In order to solve the above problems, we incorporate statistical machine translation (SMT) features, such as a translation model and an n-gram language model, with the NMT model under the log-linear framework. Our experiments show that the proposed method significantly improves the translation quality of the state-ofthe-art NMT system on Chinese-to-English translation tasks. Our method produces a gain of up to 2.33 BLEU score on NIST open test sets.},
	language = {en},
	number = {1},
	urldate = {2023-11-01},
	journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
	author = {He, Wei and He, Zhongjun and Wu, Hua and Wang, Haifeng},
	month = feb,
	year = {2016},
	note = {Number: 1},
	keywords = {Recurrent Neural Network},
}

@misc{fan_hierarchical_2018,
	title = {Hierarchical {Neural} {Story} {Generation}},
	url = {https://arxiv.org/abs/1805.04833v1},
	abstract = {We explore story generation: creative systems that can build coherent and fluent passages of text about a topic. We collect a large dataset of 300K human-written stories paired with writing prompts from an online forum. Our dataset enables hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text. We gain further improvements with a novel form of model fusion that improves the relevance of the story to the prompt, and adding a new gated multi-scale self-attention mechanism to model long-range context. Experiments show large improvements over strong baselines on both automated and human evaluations. Human judges prefer stories generated by our approach to those from a strong non-hierarchical model by a factor of two to one.},
	language = {en},
	urldate = {2023-11-01},
	journal = {arXiv.org},
	author = {Fan, Angela and Lewis, Mike and Dauphin, Yann},
	month = may,
	year = {2018},
}

@inproceedings{cohen_empirical_2019,
	title = {Empirical {Analysis} of {Beam} {Search} {Performance} {Degradation} in {Neural} {Sequence} {Models}},
	url = {https://proceedings.mlr.press/v97/cohen19a.html},
	abstract = {Beam search is the most popular inference algorithm for decoding neural sequence models. Unlike greedy search, beam search allows for non-greedy local decisions that can potentially lead to a sequence with a higher overall probability. However, work on a number of applications has found that the quality of the highest probability hypothesis found by beam search degrades with large beam widths. We perform an empirical study of the behavior of beam search across three sequence synthesis tasks. We find that increasing the beam width leads to sequences that are disproportionately based on early, very low probability tokens that are followed by a sequence of tokens with higher (conditional) probability. We show that, empirically, such sequences are more likely to have a lower evaluation score than lower probability sequences without this pattern. Using the notion of search discrepancies from heuristic search, we hypothesize that large discrepancies are the cause of the performance degradation. We show that this hypothesis generalizes the previous ones in machine translation and image captioning. To validate our hypothesis, we show that constraining beam search to avoid large discrepancies eliminates the performance degradation.},
	language = {en},
	urldate = {2023-10-31},
	booktitle = {Proceedings of the 36th {International} {Conference} on {Machine} {Learning}},
	publisher = {PMLR},
	author = {Cohen, Eldan and Beck, Christopher},
	month = may,
	year = {2019},
	note = {ISSN: 2640-3498},
	pages = {1290--1299},
}

@inproceedings{cho_properties_2014,
	address = {Doha, Qatar},
	title = {On the {Properties} of {Neural} {Machine} {Translation}: {Encoder}–{Decoder} {Approaches}},
	shorttitle = {On the {Properties} of {Neural} {Machine} {Translation}},
	url = {https://aclanthology.org/W14-4012},
	doi = {10.3115/v1/W14-4012},
	urldate = {2023-10-31},
	booktitle = {Proceedings of {SSST}-8, {Eighth} {Workshop} on {Syntax}, {Semantics} and {Structure} in {Statistical} {Translation}},
	publisher = {Association for Computational Linguistics},
	author = {Cho, Kyunghyun and van Merriënboer, Bart and Bahdanau, Dzmitry and Bengio, Yoshua},
	editor = {Wu, Dekai and Carpuat, Marine and Carreras, Xavier and Vecchi, Eva Maria},
	month = oct,
	year = {2014},
	pages = {103--111},
}

@misc{paulai_paper_2023,
	title = {Paper explained — {If} {Beam} {Search} is the {Answer}, {What} was the {Question}?},
	url = {https://medium.com/@anna.vnlst/paper-explained-if-beam-search-is-the-answer-what-was-the-question-3f58ce972fcf},
	abstract = {Decoding neural networks},
	language = {en},
	urldate = {2023-10-31},
	journal = {Medium},
	author = {paulai},
	month = jul,
	year = {2023},
}

@inproceedings{stahlberg_syntactically_2016,
	address = {Berlin, Germany},
	title = {Syntactically {Guided} {Neural} {Machine} {Translation}},
	url = {https://aclanthology.org/P16-2049},
	doi = {10.18653/v1/P16-2049},
	urldate = {2023-10-31},
	booktitle = {Proceedings of the 54th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics} ({Volume} 2: {Short} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Stahlberg, Felix and Hasler, Eva and Waite, Aurelien and Byrne, Bill},
	editor = {Erk, Katrin and Smith, Noah A.},
	month = aug,
	year = {2016},
	pages = {299--305},
}

@inproceedings{paul_pattern_2023,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Pattern {Classification} with {Temporally} {Stochastic} {Cellular} {Automata}},
	isbn = {978-3-031-42250-8},
	doi = {10.1007/978-3-031-42250-8_10},
	abstract = {This paper introduces a new model of cellular automata where two rules (say, f and g) are applied temporally on the cells with some probability. The rule f is called default rule and g is the noise which is applied with probability \$\${\textbackslash}tau \$\$τ(noise rate). This new class of automata is named as Temporally Stochastic Cellular Automata (TSCAs). The dynamical behaviour of these automata has been studied to identify the TSCAs that converge to fixed point from any seed. Finally, we use each of the convergent TSCAs on some standard datasets and observe the effectiveness of each TSCA as pattern classifier. It is observed that the proposed TSCA based classifier shows competitive performance in comparison with existing classifier algorithms.},
	language = {en},
	booktitle = {Cellular {Automata} and {Discrete} {Complex} {Systems}},
	publisher = {Springer Nature Switzerland},
	author = {Paul, Subrata and Roy, Souvik and Das, Sukanta},
	editor = {Manzoni, Luca and Mariot, Luca and Roy Chowdhury, Dipanwita},
	year = {2023},
	keywords = {Convergence, Dynamical behaviour, Pattern Classifier, Temporally Stochastic Cellular Automata},
	pages = {137--152},
}

@inproceedings{gladkova_intrinsic_2016,
	address = {Berlin, Germany},
	title = {Intrinsic {Evaluations} of {Word} {Embeddings}: {What} {Can} {We} {Do} {Better}?},
	shorttitle = {Intrinsic {Evaluations} of {Word} {Embeddings}},
	url = {https://aclanthology.org/W16-2507},
	doi = {10.18653/v1/W16-2507},
	urldate = {2023-10-31},
	booktitle = {Proceedings of the 1st {Workshop} on {Evaluating} {Vector}-{Space} {Representations} for {NLP}},
	publisher = {Association for Computational Linguistics},
	author = {Gladkova, Anna and Drozd, Aleksandr},
	month = aug,
	year = {2016},
	pages = {36--42},
}

@article{mogren_c-rnn-gan_nodate,
	title = {C-{RNN}-{GAN}: {Continuous} recurrent neural networks},
	abstract = {Generative adversarial networks have been proposed as a way of efﬁciently training deep generative neural networks. We propose a generative adversarial model that works on continuous sequential data, and apply it by training it on a collection of classical music. We conclude that it generates music that sounds better and better as the model is trained, report statistics on generated music, and let the reader judge the quality by downloading the generated songs.},
	language = {en},
	author = {Mogren, Olof},
}

@misc{yang_breaking_2018,
	title = {Breaking the {Beam} {Search} {Curse}: {A} {Study} of ({Re}-){Scoring} {Methods} and {Stopping} {Criteria} for {Neural} {Machine} {Translation}},
	shorttitle = {Breaking the {Beam} {Search} {Curse}},
	url = {http://arxiv.org/abs/1808.09582},
	doi = {10.48550/arXiv.1808.09582},
	abstract = {Beam search is widely used in neural machine translation, and usually improves translation quality compared to greedy search. It has been widely observed that, however, beam sizes larger than 5 hurt translation quality. We explain why this happens, and propose several methods to address this problem. Furthermore, we discuss the optimal stopping criteria for these methods. Results show that our hyperparameter-free methods outperform the widely-used hyperparameter-free heuristic of length normalization by +2.0 BLEU, and achieve the best results among all methods on Chinese-to-English translation.},
	urldate = {2023-10-30},
	publisher = {arXiv},
	author = {Yang, Yilin and Huang, Liang and Ma, Mingbo},
	month = oct,
	year = {2018},
	note = {arXiv:1808.09582 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{fernandes_quality-aware_2022,
	title = {Quality-{Aware} {Decoding} for {Neural} {Machine} {Translation}},
	url = {http://arxiv.org/abs/2205.00978},
	doi = {10.48550/arXiv.2205.00978},
	abstract = {Despite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers around finding the most probable translation according to the model (MAP decoding), approximated with beam search. In this paper, we bring together these two lines of research and propose quality-aware decoding for NMT, by leveraging recent breakthroughs in reference-free and reference-based MT evaluation through various inference methods like \$N\$-best reranking and minimum Bayes risk decoding. We perform an extensive comparison of various possible candidate generation and ranking methods across four datasets and two model classes and find that quality-aware decoding consistently outperforms MAP-based decoding according both to state-of-the-art automatic metrics (COMET and BLEURT) and to human assessments. Our code is available at https://github.com/deep-spin/qaware-decode.},
	urldate = {2023-10-30},
	publisher = {arXiv},
	author = {Fernandes, Patrick and Farinhas, António and Rei, Ricardo and de Souza, José G. C. and Ogayo, Perez and Neubig, Graham and Martins, André F. T.},
	month = may,
	year = {2022},
	note = {arXiv:2205.00978 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@article{richter_towards_2019,
	title = {Towards the development of music as an intervention for {Insomnia} treatment: {A} research synthesis},
	copyright = {CC0 1.0 Universal, Creative Commons Zero v1.0 Universal, 2020-07-24T14:20:00Z, 12 months},
	shorttitle = {Towards the development of music as an intervention for {Insomnia} treatment},
	url = {https://pearl-prod.plymouth.ac.uk/handle/10026.1/14697},
	doi = {10.24382/1211},
	abstract = {Though research has been conducted with regards to music and sleep over the last two decades, there is still a significant uncertainty to its effectiveness due to the lack of scientific validation through objective testing and data analysis (Jespersen et al 2015). It has been proven that music can help in the management of physical pain and psychological conditions such as depression and anxiety, hence, music is already being implemented in medical treatments in the form of music therapy (Jespersen et al 2015). The literature review commences with a brief consideration of conventional treatment methods including pharmaceutical and talking therapy-based methods. It then investigates various research trials using music as the main intervention and contributes to the field by introducing and exploring certain sleep-music products which are available on the open market. The thesis aims to compare research regarding the psychology of music and sleep, sleep sciences, music as an intervention for insomnia, music-assisted relaxation (PMR) for Insomnia, Cognitive Behavioural Therapy for Insomnia (CBTi), and Sleep Medication. From these two main areas of interest comparisons and contrasts can be drawn up which highlight specific areas which may need more attention. From the research certain gaps can be acknowledged and suggestions can be made for the continuation of sleep-music research including the need for familiarity and preference in surroundings and music choice, but also highlights the confusion on the open market with regard to binaural beats technology for example. It is suggested in this thesis that more time is required for the evidence in support of sleep-music to build, but also that care must be given as certain online sleep-music products may restrict or obscure genuine research-driven outputs.},
	language = {en},
	urldate = {2023-10-29},
	author = {Richter, Miriam},
	collaborator = {{Plymouth University} and {Plymouth University} and {Faculty Of Arts And Humanities}},
	year = {2019},
	note = {Publisher: University of Plymouth},
	keywords = {Insomnia, Music, ResM, literature review, music for insomnia},
}

@article{huang_insomnia_2023,
	title = {Insomnia and {Its} {Non}-{Pharmacological} {Management} in {Older} {Adults}},
	issn = {2196-7865},
	doi = {10.1007/s13670-023-00397-1},
	abstract = {Purpose of Review  Insomnia, a sleep disorder involving difficulty falling, maintaining, or obtaining good quality sleep, is a common health complaint in older adults. The purpose of this review is to summarize the prevalence, risk factors, and consequences of insomnia, introduce available non-pharmacological interventions, provide implications for promoting sleep health in the older adult population, and provide guidance for clinical practice and future research.},
	language = {en},
	urldate = {2023-10-29},
	journal = {Current Geriatrics Reports},
	author = {Huang, Jing and Antonsdottir, Inga M. and Wang, Richard and Li, Mengchi and Li, Junxin},
	month = sep,
	year = {2023},
}

@article{mohamad_zamani_insomnia_2022,
	title = {Insomnia {Audio} {Therapy} {Mobile} {Application} with {Music} {Recommender} {System}},
	volume = {3},
	issn = {27350703},
	url = {https://myjms.mohe.gov.my/index.php/mij/article/view/18264},
	doi = {10.24191/mij.v3i1.18264},
	abstract = {With the quick advancement of Internet and artificial intelligence technologies, development of a robust and accurate music recommendation system has become an important issue in the field of music information retrieval. Music recommendation system has been widely used in many real-life applications, including in the health domain as an alternative of therapies. This paper presents the research design and implementation of music recommendation system that possible to be used as an insomnia audio therapy in a mobile application platform. The research focused on investigating the performances of three machine learning algorithms namely Random Forest, Decision Tree and Support Vector Machine to be selected as the music recommendation tool. For the machine learning training and testing purposes, data was collected based on the simulated run of the proposed insomnia audio therapy mobile application. The results indicated that Random Forest performed as the best machine learning algorithm in predicting the relevant music. The proposed mobile application with machine learning music recommender system will provide a basis for the realization of intelligent music therapy in treating insomnia disorder patients as well as in other music applications.},
	language = {en},
	number = {1},
	urldate = {2023-10-29},
	journal = {Mathematical Sciences and Informatics Journal},
	author = {Mohamad Zamani, Nur Azmina and Omar, Nasiroh and Huda Azmi, Nur Damira},
	month = may,
	year = {2022},
	pages = {29--38},
}

@article{daly_neural_2023,
	title = {Neural decoding of music from the {EEG}},
	volume = {13},
	copyright = {2023 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-022-27361-x},
	doi = {10.1038/s41598-022-27361-x},
	abstract = {Neural decoding models can be used to decode neural representations of visual, acoustic, or semantic information. Recent studies have demonstrated neural decoders that are able to decode accoustic information from a variety of neural signal types including electrocortiography (ECoG) and the electroencephalogram (EEG). In this study we explore how functional magnetic resonance imaging (fMRI) can be combined with EEG to develop an accoustic decoder. Specifically, we first used a joint EEG-fMRI paradigm to record brain activity while participants listened to music. We then used fMRI-informed EEG source localisation and a bi-directional long-term short term deep learning network to first extract neural information from the EEG related to music listening and then to decode and reconstruct the individual pieces of music an individual was listening to. We further validated our decoding model by evaluating its performance on a separate dataset of EEG-only recordings. We were able to reconstruct music, via our fMRI-informed EEG source analysis approach, with a mean rank accuracy of 71.8\% (\$\$n{\textasciitilde}={\textasciitilde}18\$\$, \$\$p{\textasciitilde}{\textless}{\textasciitilde}0.05\$\$). Using only EEG data, without participant specific fMRI-informed source analysis, we were able to identify the music a participant was listening to with a mean rank accuracy of 59.2\% (\$\$n{\textasciitilde}={\textasciitilde}19\$\$, \$\$p{\textasciitilde}{\textless}{\textasciitilde}0.05\$\$). This demonstrates that our decoding model may use fMRI-informed source analysis to aid EEG based decoding and reconstruction of acoustic information from brain activity and makes a step towards building EEG-based neural decoders for other complex information domains such as other acoustic, visual, or semantic information.},
	language = {en},
	number = {1},
	urldate = {2023-10-29},
	journal = {Scientific Reports},
	author = {Daly, Ian},
	month = jan,
	year = {2023},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Cognitive neuroscience, Computational neuroscience, Design, Imaging techniques, Machine learning, synthesis and processing},
	pages = {624},
}

@article{liu_emotion-based_2023,
	title = {An emotion-based personalized music recommendation framework for emotion improvement},
	volume = {60},
	issn = {0306-4573},
	url = {https://www.sciencedirect.com/science/article/pii/S0306457322003570},
	doi = {10.1016/j.ipm.2022.103256},
	abstract = {Music has a close relationship with people's emotion and mental status. Music recommendation has both economic and social benefits. Unfortunately, most existing music recommendation methods were constructed based on genre features (e.g., style and album), which cannot meet the emotional needs of listeners. Furthermore, the “filter bubble” effect may make the situation even worse, when a user seeks music for emotional support. In this study, we designed a novel emotion-based personalized music recommendation framework to meet users’ emotional needs and help improve their mental status. In our framework, we designed a LSTM-based model to select the most suitable music based on users’ mood in previous period and current emotion stimulus. A care factor was used to adjust the results so that users’ mental status could be improved by the recommendation. The empirical experiments and user study showed that the recommendations of our novel framework are precise and helpful for users.},
	number = {3},
	urldate = {2023-10-29},
	journal = {Information Processing \& Management},
	author = {Liu, Zhiyuan and Xu, Wei and Zhang, Wenping and Jiang, Qiqi},
	month = may,
	year = {2023},
	keywords = {Deep learning, Emotional needs, Music emotion recognition, Personalized music recommendation, User study},
	pages = {103256},
}

@misc{schneider_archisound_2023,
	title = {{ArchiSound}: {Audio} {Generation} with {Diffusion}},
	shorttitle = {{ArchiSound}},
	url = {http://arxiv.org/abs/2301.13267},
	doi = {10.48550/arXiv.2301.13267},
	abstract = {The recent surge in popularity of diffusion models for image generation has brought new attention to the potential of these models in other areas of media generation. One area that has yet to be fully explored is the application of diffusion models to audio generation. Audio generation requires an understanding of multiple aspects, such as the temporal dimension, long term structure, multiple layers of overlapping sounds, and the nuances that only trained listeners can detect. In this work, we investigate the potential of diffusion models for audio generation. We propose a set of models to tackle multiple aspects, including a new method for text-conditional latent audio diffusion with stacked 1D U-Nets, that can generate multiple minutes of music from a textual description. For each model, we make an effort to maintain reasonable inference speed, targeting real-time on a single consumer GPU. In addition to trained models, we provide a collection of open source libraries with the hope of simplifying future work in the field. Samples can be found at https://bit.ly/audio-diffusion. Codes are at https://github.com/archinetai/audio-diffusion-pytorch.},
	urldate = {2023-10-29},
	publisher = {arXiv},
	author = {Schneider, Flavio},
	month = jan,
	year = {2023},
	note = {arXiv:2301.13267 [cs, eess]},
	keywords = {Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{huang_noise2music_2023,
	title = {{Noise2Music}: {Text}-conditioned {Music} {Generation} with {Diffusion} {Models}},
	shorttitle = {{Noise2Music}},
	url = {http://arxiv.org/abs/2302.03917},
	doi = {10.48550/arXiv.2302.03917},
	abstract = {We introduce Noise2Music, where a series of diffusion models is trained to generate high-quality 30-second music clips from text prompts. Two types of diffusion models, a generator model, which generates an intermediate representation conditioned on text, and a cascader model, which generates high-fidelity audio conditioned on the intermediate representation and possibly the text, are trained and utilized in succession to generate high-fidelity music. We explore two options for the intermediate representation, one using a spectrogram and the other using audio with lower fidelity. We find that the generated audio is not only able to faithfully reflect key elements of the text prompt such as genre, tempo, instruments, mood, and era, but goes beyond to ground fine-grained semantics of the prompt. Pretrained large language models play a key role in this story -- they are used to generate paired text for the audio of the training set and to extract embeddings of the text prompts ingested by the diffusion models. Generated examples: https://google-research.github.io/noise2music},
	urldate = {2023-10-29},
	publisher = {arXiv},
	author = {Huang, Qingqing and Park, Daniel S. and Wang, Tao and Denk, Timo I. and Ly, Andy and Chen, Nanxin and Zhang, Zhengdong and Zhang, Zhishuai and Yu, Jiahui and Frank, Christian and Engel, Jesse and Le, Quoc V. and Chan, William and Chen, Zhifeng and Han, Wei},
	month = mar,
	year = {2023},
	note = {arXiv:2302.03917 [cs, eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@misc{schneider_mousai_2023,
	title = {Mo{\textbackslash}{\textasciicircum}usai: {Text}-to-{Music} {Generation} with {Long}-{Context} {Latent} {Diffusion}},
	shorttitle = {Mo{\textbackslash}{\textasciicircum}usai},
	url = {http://arxiv.org/abs/2301.11757},
	doi = {10.48550/arXiv.2301.11757},
	abstract = {Recent years have seen the rapid development of large generative models for text; however, much less research has explored the connection between text and another "language" of communication -- music. Music, much like text, can convey emotions, stories, and ideas, and has its own unique structure and syntax. In our work, we bridge text and music via a text-to-music generation model that is highly efficient, expressive, and can handle long-term structure. Specifically, we develop Mo{\textbackslash}{\textasciicircum}usai, a cascading two-stage latent diffusion model that can generate multiple minutes of high-quality stereo music at 48kHz from textual descriptions. Moreover, our model features high efficiency, which enables real-time inference on a single consumer GPU with a reasonable speed. Through experiments and property analyses, we show our model's competence over a variety of criteria compared with existing music generation models. Lastly, to promote the open-source culture, we provide a collection of open-source libraries with the hope of facilitating future work in the field. We open-source the following: Codes: https://github.com/archinetai/audio-diffusion-pytorch; music samples for this paper: http://bit.ly/44ozWDH; all music samples for all models: https://bit.ly/audio-diffusion.},
	urldate = {2023-10-29},
	publisher = {arXiv},
	author = {Schneider, Flavio and Kamal, Ojasv and Jin, Zhijing and Schölkopf, Bernhard},
	month = oct,
	year = {2023},
	note = {arXiv:2301.11757 [cs, eess]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@article{ji_survey_2023,
	title = {A {Survey} on {Deep} {Learning} for {Symbolic} {Music} {Generation}: {Representations}, {Algorithms}, {Evaluations}, and {Challenges}},
	volume = {56},
	issn = {0360-0300},
	shorttitle = {A {Survey} on {Deep} {Learning} for {Symbolic} {Music} {Generation}},
	url = {https://dl.acm.org/doi/10.1145/3597493},
	doi = {10.1145/3597493},
	abstract = {Significant progress has been made in symbolic music generation with the help of deep learning techniques. However, the tasks covered by symbolic music generation have not been well summarized, and the evolution of generative models for the specific music generation task has not been illustrated systematically. This paper attempts to provide a task-oriented survey of symbolic music generation based on deep learning techniques, covering most of the currently popular music generation tasks. The distinct models under the same task are set forth briefly and strung according to their motivations, basically in chronological order. Moreover, we summarize the common datasets suitable for various tasks, discuss the music representations and the evaluation methods, highlight current challenges in symbolic music generation, and finally point out potential future research directions.},
	number = {1},
	urldate = {2023-10-29},
	journal = {ACM Computing Surveys},
	author = {Ji, Shulei and Yang, Xinyu and Luo, Jing},
	month = aug,
	year = {2023},
	keywords = {Symbolic music generation, deep learning, music evaluation methods, symbolic music representations, task-oriented survey},
	pages = {7:1--7:39},
}

@misc{noauthor_papers_nodate,
	title = {Papers with {Code} - {Compound} {Word} {Transformer}: {Learning} to {Compose} {Full}-{Song} {Music} over {Dynamic} {Directed} {Hypergraphs}},
	shorttitle = {Papers with {Code} - {Compound} {Word} {Transformer}},
	url = {https://paperswithcode.com/paper/compound-word-transformer-learning-to-compose},
	abstract = {Implemented in 4 code libraries.},
	language = {en},
	urldate = {2023-10-29},
}

@misc{noauthor_papers_nodate-1,
	title = {Papers with {Code} - {It}'s {Raw}! {Audio} {Generation} with {State}-{Space} {Models}},
	url = {https://paperswithcode.com/paper/it-s-raw-audio-generation-with-state-space},
	abstract = {Implemented in 5 code libraries.},
	language = {en},
	urldate = {2023-10-29},
}

@article{miller_neilan_agent-based_2021,
	title = {Agent-based modeling of the central amygdala and pain using cell-type specific physiological parameters},
	volume = {17},
	issn = {1553-7358},
	doi = {10.1371/journal.pcbi.1009097},
	abstract = {The amygdala is a brain area involved in emotional regulation and pain. Over the course of the last 20 years, multiple researchers have studied sensory and motor connections within the amygdala in trying to understand the ultimate role of this structure in pain perception and descending control of pain. A number of investigators have been using cell-type specific manipulations to probe the underlying circuitry of the amygdala. As data have accumulated in this research space, we recognized a critical need for a single framework to integrate these data and evaluate emergent system-level responses. In this manuscript, we present an agent-based computational model of two distinct inhibitory neuron populations in the amygdala, those that express protein kinase C delta (PKCδ) and those that express somatostatin (SOM). We utilized a network of neural links to simulate connectivity and the transmission of inhibitory signals between neurons. Type-specific parameters describing the response of these neurons to noxious stimuli were estimated from published physiological and immunological data as well as our own wet-lab experiments. The model outputs an abstract measure of pain, which is calculated in terms of the cumulative pro-nociceptive and anti-nociceptive activity across neurons in both hemispheres of the amygdala. Results demonstrate the ability of the model to produce changes in pain that are consistent with published studies and highlight the importance of several model parameters. In particular, we found that the relative proportion of PKCδ and SOM neurons within each hemisphere is a key parameter in predicting pain and we explored model predictions for three possible values of this parameter. We compared model predictions of pain to data from our earlier behavioral studies and found areas of similarity as well as distinctions between the data sets. These differences, in particular, suggest a number of wet-lab experiments that could be done in the future.},
	language = {eng},
	number = {6},
	journal = {PLoS computational biology},
	author = {Miller Neilan, Rachael and Majetic, Gabrielle and Gil-Silva, Mauricio and Adke, Anisha P. and Carrasquillo, Yarimar and Kolber, Benedict J.},
	month = jun,
	year = {2021},
	pmid = {34101729},
	pmcid = {PMC8213159},
	keywords = {Animals, Central Amygdaloid Nucleus, Computational Biology, Disease Models, Animal, Dominance, Cerebral, Electrophysiological Phenomena, Humans, In Vitro Techniques, Male, Mice, Models, Neurological, Nerve Net, Neuralgia, Neurons, Pain, Protein Kinase C-delta, Somatostatin, Systems Analysis},
	pages = {e1009097},
}

@article{vlachos_stopping_2008,
	title = {A stopping criterion for active learning},
	volume = {22},
	issn = {0885-2308},
	url = {https://www.sciencedirect.com/science/article/pii/S088523080700068X},
	doi = {10.1016/j.csl.2007.12.001},
	abstract = {Active learning (AL) is a framework that attempts to reduce the cost of annotating training material for statistical learning methods. While a lot of papers have been presented on applying AL to natural language processing tasks reporting impressive savings, little work has been done on defining a stopping criterion. In this work, we present a stopping criterion for active learning based on the way instances are selected during uncertainty-based sampling and verify its applicability in a variety of settings. The statistical learning models used in our study are support vector machines (SVMs), maximum entropy models and Bayesian logistic regression and the tasks performed are text classification, named entity recognition and shallow parsing. In addition, we present a method for multiclass mutually exclusive SVM active learning.},
	number = {3},
	urldate = {2023-10-28},
	journal = {Computer Speech \& Language},
	author = {Vlachos, Andreas},
	month = jul,
	year = {2008},
	keywords = {Active learning, NER, SVMs, Text classification},
	pages = {295--312},
}

@article{menon_overparameterisation_2021,
	title = {{OVERPARAMETERISATION} {AND} {WORST}-{CASE} {GENER}- {ALISATION}: {FRIEND} {OR} {FOE}?},
	abstract = {Overparameterised neural networks have demonstrated the remarkable ability to perfectly ﬁt training samples, while still generalising to unseen test samples. However, several recent works have revealed that such models’ good average performance does not always translate to good worst-case performance: in particular, they may perform poorly on subgroups that are under-represented in the training set. In this paper, we show that in certain settings, overparameterised models’ performance on under-represented subgroups may be improved via post-hoc processing. Speciﬁcally, such models’ bias can be restricted to their classiﬁcation layers, and manifest as structured prediction shifts for rare subgroups. We detail two post-hoc correction techniques to mitigate this bias, which operate purely on the outputs of standard model training. We empirically verify that with such post-hoc correction, overparameterisation can improve average and worst-case performance.},
	language = {en},
	author = {Menon, Aditya Krishna and Rawat, Ankit Singh and Kumar, Sanjiv},
	year = {2021},
}

@misc{kirichenko_last_2023,
	title = {Last {Layer} {Re}-{Training} is {Sufficient} for {Robustness} to {Spurious} {Correlations}},
	url = {http://arxiv.org/abs/2204.02937},
	doi = {10.48550/arXiv.2204.02937},
	abstract = {Neural network classifiers can largely rely on simple spurious features, such as backgrounds, to make predictions. However, even in these cases, we show that they still often learn core features associated with the desired attributes of the data, contrary to recent findings. Inspired by this insight, we demonstrate that simple last layer retraining can match or outperform state-of-the-art approaches on spurious correlation benchmarks, but with profoundly lower complexity and computational expenses. Moreover, we show that last layer retraining on large ImageNet-trained models can also significantly reduce reliance on background and texture information, improving robustness to covariate shift, after only minutes of training on a single GPU.},
	urldate = {2023-10-28},
	publisher = {arXiv},
	author = {Kirichenko, Polina and Izmailov, Pavel and Wilson, Andrew Gordon},
	month = jun,
	year = {2023},
	note = {arXiv:2204.02937 [cs, stat]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning},
}

@article{izmailov_feature_nodate,
	title = {On {Feature} {Learning} in the {Presence} of {Spurious} {Correlations}},
	abstract = {Deep classifiers are known to rely on spurious features — patterns which are correlated with the target on the training data but not inherently relevant to the learning problem, such as the image backgrounds when classifying the foregrounds. In this paper we evaluate the amount of information about the core (non-spurious) features that can be decoded from the representations learned by standard empirical risk minimization (ERM) and specialized group robustness training. Following recent work on Deep Feature Reweighting (DFR), we evaluate the feature representations by re-training the last layer of the model on a held-out set where the spurious correlation is broken. On multiple vision and NLP problems, we show that the features learned by simple ERM are highly competitive with the features learned by specialized group robustness methods targeted at reducing the effect of spurious correlations. Moreover, we show that the quality of learned feature representations is greatly affected by the design decisions beyond the training method, such as the model architecture and pre-training strategy. On the other hand, we find that strong regularization is not necessary for learning high-quality feature representations. Finally, using insights from our analysis, we significantly improve upon the best results reported in the literature on the popular Waterbirds, CelebA hair color prediction and WILDS-FMOW problems, achieving 97\%, 92\% and 50\% worst-group accuracies, respectively.},
	language = {en},
	author = {Izmailov, Pavel and Kirichenko, Polina and Gruver, Nate and Wilson, Andrew Gordon},
}

@article{rossman_gef_2005,
	title = {{GEF} means go: turning on {RHO} {GTPases} with guanine nucleotide-exchange factors},
	volume = {6},
	issn = {1471-0072},
	shorttitle = {{GEF} means go},
	doi = {10.1038/nrm1587},
	abstract = {Guanine nucleotide-exchange factors (GEFs) are directly responsible for the activation of Rho-family GTPases in response to diverse extracellular stimuli, and ultimately regulate numerous cellular responses such as proliferation, differentiation and movement. With 69 distinct homologues, Dbl-related GEFs represent the largest family of direct activators of Rho GTPases in humans, and they activate Rho GTPases within particular spatio-temporal contexts. The failure to do so can have significant consequences and is reflected in the aberrant function of Dbl-family GEFs in some human diseases.},
	language = {eng},
	number = {2},
	journal = {Nature Reviews. Molecular Cell Biology},
	author = {Rossman, Kent L. and Der, Channing J. and Sondek, John},
	month = feb,
	year = {2005},
	pmid = {15688002},
	keywords = {Allosteric Site, Animals, Guanine Nucleotide Exchange Factors, Humans, Lipid Bilayers, Mice, Models, Biological, Models, Molecular, Neurons, Phylogeny, Protein Binding, Protein Structure, Tertiary, Signal Transduction, ras-GRF1, rho GTP-Binding Proteins},
	pages = {167--180},
}

@article{glen_agent-based_2019,
	title = {Agent-based modeling of morphogenetic systems: {Advantages} and challenges},
	volume = {15},
	issn = {1553-7358},
	shorttitle = {Agent-based modeling of morphogenetic systems},
	url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006577},
	doi = {10.1371/journal.pcbi.1006577},
	abstract = {The complexity of morphogenesis poses a fundamental challenge to understanding the mechanisms governing the formation of biological patterns and structures. Over the past century, numerous processes have been identified as critically contributing to morphogenetic events, but the interplay between the various components and aspects of pattern formation have been much harder to grasp. The combination of traditional biology with mathematical and computational methods has had a profound effect on our current understanding of morphogenesis and led to significant insights and advancements in the field. In particular, the theoretical concepts of reaction–diffusion systems and positional information, proposed by Alan Turing and Lewis Wolpert, respectively, dramatically influenced our general view of morphogenesis, although typically in isolation from one another. In recent years, agent-based modeling has been emerging as a consolidation and implementation of the two theories within a single framework. Agent-based models (ABMs) are unique in their ability to integrate combinations of heterogeneous processes and investigate their respective dynamics, especially in the context of spatial phenomena. In this review, we highlight the benefits and technical challenges associated with ABMs as tools for examining morphogenetic events. These models display unparalleled flexibility for studying various morphogenetic phenomena at multiple levels and have the important advantage of informing future experimental work, including the targeted engineering of tissues and organs.},
	language = {en},
	number = {3},
	urldate = {2023-10-28},
	journal = {PLOS Computational Biology},
	author = {Glen, Chad M. and Kemp, Melissa L. and Voit, Eberhard O.},
	month = mar,
	year = {2019},
	note = {Publisher: Public Library of Science},
	keywords = {Agent-based modeling, Apoptosis, Cell cycle and cell division, Cell differentiation, Cell migration, Morphogenesis, Morphogens, Pluripotency},
	pages = {e1006577},
}

@article{meister_locally_2023,
	title = {Locally {Typical} {Sampling}},
	volume = {11},
	issn = {2307-387X},
	url = {https://doi.org/10.1162/tacl_a_00536},
	doi = {10.1162/tacl_a_00536},
	abstract = {Today’s probabilistic language generators fall short when it comes to producing coherent and fluent text despite the fact that the underlying models perform well under standard metrics (e.g., perplexity). This discrepancy has puzzled the language generation community for the last few years. In this work, we posit that the abstraction of natural language generation as a discrete stochastic process—which allows for an information-theoretic analysis—can provide new insights into the behavior of probabilistic language generators, for example, why high-probability texts can be dull or repetitive. Humans use language as a means of communicating information, aiming to do so in a simultaneously efficient and error-minimizing manner; in fact, psycholinguistics research suggests humans choose each word in a string with this subconscious goal in mind. We formally define the set of strings that meet this criterion: Those for which each word has an information content close to the expected information content, namely, the conditional entropy of our model. We then propose a simple and efficient procedure for enforcing this criterion when generating from probabilistic models, which we call locally typical sampling. Automatic and human evaluations show that, in comparison to nucleus and top-k sampling, locally typical sampling offers competitive performance (in both abstractive summarization and story generation) in terms of quality while consistently reducing degenerate repetitions.},
	urldate = {2023-10-28},
	journal = {Transactions of the Association for Computational Linguistics},
	author = {Meister, Clara and Pimentel, Tiago and Wiher, Gian and Cotterell, Ryan},
	month = jan,
	year = {2023},
	pages = {102--121},
}

@misc{bertsch_its_2023,
	title = {It's {MBR} {All} the {Way} {Down}: {Modern} {Generation} {Techniques} {Through} the {Lens} of {Minimum} {Bayes} {Risk}},
	shorttitle = {It's {MBR} {All} the {Way} {Down}},
	url = {http://arxiv.org/abs/2310.01387},
	abstract = {Minimum Bayes Risk (MBR) decoding is a method for choosing the outputs of a machine learning system based not on the output with the highest probability, but the output with the lowest risk (expected error) among multiple candidates. It is a simple but powerful method: for an additional cost at inference time, MBR provides reliable several-point improvements across metrics for a wide variety of tasks without any additional data or training. Despite this, MBR is not frequently applied in NLP works, and knowledge of the method itself is limited. We first provide an introduction to the method and the recent literature. We show that several recent methods that do not reference MBR can be written as special cases of MBR; this reformulation provides additional theoretical justification for the performance of these methods, explaining some results that were previously only empirical. We provide theoretical and empirical results about the effectiveness of various MBR variants and make concrete recommendations for the application of MBR in NLP models, including future directions in this area.},
	urldate = {2023-10-28},
	publisher = {arXiv},
	author = {Bertsch, Amanda and Xie, Alex and Neubig, Graham and Gormley, Matthew R.},
	month = oct,
	year = {2023},
	note = {arXiv:2310.01387 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@article{foks_msc_nodate,
	title = {{MSc} {Thesis} ({Afstudeerscriptie})},
	language = {en},
	author = {Foks, Gerson},
}

@article{cheng_faster_nodate,
	title = {Faster {Minimum} {Bayes} {Risk} {Decoding} with {Confidence}-based {Pruning}},
	abstract = {Minimum Bayes risk (MBR) decoding outputs the hypothesis with the highest expected utility over the model distribution for some utility function. It has been shown to improve accuracy over beam search in conditional language generation problems and especially neural machine translation, in both human and automatic evaluations. However, the standard samplingbased algorithm for MBR is substantially more computationally expensive than beam search, requiring a large number of samples as well as a quadratic number of calls to the utility function, limiting its applicability. We describe an algorithm for MBR which gradually grows the number of samples used to estimate the utility while pruning hypotheses that are unlikely to have the highest utility according to confidence estimates obtained with bootstrap sampling. Our method requires fewer samples and drastically reduces the number of calls to the utility function compared to standard MBR while being statistically indistinguishable in terms of accuracy. We demonstrate the effectiveness of our approach in experiments on three language pairs, using chrF++ and COMET as utility/evaluation metrics.},
	language = {en},
	author = {Cheng, Julius and Vlachos, Andreas},
}

@misc{noauthor_its_nodate,
	title = {Its {Easy} for {Neural} {Networks} {To} {Learn} {Game} of {Life}},
	url = {https://kaggle.com/code/jamesmcguigan/its-easy-for-neural-networks-to-learn-game-of-life},
	abstract = {Explore and run machine learning code with Kaggle Notebooks {\textbar} Using data from No attached data sources},
	language = {en},
	urldate = {2023-10-28},
}

@misc{noauthor_learning_nodate,
	title = {learning rules to {GoL} with {CNN}},
	url = {https://danielrapp.github.io/cnn-gol/},
	urldate = {2023-10-28},
}

@misc{aach_generalization_2021,
	title = {Generalization over different cellular automata rules learned by a deep feed-forward neural network},
	url = {http://arxiv.org/abs/2103.14886},
	abstract = {To test generalization ability of a class of deep neural networks, we randomly generate a large number of different rule sets for 2-D cellular automata (CA), based on John Conway's Game of Life. Using these rules, we compute several trajectories for each CA instance. A deep convolutional encoder-decoder network with short and long range skip connections is trained on various generated CA trajectories to predict the next CA state given its previous states. Results show that the network is able to learn the rules of various, complex cellular automata and generalize to unseen configurations. To some extent, the network shows generalization to rule sets and neighborhood sizes that were not seen during the training at all. Code to reproduce the experiments is publicly available at: https://github.com/SLAMPAI/generalization-cellular-automata},
	urldate = {2023-10-28},
	publisher = {arXiv},
	author = {Aach, Marcel and Goebbert, Jens Henrik and Jitsev, Jenia},
	month = nov,
	year = {2021},
	note = {arXiv:2103.14886 [nlin]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Nonlinear Sciences - Adaptation and Self-Organizing Systems},
}

@misc{springer_its_2020,
	title = {It's {Hard} for {Neural} {Networks} {To} {Learn} the {Game} of {Life}},
	url = {http://arxiv.org/abs/2009.01398},
	doi = {10.48550/arXiv.2009.01398},
	abstract = {Efforts to improve the learning abilities of neural networks have focused mostly on the role of optimization methods rather than on weight initializations. Recent findings, however, suggest that neural networks rely on lucky random initial weights of subnetworks called "lottery tickets" that converge quickly to a solution. To investigate how weight initializations affect performance, we examine small convolutional networks that are trained to predict n steps of the two-dimensional cellular automaton Conway's Game of Life, the update rules of which can be implemented efficiently in a 2n+1 layer convolutional network. We find that networks of this architecture trained on this task rarely converge. Rather, networks require substantially more parameters to consistently converge. In addition, near-minimal architectures are sensitive to tiny changes in parameters: changing the sign of a single weight can cause the network to fail to learn. Finally, we observe a critical value d\_0 such that training minimal networks with examples in which cells are alive with probability d\_0 dramatically increases the chance of convergence to a solution. We conclude that training convolutional neural networks to learn the input/output function represented by n steps of Game of Life exhibits many characteristics predicted by the lottery ticket hypothesis, namely, that the size of the networks required to learn this function are often significantly larger than the minimal network required to implement the function.},
	urldate = {2023-10-28},
	publisher = {arXiv},
	author = {Springer, Jacob M. and Kenyon, Garrett T.},
	month = sep,
	year = {2020},
	note = {arXiv:2009.01398 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{hinton_distilling_2015,
	title = {Distilling the {Knowledge} in a {Neural} {Network}},
	url = {http://arxiv.org/abs/1503.02531},
	doi = {10.48550/arXiv.1503.02531},
	abstract = {A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel.},
	urldate = {2023-10-24},
	publisher = {arXiv},
	author = {Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff},
	month = mar,
	year = {2015},
	note = {arXiv:1503.02531 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Statistics - Machine Learning},
}

@inproceedings{wang_adversarial_2021,
	title = {Adversarial {GLUE}: {A} {Multi}-{Task} {Benchmark} for {Robustness} {Evaluation} of {Language} {Models}},
	shorttitle = {Adversarial {GLUE}},
	url = {https://openreview.net/forum?id=GF9cSKI3A_q},
	abstract = {Large-scale pre-trained language models have achieved tremendous success across a wide range of natural language understanding (NLU) tasks, even surpassing human performance. However, recent studies reveal that the robustness of these models can be challenged by carefully crafted textual adversarial examples. While several individual datasets have been proposed to evaluate model robustness, a principled and comprehensive benchmark is still missing. In this paper, we present Adversarial GLUE (AdvGLUE), a new multi-task benchmark to quantitatively and thoroughly explore and evaluate the vulnerabilities of modern large-scale language models under various types of adversarial attacks. In particular, we systematically apply 14 textual adversarial attack methods to GLUE tasks to construct AdvGLUE, which is further validated by humans for reliable annotations. Our ﬁndings are summarized as follows. (i) Most existing adversarial attack algorithms are prone to generating invalid or ambiguous adversarial examples, with around 90\% of them either changing the original semantic meanings or misleading human annotators as well. Therefore, we perform a careful ﬁltering process to curate a high-quality benchmark. (ii) All the language models and robust training methods we tested perform poorly on AdvGLUE, with scores lagging far behind the benign accuracy. We hope our work will motivate the development of new adversarial attacks that are more stealthy and semantic-preserving, as well as new robust language models against sophisticated adversarial attacks. AdvGLUE is available at https://adversarialglue.github.io.},
	language = {en},
	urldate = {2023-10-23},
	author = {Wang, Boxin and Xu, Chejian and Wang, Shuohang and Gan, Zhe and Cheng, Yu and Gao, Jianfeng and Awadallah, Ahmed Hassan and Li, Bo},
	month = aug,
	year = {2021},
}

@misc{chen_drop_2019,
	title = {Drop an {Octave}: {Reducing} {Spatial} {Redundancy} in {Convolutional} {Neural} {Networks} with {Octave} {Convolution}},
	shorttitle = {Drop an {Octave}},
	url = {http://arxiv.org/abs/1904.05049},
	doi = {10.48550/arXiv.1904.05049},
	abstract = {In natural images, information is conveyed at different frequencies where higher frequencies are usually encoded with fine details and lower frequencies are usually encoded with global structures. Similarly, the output feature maps of a convolution layer can also be seen as a mixture of information at different frequencies. In this work, we propose to factorize the mixed feature maps by their frequencies, and design a novel Octave Convolution (OctConv) operation to store and process feature maps that vary spatially "slower" at a lower spatial resolution reducing both memory and computation cost. Unlike existing multi-scale methods, OctConv is formulated as a single, generic, plug-and-play convolutional unit that can be used as a direct replacement of (vanilla) convolutions without any adjustments in the network architecture. It is also orthogonal and complementary to methods that suggest better topologies or reduce channel-wise redundancy like group or depth-wise convolutions. We experimentally show that by simply replacing convolutions with OctConv, we can consistently boost accuracy for both image and video recognition tasks, while reducing memory and computational cost. An OctConv-equipped ResNet-152 can achieve 82.9\% top-1 classification accuracy on ImageNet with merely 22.2 GFLOPs.},
	urldate = {2023-10-22},
	publisher = {arXiv},
	author = {Chen, Yunpeng and Fan, Haoqi and Xu, Bing and Yan, Zhicheng and Kalantidis, Yannis and Rohrbach, Marcus and Yan, Shuicheng and Feng, Jiashi},
	month = aug,
	year = {2019},
	note = {arXiv:1904.05049 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@article{purwins_deep_2019,
	title = {Deep {Learning} for {Audio} {Signal} {Processing}},
	volume = {13},
	issn = {1932-4553, 1941-0484},
	url = {http://arxiv.org/abs/1905.00078},
	doi = {10.1109/JSTSP.2019.2908700},
	abstract = {Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.},
	number = {2},
	urldate = {2023-10-20},
	journal = {IEEE Journal of Selected Topics in Signal Processing},
	author = {Purwins, Hendrik and Li, Bo and Virtanen, Tuomas and Schlüter, Jan and Chang, Shuo-yiin and Sainath, Tara},
	month = may,
	year = {2019},
	note = {arXiv:1905.00078 [cs, eess, stat]},
	keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, H.5.1, I.2.6, Statistics - Machine Learning},
	pages = {206--219},
}

@article{purwins_deep_2019-1,
	title = {Deep {Learning} for {Audio} {Signal} {Processing}},
	volume = {13},
	issn = {1932-4553, 1941-0484},
	url = {http://arxiv.org/abs/1905.00078},
	doi = {10.1109/JSTSP.2019.2908700},
	abstract = {Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.},
	number = {2},
	urldate = {2023-10-20},
	journal = {IEEE Journal of Selected Topics in Signal Processing},
	author = {Purwins, Hendrik and Li, Bo and Virtanen, Tuomas and Schlüter, Jan and Chang, Shuo-yiin and Sainath, Tara},
	month = may,
	year = {2019},
	note = {arXiv:1905.00078 [cs, eess, stat]},
	keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, H.5.1, I.2.6, Statistics - Machine Learning},
	pages = {206--219},
}

@misc{springer_its_2020-1,
	title = {It's {Hard} for {Neural} {Networks} {To} {Learn} the {Game} of {Life}},
	url = {http://arxiv.org/abs/2009.01398},
	doi = {10.48550/arXiv.2009.01398},
	abstract = {Efforts to improve the learning abilities of neural networks have focused mostly on the role of optimization methods rather than on weight initializations. Recent findings, however, suggest that neural networks rely on lucky random initial weights of subnetworks called "lottery tickets" that converge quickly to a solution. To investigate how weight initializations affect performance, we examine small convolutional networks that are trained to predict n steps of the two-dimensional cellular automaton Conway's Game of Life, the update rules of which can be implemented efficiently in a 2n+1 layer convolutional network. We find that networks of this architecture trained on this task rarely converge. Rather, networks require substantially more parameters to consistently converge. In addition, near-minimal architectures are sensitive to tiny changes in parameters: changing the sign of a single weight can cause the network to fail to learn. Finally, we observe a critical value d\_0 such that training minimal networks with examples in which cells are alive with probability d\_0 dramatically increases the chance of convergence to a solution. We conclude that training convolutional neural networks to learn the input/output function represented by n steps of Game of Life exhibits many characteristics predicted by the lottery ticket hypothesis, namely, that the size of the networks required to learn this function are often significantly larger than the minimal network required to implement the function.},
	urldate = {2023-10-19},
	publisher = {arXiv},
	author = {Springer, Jacob M. and Kenyon, Garrett T.},
	month = sep,
	year = {2020},
	note = {arXiv:2009.01398 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@article{vinas_hypergraph_2023,
	title = {Hypergraph factorization for multi-tissue gene expression imputation},
	volume = {5},
	copyright = {2023 The Author(s)},
	issn = {2522-5839},
	url = {https://www.nature.com/articles/s42256-023-00684-8},
	doi = {10.1038/s42256-023-00684-8},
	abstract = {Integrating gene expression across tissues and cell types is crucial for understanding the coordinated biological mechanisms that drive disease and characterize homoeostasis. However, traditional multi-tissue integration methods either cannot handle uncollected tissues or rely on genotype information, which is often unavailable and subject to privacy concerns. Here we present HYFA (hypergraph factorization), a parameter-efficient graph representation learning approach for joint imputation of multi-tissue and cell-type gene expression. HYFA is genotype agnostic, supports a variable number of collected tissues per individual, and imposes strong inductive biases to leverage the shared regulatory architecture of tissues and genes. In performance comparison on Genotype–Tissue Expression project data, HYFA achieves superior performance over existing methods, especially when multiple reference tissues are available. The HYFA-imputed dataset can be used to identify replicable regulatory genetic variations (expression quantitative trait loci), with substantial gains over the original incomplete dataset. HYFA can accelerate the effective and scalable integration of tissue and cell-type transcriptome biorepositories.},
	language = {en},
	number = {7},
	urldate = {2023-10-19},
	journal = {Nature Machine Intelligence},
	author = {Viñas, Ramon and Joshi, Chaitanya K. and Georgiev, Dobrik and Lin, Phillip and Dumitrascu, Bianca and Gamazon, Eric R. and Liò, Pietro},
	month = jul,
	year = {2023},
	note = {Number: 7
Publisher: Nature Publishing Group},
	keywords = {Machine learning, Transcriptomics},
	pages = {739--753},
}

@article{vlachos_stopping_2008-1,
	title = {A stopping criterion for active learning},
	volume = {22},
	issn = {0885-2308},
	url = {https://www.sciencedirect.com/science/article/pii/S088523080700068X},
	doi = {10.1016/j.csl.2007.12.001},
	abstract = {Active learning (AL) is a framework that attempts to reduce the cost of annotating training material for statistical learning methods. While a lot of papers have been presented on applying AL to natural language processing tasks reporting impressive savings, little work has been done on defining a stopping criterion. In this work, we present a stopping criterion for active learning based on the way instances are selected during uncertainty-based sampling and verify its applicability in a variety of settings. The statistical learning models used in our study are support vector machines (SVMs), maximum entropy models and Bayesian logistic regression and the tasks performed are text classification, named entity recognition and shallow parsing. In addition, we present a method for multiclass mutually exclusive SVM active learning.},
	number = {3},
	urldate = {2023-10-17},
	journal = {Computer Speech \& Language},
	author = {Vlachos, Andreas},
	month = jul,
	year = {2008},
	keywords = {Active learning, NER, SVMs, Text classification},
	pages = {295--312},
}

@misc{noauthor_andreas_nodate,
	title = {Andreas {Vlachos}},
	url = {https://andreasvlachos.github.io/mphil_projects2023-2024/},
	urldate = {2023-10-17},
}

@article{freitag_high_2022,
	title = {High {Quality} {Rather} than {High} {Model} {Probability}: {Minimum} {Bayes} {Risk} {Decoding} with {Neural} {Metrics}},
	volume = {10},
	shorttitle = {High {Quality} {Rather} than {High} {Model} {Probability}},
	url = {https://aclanthology.org/2022.tacl-1.47},
	doi = {10.1162/tacl_a_00491},
	abstract = {In Neural Machine Translation, it is typically assumed that the sentence with the highest estimated probability should also be the translation with the highest quality as measured by humans. In this work, we question this assumption and show that model estimates and translation quality only vaguely correlate. We apply Minimum Bayes Risk (MBR) decoding on unbiased samples to optimize diverse automated metrics of translation quality as an alternative inference strategy to beam search. Instead of targeting the hypotheses with the highest model probability, MBR decoding extracts the hypotheses with the highest estimated quality. Our experiments show that the combination of a neural translation model with a neural reference-based metric, Bleurt, results in significant improvement in human evaluations. This improvement is obtained with translations different from classical beam-search output: These translations have much lower model likelihood and are less favored by surface metrics like Bleu.},
	urldate = {2023-10-17},
	journal = {Transactions of the Association for Computational Linguistics},
	author = {Freitag, Markus and Grangier, David and Tan, Qijun and Liang, Bowen},
	year = {2022},
	note = {Place: Cambridge, MA
Publisher: MIT Press},
	pages = {811--825},
}

@inproceedings{kumar_minimum_2004,
	address = {Boston, Massachusetts, USA},
	title = {Minimum {Bayes}-{Risk} {Decoding} for {Statistical} {Machine} {Translation}},
	url = {https://aclanthology.org/N04-1022},
	urldate = {2023-10-17},
	booktitle = {Proceedings of the {Human} {Language} {Technology} {Conference} of the {North} {American} {Chapter} of the {Association} for {Computational} {Linguistics}: {HLT}-{NAACL} 2004},
	publisher = {Association for Computational Linguistics},
	author = {Kumar, Shankar and Byrne, William},
	month = may,
	year = {2004},
	pages = {169--176},
}

@inproceedings{hudcova_computational_2021,
	title = {Computational {Hierarchy} of {Elementary} {Cellular} {Automata}},
	url = {https://dx.doi.org/10.1162/isal_a_00447},
	doi = {10.1162/isal_a_00447},
	abstract = {Abstract. The complexity of cellular automata is traditionally measured by their computational capacity. However, it is difficult to choose a challenging set of computational tasks suitable for the parallel nature of such systems. We study the ability of automata to emulate one another, and we use this notion to define such a set of naturally emerging tasks. We present the results for elementary cellular automata, although the core ideas can be extended to other computational systems. We compute a graph showing which elementary cellular automata can be emulated by which and show that certain chaotic automata are the only ones that cannot emulate any automata non-trivially. Finally, we use the emulation notion to suggest a novel definition of chaos that we believe is suitable for discrete computational systems. We believe our work can help design parallel computational systems that are Turing-complete and also computationally efficient.},
	language = {en},
	urldate = {2023-10-17},
	publisher = {MIT Press},
	author = {Hudcová, Barbora and Mikolov, Tomáš},
	month = jul,
	year = {2021},
}

@inproceedings{joulin_bag_2017,
	address = {Valencia, Spain},
	title = {Bag of {Tricks} for {Efficient} {Text} {Classification}},
	url = {https://aclanthology.org/E17-2068},
	abstract = {This paper explores a simple and efficient baseline for text classification. Our experiments show that our fast text classifier fastText is often on par with deep learning classifiers in terms of accuracy, and many orders of magnitude faster for training and evaluation. We can train fastText on more than one billion words in less than ten minutes using a standard multicore CPU, and classify half a million sentences among 312K classes in less than a minute.},
	urldate = {2023-10-17},
	booktitle = {Proceedings of the 15th {Conference} of the {European} {Chapter} of the {Association} for {Computational} {Linguistics}: {Volume} 2, {Short} {Papers}},
	publisher = {Association for Computational Linguistics},
	author = {Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
	month = apr,
	year = {2017},
	pages = {427--431},
}

@misc{narang_transformer_2021,
	title = {Do {Transformer} {Modifications} {Transfer} {Across} {Implementations} and {Applications}?},
	url = {http://arxiv.org/abs/2102.11972},
	doi = {10.48550/arXiv.2102.11972},
	abstract = {The research community has proposed copious modifications to the Transformer architecture since it was introduced over three years ago, relatively few of which have seen widespread adoption. In this paper, we comprehensively evaluate many of these modifications in a shared experimental setting that covers most of the common uses of the Transformer in natural language processing. Surprisingly, we find that most modifications do not meaningfully improve performance. Furthermore, most of the Transformer variants we found beneficial were either developed in the same codebase that we used or are relatively minor changes. We conjecture that performance improvements may strongly depend on implementation details and correspondingly make some recommendations for improving the generality of experimental results.},
	urldate = {2023-10-17},
	publisher = {arXiv},
	author = {Narang, Sharan and Chung, Hyung Won and Tay, Yi and Fedus, William and Fevry, Thibault and Matena, Michael and Malkan, Karishma and Fiedel, Noah and Shazeer, Noam and Lan, Zhenzhong and Zhou, Yanqi and Li, Wei and Ding, Nan and Marcus, Jake and Roberts, Adam and Raffel, Colin},
	month = sep,
	year = {2021},
	note = {arXiv:2102.11972 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{wang_adversarial_2022,
	title = {Adversarial {GLUE}: {A} {Multi}-{Task} {Benchmark} for {Robustness} {Evaluation} of {Language} {Models}},
	shorttitle = {Adversarial {GLUE}},
	url = {http://arxiv.org/abs/2111.02840},
	doi = {10.48550/arXiv.2111.02840},
	abstract = {Large-scale pre-trained language models have achieved tremendous success across a wide range of natural language understanding (NLU) tasks, even surpassing human performance. However, recent studies reveal that the robustness of these models can be challenged by carefully crafted textual adversarial examples. While several individual datasets have been proposed to evaluate model robustness, a principled and comprehensive benchmark is still missing. In this paper, we present Adversarial GLUE (AdvGLUE), a new multi-task benchmark to quantitatively and thoroughly explore and evaluate the vulnerabilities of modern large-scale language models under various types of adversarial attacks. In particular, we systematically apply 14 textual adversarial attack methods to GLUE tasks to construct AdvGLUE, which is further validated by humans for reliable annotations. Our findings are summarized as follows. (i) Most existing adversarial attack algorithms are prone to generating invalid or ambiguous adversarial examples, with around 90\% of them either changing the original semantic meanings or misleading human annotators as well. Therefore, we perform a careful filtering process to curate a high-quality benchmark. (ii) All the language models and robust training methods we tested perform poorly on AdvGLUE, with scores lagging far behind the benign accuracy. We hope our work will motivate the development of new adversarial attacks that are more stealthy and semantic-preserving, as well as new robust language models against sophisticated adversarial attacks. AdvGLUE is available at https://adversarialglue.github.io.},
	urldate = {2023-10-17},
	publisher = {arXiv},
	author = {Wang, Boxin and Xu, Chejian and Wang, Shuohang and Gan, Zhe and Cheng, Yu and Gao, Jianfeng and Awadallah, Ahmed Hassan and Li, Bo},
	month = jan,
	year = {2022},
	note = {arXiv:2111.02840 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Cryptography and Security, Computer Science - Machine Learning},
}

@article{izmailov_feature_nodate-1,
	title = {On {Feature} {Learning} in the {Presence} of {Spurious} {Correlations}},
	abstract = {Deep classifiers are known to rely on spurious features — patterns which are correlated with the target on the training data but not inherently relevant to the learning problem, such as the image backgrounds when classifying the foregrounds. In this paper we evaluate the amount of information about the core (non-spurious) features that can be decoded from the representations learned by standard empirical risk minimization (ERM) and specialized group robustness training. Following recent work on Deep Feature Reweighting (DFR), we evaluate the feature representations by re-training the last layer of the model on a held-out set where the spurious correlation is broken. On multiple vision and NLP problems, we show that the features learned by simple ERM are highly competitive with the features learned by specialized group robustness methods targeted at reducing the effect of spurious correlations. Moreover, we show that the quality of learned feature representations is greatly affected by the design decisions beyond the training method, such as the model architecture and pre-training strategy. On the other hand, we find that strong regularization is not necessary for learning high-quality feature representations. Finally, using insights from our analysis, we significantly improve upon the best results reported in the literature on the popular Waterbirds, CelebA hair color prediction and WILDS-FMOW problems, achieving 97\%, 92\% and 50\% worst-group accuracies, respectively.},
	language = {en},
	author = {Izmailov, Pavel and Kirichenko, Polina and Gruver, Nate and Wilson, Andrew Gordon},
}

@article{geirhos_shortcut_2020,
	title = {Shortcut learning in deep neural networks},
	volume = {2},
	copyright = {2020 Springer Nature Limited},
	issn = {2522-5839},
	url = {https://www.nature.com/articles/s42256-020-00257-z},
	doi = {10.1038/s42256-020-00257-z},
	abstract = {Deep learning has triggered the current rise of artificial intelligence and is the workhorse of today’s machine intelligence. Numerous success stories have rapidly spread all over science, industry and society, but its limitations have only recently come into focus. In this Perspective we seek to distil how many of deep learning’s failures can be seen as different symptoms of the same underlying problem: shortcut learning. Shortcuts are decision rules that perform well on standard benchmarks but fail to transfer to more challenging testing conditions, such as real-world scenarios. Related issues are known in comparative psychology, education and linguistics, suggesting that shortcut learning may be a common characteristic of learning systems, biological and artificial alike. Based on these observations, we develop a set of recommendations for model interpretation and benchmarking, highlighting recent advances in machine learning to improve robustness and transferability from the lab to real-world applications.},
	language = {en},
	number = {11},
	urldate = {2023-10-16},
	journal = {Nature Machine Intelligence},
	author = {Geirhos, Robert and Jacobsen, Jörn-Henrik and Michaelis, Claudio and Zemel, Richard and Brendel, Wieland and Bethge, Matthias and Wichmann, Felix A.},
	month = nov,
	year = {2020},
	note = {Number: 11
Publisher: Nature Publishing Group},
	keywords = {Computational science, Human behaviour, Information technology, Network models},
	pages = {665--673},
}

@article{tan_neural_2020,
	title = {Neural machine translation: {A} review of methods, resources, and tools},
	volume = {1},
	issn = {2666-6510},
	shorttitle = {Neural machine translation},
	url = {https://www.sciencedirect.com/science/article/pii/S2666651020300024},
	doi = {10.1016/j.aiopen.2020.11.001},
	abstract = {Machine translation (MT) is an important sub-field of natural language processing that aims to translate natural languages using computers. In recent years, end-to-end neural machine translation (NMT) has achieved great success and has become the new mainstream method in practical MT systems. In this article, we first provide a broad review of the methods for NMT and focus on methods relating to architectures, decoding, and data augmentation. Then we summarize the resources and tools that are useful for researchers. Finally, we conclude with a discussion of possible future research directions.},
	urldate = {2023-10-16},
	journal = {AI Open},
	author = {Tan, Zhixing and Wang, Shuo and Yang, Zonghan and Chen, Gang and Huang, Xuancheng and Sun, Maosong and Liu, Yang},
	month = jan,
	year = {2020},
	keywords = {Attention mechanism, Deep learning, Natural language processing, neural Machine translation},
	pages = {5--21},
}

@article{sennrich_machine_2018,
	title = {Machine {Translation} - 14: {Advanced} {Decoding} {Techniques}},
	language = {en},
	author = {Sennrich, Rico},
	year = {2018},
}

@misc{noauthor_introducing_2023,
	title = {Introducing {Code} {Llama}, an {AI} {Tool} for {Coding}},
	url = {https://about.fb.com/news/2023/08/code-llama-ai-for-coding/},
	abstract = {We're releasing Code Llama, an AI model built on top of Llama 2 that's fine-tuned for generating and discussing code.},
	language = {en-US},
	urldate = {2023-10-16},
	journal = {Meta},
	month = aug,
	year = {2023},
}

@misc{noauthor_pytorch_nodate,
	title = {{PyTorch}},
	url = {https://www.pytorch.org},
	abstract = {An open source machine learning framework that accelerates the path from research prototyping to production deployment.},
	language = {en},
	urldate = {2023-10-16},
}

@misc{santhosh_understanding_2023,
	title = {Understanding {BLEU} and {ROUGE} score for {NLP} evaluation},
	url = {https://medium.com/@sthanikamsanthosh1994/understanding-bleu-and-rouge-score-for-nlp-evaluation-1ab334ecadcb},
	abstract = {As natural language processing (NLP) continues to advance, the need for evaluating NLP models becomes increasingly important.},
	language = {en},
	urldate = {2023-10-16},
	journal = {Medium},
	author = {Santhosh, Sthanikam},
	month = apr,
	year = {2023},
}

@misc{margeloiu_weight_2022,
	title = {Weight {Predictor} {Network} with {Feature} {Selection} for {Small} {Sample} {Tabular} {Biomedical} {Data}},
	url = {http://arxiv.org/abs/2211.15616},
	doi = {10.48550/arXiv.2211.15616},
	abstract = {Tabular biomedical data is often high-dimensional but with a very small number of samples. Although recent work showed that well-regularised simple neural networks could outperform more sophisticated architectures on tabular data, they are still prone to overfitting on tiny datasets with many potentially irrelevant features. To combat these issues, we propose Weight Predictor Network with Feature Selection (WPFS) for learning neural networks from high-dimensional and small sample data by reducing the number of learnable parameters and simultaneously performing feature selection. In addition to the classification network, WPFS uses two small auxiliary networks that together output the weights of the first layer of the classification model. We evaluate on nine real-world biomedical datasets and demonstrate that WPFS outperforms other standard as well as more recent methods typically applied to tabular data. Furthermore, we investigate the proposed feature selection mechanism and show that it improves performance while providing useful insights into the learning task.},
	urldate = {2023-10-16},
	publisher = {arXiv},
	author = {Margeloiu, Andrei and Simidjievski, Nikola and Lio, Pietro and Jamnik, Mateja},
	month = nov,
	year = {2022},
	note = {arXiv:2211.15616 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
}

@article{klein_lagrange_nodate,
	title = {Lagrange {Multipliers} without {Permanent} {Scarring}},
	language = {en},
	author = {Klein, Dan},
}

@misc{schulman_trust_2017,
	title = {Trust {Region} {Policy} {Optimization}},
	url = {http://arxiv.org/abs/1502.05477},
	doi = {10.48550/arXiv.1502.05477},
	abstract = {We describe an iterative procedure for optimizing policies, with guaranteed monotonic improvement. By making several approximations to the theoretically-justified procedure, we develop a practical algorithm, called Trust Region Policy Optimization (TRPO). This algorithm is similar to natural policy gradient methods and is effective for optimizing large nonlinear policies such as neural networks. Our experiments demonstrate its robust performance on a wide variety of tasks: learning simulated robotic swimming, hopping, and walking gaits; and playing Atari games using images of the screen as input. Despite its approximations that deviate from the theory, TRPO tends to give monotonic improvement, with little tuning of hyperparameters.},
	urldate = {2023-10-15},
	publisher = {arXiv},
	author = {Schulman, John and Levine, Sergey and Moritz, Philipp and Jordan, Michael I. and Abbeel, Pieter},
	month = apr,
	year = {2017},
	note = {arXiv:1502.05477 [cs]},
	keywords = {Computer Science - Machine Learning},
}

@article{noauthor_03_optimization_fundamentals_nodate,
	title = {03\_Optimization\_fundamentals},
	language = {en},
}

@article{schapire_cos_nodate,
	title = {{COS} 511: {Theoretical} {Machine} {Learning}},
	language = {en},
	author = {Schapire, Rob and Marsh, Charles},
}

@inproceedings{ng_discriminative_2001,
	title = {On {Discriminative} vs. {Generative} {Classifiers}: {A} comparison of logistic regression and naive {Bayes}},
	volume = {14},
	shorttitle = {On {Discriminative} vs. {Generative} {Classifiers}},
	url = {https://proceedings.neurips.cc/paper_files/paper/2001/hash/7b7a53e239400a13bd6be6c91c4f6c4e-Abstract.html},
	abstract = {We  compare discriminative  and  generative learning as  typified  by  logistic regression and naive Bayes.  We show,  contrary to a widely(cid:173) held  belief  that  discriminative  classifiers  are  almost  always  to  be  preferred,  that  there  can  often  be  two  distinct  regimes  of  per(cid:173) formance  as  the  training  set  size  is  increased,  one  in  which  each  algorithm  does  better.  This  stems  from  the  observation- which  is  borne  out  in  repeated  experiments- that  while  discriminative  learning has lower asymptotic error, a generative classifier may also  approach its  (higher)  asymptotic error  much faster.},
	urldate = {2023-10-15},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {MIT Press},
	author = {Ng, Andrew and Jordan, Michael},
	year = {2001},
}

@misc{kwon_fast_2022,
	title = {A {Fast} {Post}-{Training} {Pruning} {Framework} for {Transformers}},
	url = {http://arxiv.org/abs/2204.09656},
	abstract = {Pruning is an effective way to reduce the huge inference cost of Transformer models. However, prior work on pruning Transformers requires retraining the models. This can add high training cost and high complexity to model deployment, making it difficult to use in many practical situations. To address this, we propose a fast post-training pruning framework for Transformers that does not require any retraining. Given a resource constraint and a sample dataset, our framework automatically prunes the Transformer model using structured sparsity methods. To retain high accuracy without retraining, we introduce three novel techniques: (i) a lightweight mask search algorithm that finds which heads and filters to prune based on the Fisher information; (ii) mask rearrangement that complements the search algorithm; and (iii) mask tuning that reconstructs the output activations for each layer. We apply our method to BERT-base and DistilBERT, and we evaluate its effectiveness on GLUE and SQuAD benchmarks. Our framework achieves up to 2.0x reduction in FLOPs and 1.56x speedup in inference latency, while maintaining {\textless} 1\% loss in accuracy. Importantly, our framework prunes Transformers in less than 3 minutes on a single GPU, which is over two orders of magnitude faster than existing pruning approaches that retrain the models.},
	urldate = {2023-10-14},
	publisher = {arXiv},
	author = {Kwon, Woosuk and Kim, Sehoon and Mahoney, Michael W. and Hassoun, Joseph and Keutzer, Kurt and Gholami, Amir},
	month = oct,
	year = {2022},
	note = {arXiv:2204.09656 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@article{nguyen_code-switching_2023,
	title = {Code-switching input for machine translation: a case study of {Vietnamese}–{English} data},
	volume = {0},
	issn = {1479-0718},
	shorttitle = {Code-switching input for machine translation},
	url = {https://doi.org/10.1080/14790718.2023.2224013},
	doi = {10.1080/14790718.2023.2224013},
	abstract = {Multilingualism presents both a challenge and an opportunity for Natural Language Processing, with code-switching representing a particularly interesting problem for computational models trained on monolingual datasets. In this paper, we explore how code-switched data affects the task of Machine Translation, a task which only recently has started to tackle the challenge of multilingual data. We test three Machine Translation systems on data from the Canberra Vietnamese–English Codeswitching Natural Speech Corpus (CanVEC) and evaluate translation output using both automatic and human metrics. We find that, perhaps counter-intuitively, Machine Translation performs better on code-switching input than monolingual input. In particular, comparison of human and automatic evaluation suggests that codeswitching input may boost the semantic faithfulness of the translation output, an effect we term lexico-semantic enrichment. We also report two cases where this effect is most and least clear in Vietnamese–English, namely gender-neutral 3SG pronouns and interrogative constructions respectively. Overall, we suggest that Machine Translation, and Natural Language Processing more generally, ought to view multilingualism as an opportunity rather than an obstacle. Abbreviations: 1: First person; 2: Second person; 3: Third person; CLF: Classifier; COP: Copula; DET: Determiner; PL: Plural; POSS: Possessive marker; PRT: Particle; PST: Past tense; Q: Question marker; SG: Singular},
	number = {0},
	urldate = {2023-10-13},
	journal = {International Journal of Multilingualism},
	author = {Nguyen, Li and Mayeux, Oliver and Yuan, Zheng},
	year = {2023},
	note = {Publisher: Routledge
\_eprint: https://doi.org/10.1080/14790718.2023.2224013},
	keywords = {Machine translation, Vietnamese, automatic evaluation, code-switching, human evaluation, lexico-semantic enrichment},
	pages = {1--22},
}

@inproceedings{kang_improved_2020,
	address = {Online},
	title = {Improved {Natural} {Language} {Generation} via {Loss} {Truncation}},
	url = {https://aclanthology.org/2020.acl-main.66},
	doi = {10.18653/v1/2020.acl-main.66},
	abstract = {Neural language models are usually trained to match the distributional properties of large-scale corpora by minimizing the log loss. While straightforward to optimize, this approach forces the model to reproduce all variations in the dataset, including noisy and invalid references (e.g., misannotations and hallucinated facts). Even a small fraction of noisy data can degrade the performance of log loss. As an alternative, prior work has shown that minimizing the distinguishability of generated samples is a principled and robust loss that can handle invalid references. However, distinguishability has not been used in practice due to challenges in optimization and estimation. We propose loss truncation: a simple and scalable procedure which adaptively removes high log loss examples as a way to optimize for distinguishability. Empirically, we demonstrate that loss truncation outperforms existing baselines on distinguishability on a summarization task. Furthermore, we show that samples generated by the loss truncation model have factual accuracy ratings that exceed those of baselines and match human references.},
	urldate = {2023-10-12},
	booktitle = {Proceedings of the 58th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics}},
	publisher = {Association for Computational Linguistics},
	author = {Kang, Daniel and Hashimoto, Tatsunori B.},
	month = jul,
	year = {2020},
	pages = {718--731},
}

@inproceedings{nguyen_how_2023,
	address = {Toronto, Canada},
	title = {How effective is machine translation on low-resource code-switching? {A} case study comparing human and automatic metrics},
	shorttitle = {How effective is machine translation on low-resource code-switching?},
	url = {https://aclanthology.org/2023.findings-acl.893},
	doi = {10.18653/v1/2023.findings-acl.893},
	abstract = {This paper presents an investigation into the differences between processing monolingual input and code-switching (CSW) input in the context of machine translation (MT). Specifically, we compare the performance of three MT systems (Google, mBART-50 and M2M-100-big) in terms of their ability to translate monolingual Vietnamese, a low-resource language, and Vietnamese-English CSW respectively. To our knowledge, this is the first study to systematically analyse what might happen when multilingual MT systems are exposed to CSW data using both automatic and human metrics. We find that state-of-the-art neural translation systems not only achieve higher scores on automatic metrics when processing CSW input (compared to monolingual input), but also produce translations that are consistently rated as more semantically faithful by humans. We further suggest that automatic evaluation alone is insufficient for evaluating the translation of CSW input. Our findings establish a new benchmark that offers insights into the relationship between MT and CSW.},
	urldate = {2023-10-12},
	booktitle = {Findings of the {Association} for {Computational} {Linguistics}: {ACL} 2023},
	publisher = {Association for Computational Linguistics},
	author = {Nguyen, Li and Bryant, Christopher and Mayeux, Oliver and Yuan, Zheng},
	month = jul,
	year = {2023},
	pages = {14186--14195},
}

@article{nam_systems_2014,
	title = {A {Systems} {Approach} to {Predict} {Oncometabolites} via {Context}-{Specific} {Genome}-{Scale} {Metabolic} {Networks}},
	volume = {10},
	issn = {1553-7358},
	url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003837},
	doi = {10.1371/journal.pcbi.1003837},
	abstract = {Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH), succinate dehydrogenase (SDH), and fumarate hydratase (FH) that produce oncometabolites that competitively inhibit epigenetic regulation. In this study, we demonstrate in silico predictions of oncometabolites that have the potential to dysregulate epigenetic controls in nine types of cancer by incorporating massive scale genetic mutation information (collected from more than 1,700 cancer genomes), expression profiling data, and deploying Recon 2 to reconstruct context-specific genome-scale metabolic models. Our analysis predicted 15 compounds and 24 substructures of potential oncometabolites that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes, respectively. These results suggest a substantial potential for discovering unidentified oncometabolites in various forms of cancers.},
	language = {en},
	number = {9},
	urldate = {2023-10-12},
	journal = {PLOS Computational Biology},
	author = {Nam, Hojung and Campodonico, Miguel and Bordbar, Aarash and Hyduke, Daniel R. and Kim, Sangwoo and Zielinski, Daniel C. and Palsson, Bernhard O.},
	month = sep,
	year = {2014},
	note = {Publisher: Public Library of Science},
	keywords = {Breast cancer, Cancers and neoplasms, Enzyme metabolism, Enzymes, Gastric cancer, Hepatocellular carcinoma, Lung and intrathoracic tumors, Renal cancer},
	pages = {e1003837},
}

@inproceedings{eikema_is_2020,
	address = {Barcelona, Spain (Online)},
	title = {Is {MAP} {Decoding} {All} {You} {Need}? {The} {Inadequacy} of the {Mode} in {Neural} {Machine} {Translation}},
	shorttitle = {Is {MAP} {Decoding} {All} {You} {Need}?},
	url = {https://www.aclweb.org/anthology/2020.coling-main.398},
	doi = {10.18653/v1/2020.coling-main.398},
	language = {en},
	urldate = {2023-10-10},
	booktitle = {Proceedings of the 28th {International} {Conference} on {Computational} {Linguistics}},
	publisher = {International Committee on Computational Linguistics},
	author = {Eikema, Bryan and Aziz, Wilker},
	year = {2020},
	pages = {4506--4520},
}

@article{zador_catalyzing_2023,
	title = {Catalyzing next-generation {Artificial} {Intelligence} through {NeuroAI}},
	volume = {14},
	copyright = {2023 The Author(s)},
	issn = {2041-1723},
	url = {https://www.nature.com/articles/s41467-023-37180-x},
	doi = {10.1038/s41467-023-37180-x},
	abstract = {Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities – inherited from over 500 million years of evolution – that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI.},
	language = {en},
	number = {1},
	urldate = {2023-10-10},
	journal = {Nature Communications},
	author = {Zador, Anthony and Escola, Sean and Richards, Blake and Ölveczky, Bence and Bengio, Yoshua and Boahen, Kwabena and Botvinick, Matthew and Chklovskii, Dmitri and Churchland, Anne and Clopath, Claudia and DiCarlo, James and Ganguli, Surya and Hawkins, Jeff and Körding, Konrad and Koulakov, Alexei and LeCun, Yann and Lillicrap, Timothy and Marblestone, Adam and Olshausen, Bruno and Pouget, Alexandre and Savin, Cristina and Sejnowski, Terrence and Simoncelli, Eero and Solla, Sara and Sussillo, David and Tolias, Andreas S. and Tsao, Doris},
	month = mar,
	year = {2023},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Computer science, Neuroscience},
	pages = {1597},
}

@misc{perot_lmdx_2023,
	title = {{LMDX}: {Language} {Model}-based {Document} {Information} {Extraction} and {Localization}},
	shorttitle = {{LMDX}},
	url = {https://arxiv.org/abs/2309.10952v1},
	abstract = {Large Language Models (LLM) have revolutionized Natural Language Processing (NLP), improving state-of-the-art on many existing tasks and exhibiting emergent capabilities. However, LLMs have not yet been successfully applied on semi-structured document information extraction, which is at the core of many document processing workflows and consists of extracting key entities from a visually rich document (VRD) given a predefined target schema. The main obstacles to LLM adoption in that task have been the absence of layout encoding within LLMs, critical for a high quality extraction, and the lack of a grounding mechanism ensuring the answer is not hallucinated. In this paper, we introduce Language Model-based Document Information Extraction and Localization (LMDX), a methodology to adapt arbitrary LLMs for document information extraction. LMDX can do extraction of singular, repeated, and hierarchical entities, both with and without training data, while providing grounding guarantees and localizing the entities within the document. In particular, we apply LMDX to the PaLM 2-S LLM and evaluate it on VRDU and CORD benchmarks, setting a new state-of-the-art and showing how LMDX enables the creation of high quality, data-efficient parsers.},
	language = {en},
	urldate = {2023-10-10},
	journal = {arXiv.org},
	author = {Perot, Vincent and Kang, Kai and Luisier, Florian and Su, Guolong and Sun, Xiaoyu and Boppana, Ramya Sree and Wang, Zilong and Mu, Jiaqi and Zhang, Hao and Hua, Nan},
	month = sep,
	year = {2023},
}

@misc{noauthor_paper_2023,
	title = {Paper page - {LMDX}: {Language} {Model}-based {Document} {Information} {Extraction} and {Localization}},
	shorttitle = {Paper page - {LMDX}},
	url = {https://huggingface.co/papers/2309.10952},
	abstract = {Join the discussion on this paper page},
	urldate = {2023-10-10},
	month = oct,
	year = {2023},
}

@article{meister_learning_2022,
	title = {Learning, fast and slow},
	volume = {75},
	issn = {0959-4388},
	url = {https://www.sciencedirect.com/science/article/pii/S0959438822000496},
	doi = {10.1016/j.conb.2022.102555},
	abstract = {Animals can learn efficiently from a single experience and change their future behavior in response. However, in other instances, animals learn very slowly, requiring thousands of experiences. Here, I survey tasks involving fast and slow learning and consider some hypotheses for what differentiates the underlying neural mechanisms. It has been proposed that fast learning relies on neural representations that favor efficient Hebbian modification of synapses. These efficient representations may be encoded in the genome, resulting in a repertoire of fast learning that differs across species. Alternatively, the required neural representations may be acquired from experience through a slow process of unsupervised learning from the environment.},
	urldate = {2023-10-10},
	journal = {Current Opinion in Neurobiology},
	author = {Meister, Markus},
	month = aug,
	year = {2022},
	pages = {102555},
}

@misc{zhang_endotaxis_2023,
	title = {Endotaxis: {A} neuromorphic algorithm for mapping, goal-learning, navigation, and patrolling},
	copyright = {© 2023, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
	shorttitle = {Endotaxis},
	url = {https://www.biorxiv.org/content/10.1101/2021.09.24.461751v3},
	doi = {10.1101/2021.09.24.461751},
	abstract = {An animal entering a new environment typically faces three challenges: explore the space for resources, memorize their locations, and navigate towards those targets as needed. Experimental work on exploration, mapping, and navigation has mostly focused on simple environments – such as an open arena [68], a pond [42], or a desert [44] – and much has been learned about neural signals in diverse brain areas under these conditions [12, 54]. However, many natural environments are highly structured, such as a system of burrows, or of intersecting paths through the underbrush. Similarly, for many cognitive tasks, a sequence of simple actions can give rise to complex solutions. Here we propose an algorithm that learns the structure of a complex environment, discovers useful targets during exploration, and navigates back to those targets by the shortest path. It makes use of a behavioral module common to all motile animals, namely the ability to follow an odor to its source [4]. We show how the brain can learn to generate internal “virtual odors” that guide the animal to any location of interest. This endotaxis algorithm can be implemented with a simple 3-layer neural circuit using only biologically realistic structures and learning rules. Several neural components of this scheme are found in brains from insects to humans. Nature may have evolved a general mechanism for search and navigation on the ancient backbone of chemotaxis.},
	language = {en},
	urldate = {2023-10-10},
	publisher = {bioRxiv},
	author = {Zhang, Tony and Rosenberg, Matthew and Jing, Zeyu and Perona, Pietro and Meister, Markus},
	month = sep,
	year = {2023},
	note = {Pages: 2021.09.24.461751
Section: New Results},
}

@article{chan_grammatical_nodate,
	title = {Grammatical {Error} {Correction} for {Code}-{Switched} {Sentences} by {Learners} of {English}},
	language = {en},
	author = {Chan, Kelvin Wey Han},
}

@misc{denk_brain2music_2023,
	title = {{Brain2Music}: {Reconstructing} {Music} from {Human} {Brain} {Activity}},
	shorttitle = {{Brain2Music}},
	url = {https://arxiv.org/abs/2307.11078v1},
	abstract = {The process of reconstructing experiences from human brain activity offers a unique lens into how the brain interprets and represents the world. In this paper, we introduce a method for reconstructing music from brain activity, captured using functional magnetic resonance imaging (fMRI). Our approach uses either music retrieval or the MusicLM music generation model conditioned on embeddings derived from fMRI data. The generated music resembles the musical stimuli that human subjects experienced, with respect to semantic properties like genre, instrumentation, and mood. We investigate the relationship between different components of MusicLM and brain activity through a voxel-wise encoding modeling analysis. Furthermore, we discuss which brain regions represent information derived from purely textual descriptions of music stimuli. We provide supplementary material including examples of the reconstructed music at https://google-research.github.io/seanet/brain2music},
	language = {en},
	urldate = {2023-10-10},
	journal = {arXiv.org},
	author = {Denk, Timo I. and Takagi, Yu and Matsuyama, Takuya and Agostinelli, Andrea and Nakai, Tomoya and Frank, Christian and Nishimoto, Shinji},
	month = jul,
	year = {2023},
}

@misc{choi_encoding_2020,
	title = {Encoding {Musical} {Style} with {Transformer} {Autoencoders}},
	url = {http://arxiv.org/abs/1912.05537},
	doi = {10.48550/arXiv.1912.05537},
	abstract = {We consider the problem of learning high-level controls over the global structure of generated sequences, particularly in the context of symbolic music generation with complex language models. In this work, we present the Transformer autoencoder, which aggregates encodings of the input data across time to obtain a global representation of style from a given performance. We show it is possible to combine this global representation with other temporally distributed embeddings, enabling improved control over the separate aspects of performance style and melody. Empirically, we demonstrate the effectiveness of our method on various music generation tasks on the MAESTRO dataset and a YouTube dataset with 10,000+ hours of piano performances, where we achieve improvements in terms of log-likelihood and mean listening scores as compared to baselines.},
	urldate = {2023-10-10},
	publisher = {arXiv},
	author = {Choi, Kristy and Hawthorne, Curtis and Simon, Ian and Dinculescu, Monica and Engel, Jesse},
	month = jun,
	year = {2020},
	note = {arXiv:1912.05537 [cs, eess, stat]},
	keywords = {Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning},
}

@misc{schlichtkrull_averitec_2023,
	title = {{AVeriTeC}: {A} {Dataset} for {Real}-world {Claim} {Verification} with {Evidence} from the {Web}},
	shorttitle = {{AVeriTeC}},
	url = {http://arxiv.org/abs/2305.13117},
	doi = {10.48550/arXiv.2305.13117},
	abstract = {Existing datasets for automated fact-checking have substantial limitations, such as relying on artificial claims, lacking annotations for evidence and intermediate reasoning, or including evidence published after the claim. In this paper we introduce AVeriTeC, a new dataset of 4,568 real-world claims covering fact-checks by 50 different organizations. Each claim is annotated with question-answer pairs supported by evidence available online, as well as textual justifications explaining how the evidence combines to produce a verdict. Through a multi-round annotation process, we avoid common pitfalls including context dependence, evidence insufficiency, and temporal leakage, and reach a substantial inter-annotator agreement of \${\textbackslash}kappa=0.619\$ on verdicts. We develop a baseline as well as an evaluation scheme for verifying claims through several question-answering steps against the open web.},
	urldate = {2023-10-10},
	publisher = {arXiv},
	author = {Schlichtkrull, Michael and Guo, Zhijiang and Vlachos, Andreas},
	month = may,
	year = {2023},
	note = {arXiv:2305.13117 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{mazurczyk_disinformation_2023,
	title = {Disinformation 2.0 in the {Age} of {AI}: {A} {Cybersecurity} {Perspective}},
	shorttitle = {Disinformation 2.0 in the {Age} of {AI}},
	url = {https://arxiv.org/abs/2306.05569v2},
	abstract = {With the explosive advancement of AI technologies in recent years, the scene of the disinformation research is also expected to rapidly change. In this viewpoint article, in particular, we first present the notion of "disinformation 2.0" in the age of AI where disinformation would become more targeted and personalized, its content becomes very difficult to distinguish from real news, and its creation and dissemination become more accelerated by AI. Then, we discuss how disinformation 2.0 and cybersecurity fit and a possible layered countermeasure to address the threat in disinformation 2.0 in a holistic manner.},
	language = {en},
	urldate = {2023-10-10},
	journal = {arXiv.org},
	author = {Mazurczyk, Wojciech and Lee, Dongwon and Vlachos, Andreas},
	month = jun,
	year = {2023},
}

@misc{katnoria_visualising_2020,
	title = {Visualising {Beam} {Search} and {Other} {Decoding} {Algorithms} for {Natural} {Language} {Generation}},
	url = {https://medium.com/voice-tech-podcast/visualising-beam-search-and-other-decoding-algorithms-for-natural-language-generation-fbba7cba2c5b},
	abstract = {Reproduced from my original post here},
	language = {en},
	urldate = {2023-10-09},
	journal = {Voice Tech Podcast},
	author = {Katnoria},
	month = jan,
	year = {2020},
}

@misc{goyal_think_2023,
	title = {Think before you speak: {Training} {Language} {Models} {With} {Pause} {Tokens}},
	shorttitle = {Think before you speak},
	url = {https://arxiv.org/abs/2310.02226v1},
	abstract = {Language models generate responses by producing a series of tokens in immediate succession: the \$(K+1){\textasciicircum}\{th\}\$ token is an outcome of manipulating \$K\$ hidden vectors per layer, one vector per preceding token. What if instead we were to let the model manipulate say, \$K+10\$ hidden vectors, before it outputs the \$(K+1){\textasciicircum}\{th\}\$ token? We operationalize this idea by performing training and inference on language models with a (learnable) \${\textbackslash}textit\{pause\}\$ token, a sequence of which is appended to the input prefix. We then delay extracting the model's outputs until the last pause token is seen, thereby allowing the model to process extra computation before committing to an answer. We empirically evaluate \${\textbackslash}textit\{pause-training\}\$ on decoder-only models of 1B and 130M parameters with causal pretraining on C4, and on downstream tasks covering reasoning, question-answering, general understanding and fact recall. Our main finding is that inference-time delays show gains when the model is both pre-trained and finetuned with delays. For the 1B model, we witness gains on 8 of 9 tasks, most prominently, a gain of \$18{\textbackslash}\%\$ EM score on the QA task of SQuAD, \$8{\textbackslash}\%\$ on CommonSenseQA and \$1{\textbackslash}\%\$ accuracy on the reasoning task of GSM8k. Our work raises a range of conceptual and practical future research questions on making delayed next-token prediction a widely applicable new paradigm.},
	language = {en},
	urldate = {2023-10-09},
	journal = {arXiv.org},
	author = {Goyal, Sachin and Ji, Ziwei and Rawat, Ankit Singh and Menon, Aditya Krishna and Kumar, Sanjiv and Nagarajan, Vaishnavh},
	month = oct,
	year = {2023},
}

@misc{kurbiel_drawing_2021,
	title = {Drawing the {Transformer} {Network} from {Scratch} ({Part} 1)},
	url = {https://towardsdatascience.com/drawing-the-transformer-network-from-scratch-part-1-9269ed9a2c5e},
	abstract = {Getting a mental model of the Transformer in a playful way},
	language = {en},
	urldate = {2023-10-09},
	journal = {Medium},
	author = {Kurbiel, Thomas},
	month = jan,
	year = {2021},
}

@misc{cristina_transformer_2022,
	title = {The {Transformer} {Model}},
	url = {https://machinelearningmastery.com/the-transformer-model/},
	abstract = {We have already familiarized ourselves with the concept of self-attention as implemented by the Transformer attention mechanism for neural machine translation. We will now be shifting our focus to the details of the Transformer architecture itself to discover how self-attention can be implemented without relying on the use of recurrence and convolutions. In this tutorial, […]},
	language = {en-US},
	urldate = {2023-10-09},
	journal = {MachineLearningMastery.com},
	author = {Cristina, Stefania},
	month = sep,
	year = {2022},
}

@inproceedings{thorne_fact_2018,
	address = {Brussels, Belgium},
	title = {The {Fact} {Extraction} and {VERification} ({FEVER}) {Shared} {Task}},
	url = {https://aclanthology.org/W18-5501},
	doi = {10.18653/v1/W18-5501},
	abstract = {We present the results of the first Fact Extraction and VERification (FEVER) Shared Task. The task challenged participants to classify whether human-written factoid claims could be SUPPORTED or REFUTED using evidence retrieved from Wikipedia. We received entries from 23 competing teams, 19 of which scored higher than the previously published baseline. The best performing system achieved a FEVER score of 64.21\%. In this paper, we present the results of the shared task and a summary of the systems, highlighting commonalities and innovations among participating systems.},
	urldate = {2023-10-09},
	booktitle = {Proceedings of the {First} {Workshop} on {Fact} {Extraction} and {VERification} ({FEVER})},
	publisher = {Association for Computational Linguistics},
	author = {Thorne, James and Vlachos, Andreas and Cocarascu, Oana and Christodoulopoulos, Christos and Mittal, Arpit},
	month = nov,
	year = {2018},
	pages = {1--9},
}

@article{tu_empirical_2020,
	title = {An {Empirical} {Study} on {Robustness} to {Spurious} {Correlations} using {Pre}-trained {Language} {Models}},
	volume = {8},
	url = {https://aclanthology.org/2020.tacl-1.40},
	doi = {10.1162/tacl_a_00335},
	abstract = {Recent work has shown that pre-trained language models such as BERT improve robustness to spurious correlations in the dataset. Intrigued by these results, we find that the key to their success is generalization from a small amount of counterexamples where the spurious correlations do not hold. When such minority examples are scarce, pre-trained models perform as poorly as models trained from scratch. In the case of extreme minority, we propose to use multi-task learning (MTL) to improve generalization. Our experiments on natural language inference and paraphrase identification show that MTL with the right auxiliary tasks significantly improves performance on challenging examples without hurting the in-distribution performance. Further, we show that the gain from MTL mainly comes from improved generalization from the minority examples. Our results highlight the importance of data diversity for overcoming spurious correlations.1},
	urldate = {2023-10-09},
	journal = {Transactions of the Association for Computational Linguistics},
	author = {Tu, Lifu and Lalwani, Garima and Gella, Spandana and He, He},
	year = {2020},
	note = {Place: Cambridge, MA
Publisher: MIT Press},
	pages = {621--633},
}

@article{guo_survey_2022,
	title = {A survey on automated fact-checking},
	volume = {10},
	url = {https://direct.mit.edu/tacl/article-abstract/doi/10.1162/tacl_a_00454/109469},
	urldate = {2023-10-09},
	journal = {Transactions of the Association for Computational Linguistics},
	author = {Guo, Zhijiang and Schlichtkrull, Michael and Vlachos, Andreas},
	year = {2022},
	note = {Publisher: MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info …},
	pages = {178--206},
}

@article{krishna_proofver_2022,
	title = {{ProoFVer}: {Natural} {Logic} {Theorem} {Proving} for {Fact} {Verification}},
	volume = {10},
	issn = {2307-387X},
	shorttitle = {{ProoFVer}},
	url = {https://doi.org/10.1162/tacl_a_00503},
	doi = {10.1162/tacl_a_00503},
	abstract = {Fact verification systems typically rely on neural network classifiers for veracity prediction, which lack explainability. This paper proposes ProoFVer, which uses a seq2seq model to generate natural logic-based inferences as proofs. These proofs consist of lexical mutations between spans in the claim and the evidence retrieved, each marked with a natural logic operator. Claim veracity is determined solely based on the sequence of these operators. Hence, these proofs are faithful explanations, and this makes ProoFVer faithful by construction. Currently, ProoFVer has the highest label accuracy and the second best score in the FEVER leaderboard. Furthermore, it improves by 13.21\% points over the next best model on a dataset with counterfactual instances, demonstrating its robustness. As explanations, the proofs show better overlap with human rationales than attention-based highlights and the proofs help humans predict model decisions correctly more often than using the evidence directly.1},
	urldate = {2023-10-08},
	journal = {Transactions of the Association for Computational Linguistics},
	author = {Krishna, Amrith and Riedel, Sebastian and Vlachos, Andreas},
	month = sep,
	year = {2022},
	pages = {1013--1030},
}

@misc{khandelwal_nearest_2021,
	title = {Nearest {Neighbor} {Machine} {Translation}},
	url = {http://arxiv.org/abs/2010.00710},
	doi = {10.48550/arXiv.2010.00710},
	abstract = {We introduce \$k\$-nearest-neighbor machine translation (\$k\$NN-MT), which predicts tokens with a nearest neighbor classifier over a large datastore of cached examples, using representations from a neural translation model for similarity search. This approach requires no additional training and scales to give the decoder direct access to billions of examples at test time, resulting in a highly expressive model that consistently improves performance across many settings. Simply adding nearest neighbor search improves a state-of-the-art German-English translation model by 1.5 BLEU. \$k\$NN-MT allows a single model to be adapted to diverse domains by using a domain-specific datastore, improving results by an average of 9.2 BLEU over zero-shot transfer, and achieving new state-of-the-art results -- without training on these domains. A massively multilingual model can also be specialized for particular language pairs, with improvements of 3 BLEU for translating from English into German and Chinese. Qualitatively, \$k\$NN-MT is easily interpretable; it combines source and target context to retrieve highly relevant examples.},
	urldate = {2023-10-08},
	publisher = {arXiv},
	author = {Khandelwal, Urvashi and Fan, Angela and Jurafsky, Dan and Zettlemoyer, Luke and Lewis, Mike},
	month = jul,
	year = {2021},
	note = {arXiv:2010.00710 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@inproceedings{gururangan_whose_2022,
	address = {Abu Dhabi, United Arab Emirates},
	title = {Whose {Language} {Counts} as {High} {Quality}? {Measuring} {Language} {Ideologies} in {Text} {Data} {Selection}},
	shorttitle = {Whose {Language} {Counts} as {High} {Quality}?},
	url = {https://aclanthology.org/2022.emnlp-main.165},
	doi = {10.18653/v1/2022.emnlp-main.165},
	abstract = {Language models increasingly rely on massive web crawls for diverse text data. However, these sources are rife with undesirable content. As such, resources like Wikipedia, books, and news often serve as anchors for automatically selecting web text most suitable for language modeling, a process typically referred to as quality filtering. Using a new dataset of U.S. high school newspaper articles—written by students from across the country—we investigate whose language is preferred by the quality filter used for GPT-3. We find that newspapers from larger schools, located in wealthier, educated, and urban zones (ZIP codes) are more likely to be classified as high quality. We also show that this quality measurement is unaligned with other sensible metrics, such as factuality or literary acclaim. We argue that privileging any corpus as high quality entails a language ideology, and more care is needed to construct training corpora for language models, with better transparency and justification for the inclusion or exclusion of various texts.},
	urldate = {2023-10-08},
	booktitle = {Proceedings of the 2022 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Gururangan, Suchin and Card, Dallas and Dreier, Sarah and Gade, Emily and Wang, Leroy and Wang, Zeyu and Zettlemoyer, Luke and Smith, Noah A.},
	month = dec,
	year = {2022},
	pages = {2562--2580},
}

@article{jiang_investigating_2022,
	title = {Investigating {Reasons} for {Disagreement} in {Natural} {Language} {Inference}},
	volume = {10},
	issn = {2307-387X},
	url = {https://doi.org/10.1162/tacl_a_00523},
	doi = {10.1162/tacl_a_00523},
	abstract = {We investigate how disagreement in natural language inference (NLI) annotation
arises. We developed a taxonomy of disagreement sources with 10 categories
spanning 3 high- level classes. We found that some disagreements are due to
uncertainty in the sentence meaning, others to annotator biases and task
artifacts, leading to different interpretations of the label distribution. We
explore two modeling approaches for detecting items with potential disagreement:
a 4-way classification with a “Complicated” label in addition to
the three standard NLI labels, and a multilabel classification approach. We
found that the multilabel classification is more expressive and gives better
recall of the possible interpretations in the data.},
	urldate = {2023-10-08},
	journal = {Transactions of the Association for Computational Linguistics},
	author = {Jiang, Nan-Jiang and Marneffe, Marie-Catherine de},
	month = dec,
	year = {2022},
	pages = {1357--1374},
}

@article{perez_true_nodate,
	title = {True {Few}-{Shot} {Learning} with {Language} {Models}},
	abstract = {Pretrained language models (LMs) perform well on many tasks even when learning from a few examples, but prior work uses many held-out examples to tune various aspects of learning, such as hyperparameters, training objectives, and natural language templates (“prompts”). Here, we evaluate the few-shot ability of LMs when such held-out examples are unavailable, a setting we call true few-shot learning. We test two model selection criteria, cross-validation and minimum description length, for choosing LM prompts and hyperparameters in the true few-shot setting. On average, both marginally outperform random selection and greatly underperform selection based on held-out examples. Moreover, selection criteria often prefer models that perform signiﬁcantly worse than randomly-selected ones. We ﬁnd similar results even when taking into account our uncertainty in a model’s true performance during selection, as well as when varying the amount of computation and number of examples used for selection. Overall, our ﬁndings suggest that prior work signiﬁcantly overestimated the true few-shot ability of LMs given the difﬁculty of few-shot model selection.},
	language = {en},
	author = {Perez, Ethan and Kiela, Douwe and Cho, Kyunghyun},
}

@misc{lewis_retrieval-augmented_2021,
	title = {Retrieval-{Augmented} {Generation} for {Knowledge}-{Intensive} {NLP} {Tasks}},
	url = {http://arxiv.org/abs/2005.11401},
	doi = {10.48550/arXiv.2005.11401},
	abstract = {Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind task-specific architectures. Additionally, providing provenance for their decisions and updating their world knowledge remain open research problems. Pre-trained models with a differentiable access mechanism to explicit non-parametric memory can overcome this issue, but have so far been only investigated for extractive downstream tasks. We explore a general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) -- models which combine pre-trained parametric and non-parametric memory for language generation. We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. We compare two RAG formulations, one which conditions on the same retrieved passages across the whole generated sequence, the other can use different passages per token. We fine-tune and evaluate our models on a wide range of knowledge-intensive NLP tasks and set the state-of-the-art on three open domain QA tasks, outperforming parametric seq2seq models and task-specific retrieve-and-extract architectures. For language generation tasks, we find that RAG models generate more specific, diverse and factual language than a state-of-the-art parametric-only seq2seq baseline.},
	urldate = {2023-10-08},
	publisher = {arXiv},
	author = {Lewis, Patrick and Perez, Ethan and Piktus, Aleksandra and Petroni, Fabio and Karpukhin, Vladimir and Goyal, Naman and Küttler, Heinrich and Lewis, Mike and Yih, Wen-tau and Rocktäschel, Tim and Riedel, Sebastian and Kiela, Douwe},
	month = apr,
	year = {2021},
	note = {arXiv:2005.11401 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@article{bartolo_beat_2020,
	title = {Beat the {AI}: {Investigating} {Adversarial} {Human} {Annotation} for {Reading} {Comprehension}},
	volume = {8},
	issn = {2307-387X},
	shorttitle = {Beat the {AI}},
	url = {https://doi.org/10.1162/tacl_a_00338},
	doi = {10.1162/tacl_a_00338},
	abstract = {Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the annotation process: Humans create questions adversarially, such that the model fails to answer them correctly. In this work we investigate this annotation methodology and apply it in three different settings, collecting a total of 36,000 samples with progressively stronger models in the annotation loop. This allows us to explore questions such as the reproducibility of the adversarial effect, transfer from data collected with varying model-in-the-loop strengths, and generalization to data collected without a model. We find that training on adversarially collected samples leads to strong generalization to non-adversarially collected datasets, yet with progressive performance deterioration with increasingly stronger models-in-the-loop. Furthermore, we find that stronger models can still learn from datasets collected with substantially weaker models-in-the-loop. When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 39.9F1 on questions that it cannot answer when trained on SQuAD—only marginally lower than when trained on data collected using RoBERTa itself (41.0F1).},
	urldate = {2023-10-08},
	journal = {Transactions of the Association for Computational Linguistics},
	author = {Bartolo, Max and Roberts, Alastair and Welbl, Johannes and Riedel, Sebastian and Stenetorp, Pontus},
	month = nov,
	year = {2020},
	pages = {662--678},
}

@inproceedings{swayamdipta_dataset_2020,
	address = {Online},
	title = {Dataset {Cartography}: {Mapping} and {Diagnosing} {Datasets} with {Training} {Dynamics}},
	shorttitle = {Dataset {Cartography}},
	url = {https://aclanthology.org/2020.emnlp-main.746},
	doi = {10.18653/v1/2020.emnlp-main.746},
	abstract = {Large datasets have become commonplace in NLP research. However, the increased emphasis on data quantity has made it challenging to assess the quality of data. We introduce Data Maps—a model-based tool to characterize and diagnose datasets. We leverage a largely ignored source of information: the behavior of the model on individual instances during training (training dynamics) for building data maps. This yields two intuitive measures for each example—the model's confidence in the true class, and the variability of this confidence across epochs—obtained in a single run of training. Experiments on four datasets show that these model-dependent measures reveal three distinct regions in the data map, each with pronounced characteristics. First, our data maps show the presence of “ambiguous” regions with respect to the model, which contribute the most towards out-of-distribution generalization. Second, the most populous regions in the data are “easy to learn” for the model, and play an important role in model optimization. Finally, data maps uncover a region with instances that the model finds “hard to learn”; these often correspond to labeling errors. Our results indicate that a shift in focus from quantity to quality of data could lead to robust models and improved out-of-distribution generalization.},
	urldate = {2023-10-08},
	booktitle = {Proceedings of the 2020 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing} ({EMNLP})},
	publisher = {Association for Computational Linguistics},
	author = {Swayamdipta, Swabha and Schwartz, Roy and Lourie, Nicholas and Wang, Yizhong and Hajishirzi, Hannaneh and Smith, Noah A. and Choi, Yejin},
	month = nov,
	year = {2020},
	pages = {9275--9293},
}

@inproceedings{eikema_is_2020-1,
	address = {Barcelona, Spain (Online)},
	title = {Is {MAP} {Decoding} {All} {You} {Need}? {The} {Inadequacy} of the {Mode} in {Neural} {Machine} {Translation}},
	shorttitle = {Is {MAP} {Decoding} {All} {You} {Need}?},
	url = {https://aclanthology.org/2020.coling-main.398},
	doi = {10.18653/v1/2020.coling-main.398},
	abstract = {Recent studies have revealed a number of pathologies of neural machine translation (NMT) systems. Hypotheses explaining these mostly suggest there is something fundamentally wrong with NMT as a model or its training algorithm, maximum likelihood estimation (MLE). Most of this evidence was gathered using maximum a posteriori (MAP) decoding, a decision rule aimed at identifying the highest-scoring translation, i.e. the mode. We argue that the evidence corroborates the inadequacy of MAP decoding more than casts doubt on the model and its training algorithm. In this work, we show that translation distributions do reproduce various statistics of the data well, but that beam search strays from such statistics. We show that some of the known pathologies and biases of NMT are due to MAP decoding and not to NMT's statistical assumptions nor MLE. In particular, we show that the most likely translations under the model accumulate so little probability mass that the mode can be considered essentially arbitrary. We therefore advocate for the use of decision rules that take into account the translation distribution holistically. We show that an approximation to minimum Bayes risk decoding gives competitive results confirming that NMT models do capture important aspects of translation well in expectation.},
	urldate = {2023-10-08},
	booktitle = {Proceedings of the 28th {International} {Conference} on {Computational} {Linguistics}},
	publisher = {International Committee on Computational Linguistics},
	author = {Eikema, Bryan and Aziz, Wilker},
	month = dec,
	year = {2020},
	pages = {4506--4520},
}

@inproceedings{stahlberg_nmt_2019,
	address = {Hong Kong, China},
	title = {On {NMT} {Search} {Errors} and {Model} {Errors}: {Cat} {Got} {Your} {Tongue}?},
	shorttitle = {On {NMT} {Search} {Errors} and {Model} {Errors}},
	url = {https://aclanthology.org/D19-1331},
	doi = {10.18653/v1/D19-1331},
	abstract = {We report on search errors and model errors in neural machine translation (NMT). We present an exact inference procedure for neural sequence models based on a combination of beam search and depth-first search. We use our exact search to find the global best model scores under a Transformer base model for the entire WMT15 English-German test set. Surprisingly, beam search fails to find these global best model scores in most cases, even with a very large beam size of 100. For more than 50\% of the sentences, the model in fact assigns its global best score to the empty translation, revealing a massive failure of neural models in properly accounting for adequacy. We show by constraining search with a minimum translation length that at the root of the problem of empty translations lies an inherent bias towards shorter translations. We conclude that vanilla NMT in its current form requires just the right amount of beam search errors, which, from a modelling perspective, is a highly unsatisfactory conclusion indeed, as the model often prefers an empty translation.},
	urldate = {2023-10-08},
	booktitle = {Proceedings of the 2019 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing} and the 9th {International} {Joint} {Conference} on {Natural} {Language} {Processing} ({EMNLP}-{IJCNLP})},
	publisher = {Association for Computational Linguistics},
	author = {Stahlberg, Felix and Byrne, Bill},
	month = nov,
	year = {2019},
	pages = {3356--3362},
}

@misc{de_cao_autoregressive_2020,
	title = {Autoregressive {Entity} {Retrieval}},
	url = {https://arxiv.org/abs/2010.00904v3},
	abstract = {Entities are at the center of how we represent and aggregate knowledge. For instance, Encyclopedias such as Wikipedia are structured by entities (e.g., one per Wikipedia article). The ability to retrieve such entities given a query is fundamental for knowledge-intensive tasks such as entity linking and open-domain question answering. Current approaches can be understood as classifiers among atomic labels, one for each entity. Their weight vectors are dense entity representations produced by encoding entity meta information such as their descriptions. This approach has several shortcomings: (i) context and entity affinity is mainly captured through a vector dot product, potentially missing fine-grained interactions; (ii) a large memory footprint is needed to store dense representations when considering large entity sets; (iii) an appropriately hard set of negative data has to be subsampled at training time. In this work, we propose GENRE, the first system that retrieves entities by generating their unique names, left to right, token-by-token in an autoregressive fashion. This mitigates the aforementioned technical issues since: (i) the autoregressive formulation directly captures relations between context and entity name, effectively cross encoding both; (ii) the memory footprint is greatly reduced because the parameters of our encoder-decoder architecture scale with vocabulary size, not entity count; (iii) the softmax loss is computed without subsampling negative data. We experiment with more than 20 datasets on entity disambiguation, end-to-end entity linking and document retrieval tasks, achieving new state-of-the-art or very competitive results while using a tiny fraction of the memory footprint of competing systems. Finally, we demonstrate that new entities can be added by simply specifying their names. Code and pre-trained models at https://github.com/facebookresearch/GENRE.},
	language = {en},
	urldate = {2023-10-08},
	journal = {arXiv.org},
	author = {De Cao, Nicola and Izacard, Gautier and Riedel, Sebastian and Petroni, Fabio},
	month = oct,
	year = {2020},
}

@misc{ranzato_sequence_2015,
	title = {Sequence {Level} {Training} with {Recurrent} {Neural} {Networks}},
	url = {https://arxiv.org/abs/1511.06732v7},
	abstract = {Many natural language processing applications use language models to generate text. These models are typically trained to predict the next word in a sequence, given the previous words and some context such as an image. However, at test time the model is expected to generate the entire sequence from scratch. This discrepancy makes generation brittle, as errors may accumulate along the way. We address this issue by proposing a novel sequence level training algorithm that directly optimizes the metric used at test time, such as BLEU or ROUGE. On three different tasks, our approach outperforms several strong baselines for greedy generation. The method is also competitive when these baselines employ beam search, while being several times faster.},
	language = {en},
	urldate = {2023-10-08},
	journal = {arXiv.org},
	author = {Ranzato, Marc'Aurelio and Chopra, Sumit and Auli, Michael and Zaremba, Wojciech},
	month = nov,
	year = {2015},
}

@article{tu_empirical_2020-1,
	title = {An {Empirical} {Study} on {Robustness} to {Spurious} {Correlations} using {Pre}-trained {Language} {Models}},
	volume = {8},
	url = {https://aclanthology.org/2020.tacl-1.40},
	doi = {10.1162/tacl_a_00335},
	abstract = {Recent work has shown that pre-trained language models such as BERT improve robustness to spurious correlations in the dataset. Intrigued by these results, we find that the key to their success is generalization from a small amount of counterexamples where the spurious correlations do not hold. When such minority examples are scarce, pre-trained models perform as poorly as models trained from scratch. In the case of extreme minority, we propose to use multi-task learning (MTL) to improve generalization. Our experiments on natural language inference and paraphrase identification show that MTL with the right auxiliary tasks significantly improves performance on challenging examples without hurting the in-distribution performance. Further, we show that the gain from MTL mainly comes from improved generalization from the minority examples. Our results highlight the importance of data diversity for overcoming spurious correlations.1},
	urldate = {2023-10-08},
	journal = {Transactions of the Association for Computational Linguistics},
	author = {Tu, Lifu and Lalwani, Garima and Gella, Spandana and He, He},
	year = {2020},
	note = {Place: Cambridge, MA
Publisher: MIT Press},
	pages = {621--633},
}

@inproceedings{bowman_large_2015,
	address = {Lisbon, Portugal},
	title = {A large annotated corpus for learning natural language inference},
	url = {https://aclanthology.org/D15-1075},
	doi = {10.18653/v1/D15-1075},
	urldate = {2023-10-08},
	booktitle = {Proceedings of the 2015 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher and Manning, Christopher D.},
	month = sep,
	year = {2015},
	pages = {632--642},
}

@inproceedings{levy_zero-shot_2017,
	address = {Vancouver, Canada},
	title = {Zero-{Shot} {Relation} {Extraction} via {Reading} {Comprehension}},
	url = {https://aclanthology.org/K17-1034},
	doi = {10.18653/v1/K17-1034},
	abstract = {We show that relation extraction can be reduced to answering simple reading comprehension questions, by associating one or more natural-language questions with each relation slot. This reduction has several advantages: we can (1) learn relation-extraction models by extending recent neural reading-comprehension techniques, (2) build very large training sets for those models by combining relation-specific crowd-sourced questions with distant supervision, and even (3) do zero-shot learning by extracting new relation types that are only specified at test-time, for which we have no labeled training examples. Experiments on a Wikipedia slot-filling task demonstrate that the approach can generalize to new questions for known relation types with high accuracy, and that zero-shot generalization to unseen relation types is possible, at lower accuracy levels, setting the bar for future work on this task.},
	urldate = {2023-10-08},
	booktitle = {Proceedings of the 21st {Conference} on {Computational} {Natural} {Language} {Learning} ({CoNLL} 2017)},
	publisher = {Association for Computational Linguistics},
	author = {Levy, Omer and Seo, Minjoon and Choi, Eunsol and Zettlemoyer, Luke},
	month = aug,
	year = {2017},
	pages = {333--342},
}

@inproceedings{ferreira_incorporating_2019,
	address = {Hong Kong, China},
	title = {Incorporating {Label} {Dependencies} in {Multilabel} {Stance} {Detection}},
	url = {https://aclanthology.org/D19-1665},
	doi = {10.18653/v1/D19-1665},
	abstract = {Stance detection in social media is a well-studied task in a variety of domains. Nevertheless, previous work has mostly focused on multiclass versions of the problem, where the labels are mutually exclusive, and typically positive, negative or neutral. In this paper, we address versions of the task in which an utterance can have multiple labels, thus corresponding to multilabel classification. We propose a method that explicitly incorporates label dependencies in the training objective and compare it against a variety of baselines, as well as a reduction of multilabel to multiclass learning. In experiments with three datasets, we find that our proposed method improves upon all baselines on two out of three datasets. We also show that the reduction of multilabel to multiclass classification can be very competitive, especially in cases where the output consists of a small number of labels and one can enumerate over all label combinations.},
	urldate = {2023-10-08},
	booktitle = {Proceedings of the 2019 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing} and the 9th {International} {Joint} {Conference} on {Natural} {Language} {Processing} ({EMNLP}-{IJCNLP})},
	publisher = {Association for Computational Linguistics},
	author = {Ferreira, William and Vlachos, Andreas},
	month = nov,
	year = {2019},
	pages = {6350--6354},
}

@inproceedings{riedel_relation_2013,
	address = {Atlanta, Georgia},
	title = {Relation {Extraction} with {Matrix} {Factorization} and {Universal} {Schemas}},
	url = {https://aclanthology.org/N13-1008},
	urldate = {2023-10-08},
	booktitle = {Proceedings of the 2013 {Conference} of the {North} {American} {Chapter} of the {Association} for {Computational} {Linguistics}: {Human} {Language} {Technologies}},
	publisher = {Association for Computational Linguistics},
	author = {Riedel, Sebastian and Yao, Limin and McCallum, Andrew and Marlin, Benjamin M.},
	month = jun,
	year = {2013},
	pages = {74--84},
}

@article{mendels_collecting_nodate,
	title = {Collecting {Code}-{Switched} {Data} from {Social} {Media}},
	abstract = {We address the problem of mining code-switched data from the web, where code-switching is deﬁned as the tendency of bilinguals to switch between their multiple languages both across and within utterances. We propose a method that identiﬁes data as code-switched in languages L1 and L2 when a language classiﬁer labels the document as language L1 but the document also contains words that can only belong to L2. We apply our method to Twitter data and collect a set of more than 43,000 tweets. We obtain language identiﬁers for a subset of 8,000 tweets using crowd-sourcing with high inter-annotator agreement and accuracy. We validate our Twitter corpus by comparing it to the Spanish-English corpus of code-switched tweets collected for the EMNLP 2016 Shared Task for Language Identiﬁcation, in terms of code-switching rates, language composition and amount of code-switch types found in both datasets. We then trained language taggers on both corpora and show that a tagger trained on the EMNLP corpus exhibits a considerable drop in accuracy when tested on the new corpus and a tagger trained on our new corpus achieves very high accuracy when tested on both corpora.},
	language = {en},
	author = {Mendels, Gideon and Soto, Victor and Jaech, Aaron and Hirschberg, Julia},
}

@misc{frankle_lottery_2019,
	title = {The {Lottery} {Ticket} {Hypothesis}: {Finding} {Sparse}, {Trainable} {Neural} {Networks}},
	shorttitle = {The {Lottery} {Ticket} {Hypothesis}},
	url = {http://arxiv.org/abs/1803.03635},
	doi = {10.48550/arXiv.1803.03635},
	abstract = {Neural network pruning techniques can reduce the parameter counts of trained networks by over 90\%, decreasing storage requirements and improving computational performance of inference without compromising accuracy. However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance. We find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively. Based on these results, we articulate the "lottery ticket hypothesis:" dense, randomly-initialized, feed-forward networks contain subnetworks ("winning tickets") that - when trained in isolation - reach test accuracy comparable to the original network in a similar number of iterations. The winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective. We present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis and the importance of these fortuitous initializations. We consistently find winning tickets that are less than 10-20\% of the size of several fully-connected and convolutional feed-forward architectures for MNIST and CIFAR10. Above this size, the winning tickets that we find learn faster than the original network and reach higher test accuracy.},
	urldate = {2023-10-06},
	publisher = {arXiv},
	author = {Frankle, Jonathan and Carbin, Michael},
	month = mar,
	year = {2019},
	note = {arXiv:1803.03635 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing},
}

@misc{koulakov_encoding_2022,
	title = {Encoding innate ability through a genomic bottleneck},
	copyright = {© 2022, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/},
	url = {https://www.biorxiv.org/content/10.1101/2021.03.16.435261v2},
	doi = {10.1101/2021.03.16.435261},
	abstract = {Animals are born with extensive innate behavioral capabilities, which arise from neural circuits encoded in the genome. However, the information capacity of the genome is orders of magnitude smaller than that needed to specify the connectivity of an arbitrary brain circuit, indicating that the rules encoding circuit formation must fit through a “genomic bottleneck” as they pass from one generation to the next. Here we formulate the problem of innate behavioral capacity in the context of artificial neural networks in terms of lossy compression of the weight matrix. We find that several standard network architectures can be compressed by several orders of magnitude, yielding pre-training performance that can approach that of the fully-trained network. Interestingly, for complex but not for simple test problems, the genomic bottleneck algorithm also captures essential features of the circuit, leading to enhanced transfer learning to novel tasks and datasets. Our results suggest that compressing a neural circuit through the genomic bottleneck serves as a regularizer, enabling evolution to select simple circuits that can be readily adapted to important real-world tasks. The genomic bottleneck also suggests how innate priors can complement conventional approaches to learning in designing algorithms for artificial intelligence.},
	language = {en},
	urldate = {2023-10-06},
	publisher = {bioRxiv},
	author = {Koulakov, Alexei and Shuvaev, Sergey and Lachi, Divyansha and Zador, Anthony},
	month = may,
	year = {2022},
	note = {Pages: 2021.03.16.435261
Section: New Results},
}

@misc{korbak_pretraining_2023,
	title = {Pretraining {Language} {Models} with {Human} {Preferences}},
	url = {http://arxiv.org/abs/2302.08582},
	doi = {10.48550/arXiv.2302.08582},
	abstract = {Language models (LMs) are pretrained to imitate internet text, including content that would violate human preferences if generated by an LM: falsehoods, offensive comments, personally identifiable information, low-quality or buggy code, and more. Here, we explore alternative objectives for pretraining LMs in a way that also guides them to generate text aligned with human preferences. We benchmark five objectives for pretraining with human feedback across three tasks and study how they affect the trade-off between alignment and capabilities of pretrained LMs. We find a Pareto-optimal and simple approach among those we explored: conditional training, or learning distribution over tokens conditional on their human preference scores given by a reward model. Conditional training reduces the rate of undesirable content by up to an order of magnitude, both when generating without a prompt and with an adversarially-chosen prompt. Moreover, conditional training maintains the downstream task performance of standard LM pretraining, both before and after task-specific finetuning. Pretraining with human feedback results in much better preference satisfaction than standard LM pretraining followed by finetuning with feedback, i.e., learning and then unlearning undesirable behavior. Our results suggest that we should move beyond imitation learning when pretraining LMs and incorporate human preferences from the start of training.},
	urldate = {2023-10-04},
	publisher = {arXiv},
	author = {Korbak, Tomasz and Shi, Kejian and Chen, Angelica and Bhalerao, Rasika and Buckley, Christopher L. and Phang, Jason and Bowman, Samuel R. and Perez, Ethan},
	month = jun,
	year = {2023},
	note = {arXiv:2302.08582 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{schick_generating_2021,
	title = {Generating {Datasets} with {Pretrained} {Language} {Models}},
	url = {http://arxiv.org/abs/2104.07540},
	doi = {10.48550/arXiv.2104.07540},
	abstract = {To obtain high-quality sentence embeddings from pretrained language models (PLMs), they must either be augmented with additional pretraining objectives or finetuned on a large set of labeled text pairs. While the latter approach typically outperforms the former, it requires great human effort to generate suitable datasets of sufficient size. In this paper, we show how PLMs can be leveraged to obtain high-quality sentence embeddings without the need for labeled data, finetuning or modifications to the pretraining objective: We utilize the generative abilities of large and high-performing PLMs to generate entire datasets of labeled text pairs from scratch, which we then use for finetuning much smaller and more efficient models. Our fully unsupervised approach outperforms strong baselines on several semantic textual similarity datasets.},
	urldate = {2023-10-04},
	publisher = {arXiv},
	author = {Schick, Timo and Schütze, Hinrich},
	month = oct,
	year = {2021},
	note = {arXiv:2104.07540 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@inproceedings{komeili_internet-augmented_2022,
	address = {Dublin, Ireland},
	title = {Internet-{Augmented} {Dialogue} {Generation}},
	url = {https://aclanthology.org/2022.acl-long.579},
	doi = {10.18653/v1/2022.acl-long.579},
	abstract = {The largest store of continually updating knowledge on our planet can be accessed via internet search. In this work we study giving access to this information to conversational agents. Large language models, even though they store an impressive amount of knowledge within their weights, are known to hallucinate facts when generating dialogue (Shuster et al., 2021); moreover, those facts are frozen in time at the point of model training. In contrast, we propose an approach that learns to generate an internet search query based on the context, and then conditions on the search results to finally generate a response, a method that can employ up-to-the-minute relevant information. We train and evaluate such models on a newly collected dataset of human-human conversations whereby one of the speakers is given access to internet search during knowledgedriven discussions in order to ground their responses. We find that search-query based access of the internet in conversation provides superior performance compared to existing approaches that either use no augmentation or FAISS-based retrieval (Lewis et al., 2020b).},
	urldate = {2023-10-04},
	booktitle = {Proceedings of the 60th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics} ({Volume} 1: {Long} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Komeili, Mojtaba and Shuster, Kurt and Weston, Jason},
	month = may,
	year = {2022},
	pages = {8460--8478},
}

@misc{schick_toolformer_2023,
	title = {Toolformer: {Language} {Models} {Can} {Teach} {Themselves} to {Use} {Tools}},
	shorttitle = {Toolformer},
	url = {http://arxiv.org/abs/2302.04761},
	doi = {10.48550/arXiv.2302.04761},
	abstract = {Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with basic functionality, such as arithmetic or factual lookup, where much simpler and smaller models excel. In this paper, we show that LMs can teach themselves to use external tools via simple APIs and achieve the best of both worlds. We introduce Toolformer, a model trained to decide which APIs to call, when to call them, what arguments to pass, and how to best incorporate the results into future token prediction. This is done in a self-supervised way, requiring nothing more than a handful of demonstrations for each API. We incorporate a range of tools, including a calculator, a Q{\textbackslash}\&A system, two different search engines, a translation system, and a calendar. Toolformer achieves substantially improved zero-shot performance across a variety of downstream tasks, often competitive with much larger models, without sacrificing its core language modeling abilities.},
	urldate = {2023-10-04},
	publisher = {arXiv},
	author = {Schick, Timo and Dwivedi-Yu, Jane and Dessì, Roberto and Raileanu, Roberta and Lomeli, Maria and Zettlemoyer, Luke and Cancedda, Nicola and Scialom, Thomas},
	month = feb,
	year = {2023},
	note = {arXiv:2302.04761 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{glockner_ambifc_2023,
	title = {{AmbiFC}: {Fact}-{Checking} {Ambiguous} {Claims} with {Evidence}},
	shorttitle = {{AmbiFC}},
	url = {http://arxiv.org/abs/2104.00640},
	abstract = {Automated fact-checking systems verify claims against evidence to predict their veracity. In real-world scenarios, the retrieved evidence may not unambiguously support or refute the claim and yield conflicting but valid interpretations. Existing fact-checking datasets assume that the models developed with them predict a single veracity label for each claim, thus discouraging the handling of such ambiguity. To address this issue we present AmbiFC, a fact-checking dataset with 10k claims derived from real-world information needs. It contains fine-grained evidence annotations of 50k passages from 5k Wikipedia pages. We analyze the disagreements arising from ambiguity when comparing claims against evidence in AmbiFC, observing a strong correlation of annotator disagreement with linguistic phenomena such as underspecification and probabilistic reasoning. We develop models for predicting veracity handling this ambiguity via soft labels and find that a pipeline that learns the label distribution for sentence-level evidence selection and veracity prediction yields the best performance. We compare models trained on different subsets of AmbiFC and show that models trained on the ambiguous instances perform better when faced with the identified linguistic phenomena.},
	urldate = {2023-10-04},
	publisher = {arXiv},
	author = {Glockner, Max and Staliūnaitė, Ieva and Thorne, James and Vallejo, Gisela and Vlachos, Andreas and Gurevych, Iryna},
	month = sep,
	year = {2023},
	note = {arXiv:2104.00640 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

@misc{glockner_ambifc_2021,
	title = {{AmbiFC}: {Fact}-{Checking} {Ambiguous} {Claims} with {Evidence}},
	shorttitle = {{AmbiFC}},
	url = {https://ui.adsabs.harvard.edu/abs/2021arXiv210400640G},
	doi = {10.48550/arXiv.2104.00640},
	abstract = {Automated fact-checking systems in real-world scenarios must compare claims with retrieved evidence to predict the veracity. The retrieved evidence may not unambiguously support or refute the claim and yield diverse valid interpretations. Existing fact-checking datasets necessitate that models predict a single veracity label for each claim and lack the ability to manage such ambiguity. We present AmbiFC, a large-scale fact-checking dataset with realistic claims derived from real-world information needs. Our dataset contains fine-grained evidence annotations of passages from complete Wikipedia pages. We thoroughly analyze disagreements arising from ambiguous claims in AmbiFC, observing a strong correlation of annotator disagreement with their self-assessment and expert-annotated linguistic phenomena. We introduce the task of evidence-based fact-checking for ambiguous claims with soft labels, and compare three methodologies incorporating annotation signals with a single-label classification approach. We find that a pipeline with annotation distillation for sentence-level evidence selection and veracity prediction yields the best performance. Models trained on ambiguous instances exhibit improved performance dealing with the identified linguistic categories, and acquire an understanding of nuanced differences among evidence sentences associated with diverse veracity interpretations.},
	urldate = {2023-09-13},
	author = {Glockner, Max and Staliūnaitė, Ieva and Thorne, James and Vallejo, Gisela and Vlachos, Andreas and Gurevych, Iryna},
	month = apr,
	year = {2021},
	note = {Publication Title: arXiv e-prints
ADS Bibcode: 2021arXiv210400640G},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

@misc{thorne_fever_2018,
	title = {{FEVER}: a large-scale dataset for {Fact} {Extraction} and {VERification}},
	shorttitle = {{FEVER}},
	url = {http://arxiv.org/abs/1803.05355},
	doi = {10.48550/arXiv.1803.05355},
	abstract = {In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. It consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as Supported, Refuted or NotEnoughInfo by annotators achieving 0.6841 in Fleiss \${\textbackslash}kappa\$. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment. To characterize the challenge of the dataset presented, we develop a pipeline approach and compare it to suitably designed oracles. The best accuracy we achieve on labeling a claim accompanied by the correct evidence is 31.87\%, while if we ignore the evidence we achieve 50.91\%. Thus we believe that FEVER is a challenging testbed that will help stimulate progress on claim verification against textual sources.},
	urldate = {2023-09-13},
	publisher = {arXiv},
	author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit},
	month = dec,
	year = {2018},
	note = {arXiv:1803.05355 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{kalyan_ammus_2021,
	title = {{AMMUS} : {A} {Survey} of {Transformer}-based {Pretrained} {Models} in {Natural} {Language} {Processing}},
	shorttitle = {{AMMUS}},
	url = {http://arxiv.org/abs/2108.05542},
	doi = {10.48550/arXiv.2108.05542},
	abstract = {Transformer-based pretrained language models (T-PTLMs) have achieved great success in almost every NLP task. The evolution of these models started with GPT and BERT. These models are built on the top of transformers, self-supervised learning and transfer learning. Transformed-based PTLMs learn universal language representations from large volumes of text data using self-supervised learning and transfer this knowledge to downstream tasks. These models provide good background knowledge to downstream tasks which avoids training of downstream models from scratch. In this comprehensive survey paper, we initially give a brief overview of self-supervised learning. Next, we explain various core concepts like pretraining, pretraining methods, pretraining tasks, embeddings and downstream adaptation methods. Next, we present a new taxonomy of T-PTLMs and then give brief overview of various benchmarks including both intrinsic and extrinsic. We present a summary of various useful libraries to work with T-PTLMs. Finally, we highlight some of the future research directions which will further improve these models. We strongly believe that this comprehensive survey paper will serve as a good reference to learn the core concepts as well as to stay updated with the recent happenings in T-PTLMs.},
	urldate = {2023-09-13},
	publisher = {arXiv},
	author = {Kalyan, Katikapalli Subramanyam and Rajasekharan, Ajit and Sangeetha, Sivanesan},
	month = aug,
	year = {2021},
	note = {arXiv:2108.05542 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{devlin_bert_2019,
	title = {{BERT}: {Pre}-training of {Deep} {Bidirectional} {Transformers} for {Language} {Understanding}},
	shorttitle = {{BERT}},
	url = {http://arxiv.org/abs/1810.04805},
	doi = {10.48550/arXiv.1810.04805},
	abstract = {We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5\% (7.7\% point absolute improvement), MultiNLI accuracy to 86.7\% (4.6\% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).},
	urldate = {2023-04-28},
	publisher = {arXiv},
	author = {Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
	month = may,
	year = {2019},
	note = {arXiv:1810.04805 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{pimentel_surprisal--duration_2021,
	title = {A surprisal--duration trade-off across and within the world's languages},
	url = {http://arxiv.org/abs/2109.15000},
	abstract = {While there exist scores of natural languages, each with its unique features and idiosyncrasies, they all share a unifying theme: enabling human communication. We may thus reasonably predict that human cognition shapes how these languages evolve and are used. Assuming that the capacity to process information is roughly constant across human populations, we expect a surprisal--duration trade-off to arise both across and within languages. We analyse this trade-off using a corpus of 600 languages and, after controlling for several potential confounds, we find strong supporting evidence in both settings. Specifically, we find that, on average, phones are produced faster in languages where they are less surprising, and vice versa. Further, we confirm that more surprising phones are longer, on average, in 319 languages out of the 600. We thus conclude that there is strong evidence of a surprisal--duration trade-off in operation, both across and within the world's languages.},
	urldate = {2023-04-05},
	publisher = {arXiv},
	author = {Pimentel, Tiago and Meister, Clara and Salesky, Elizabeth and Teufel, Simone and Blasi, Damián and Cotterell, Ryan},
	month = sep,
	year = {2021},
	note = {arXiv:2109.15000 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@article{deandra_probiotics_2023,
	title = {Probiotics and metabolites regulate the oral and gut microbiome composition as host modulation agents in periodontitis: {A} narrative review},
	volume = {9},
	issn = {24058440},
	shorttitle = {Probiotics and metabolites regulate the oral and gut microbiome composition as host modulation agents in periodontitis},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2405844023006825},
	doi = {10.1016/j.heliyon.2023.e13475},
	abstract = {Periodontitis is defined as an oral bacterial dysbiosis-induced persistent inflammation on dental supporting tissue resulting in periodontal tissue breakdown and alveolar bone destruction. The disease is initiated by the interaction between periodontopathogens and the host immune system. Its development and severity can be associated with several systemic diseases, such as cardio­ vascular disease (CVD), diabetes mellitus, and rheumatoid arthritis (RA). Moreover, the latest research has suggested that the oral and gut microbiome hypothesis lays the oral and systemic connection mechanism. Bacterial homeostasis and restoration in the oral cavity and intestine become therapeutics concepts. Concerning the treatment of periodontitis, a local inflammatory condition, prolonged systemic administration of antibiotics is no longer recommended due to bacterial resistance issues. Probiotics and several bioactive metabolites have been widely inves­ tigated to address the needs of host modulation therapy in periodontitis. Evidence suggests that the use of probiotics helps downregulate the inflammation process through the regulation of tolllike receptor 4 (TLR4) and the production of fatty acid, targeting reactive oxygen species (ROS). In brief, several herbals have anti-inflammatory properties by inhibiting pro-inflammatory cy­ tokines and mediators, including mitogen-activated protein kinase (MAPK) and nuclear factor kappa B (NF-κB). Consistently, improvement of periodontal pocket depth (PPD) and gingival index (GI) was seen in a group given melatonin as an adjunct treatment. In all, this review will highlight host modulation agents regarding periodontitis therapy, plausible mechanisms on how probiotics and metabolites work on periodontal restoration, and their reported studies. Limita­ tions given by published studies will be elaborated, while future directions will be proposed.},
	language = {en},
	number = {2},
	urldate = {2023-02-27},
	journal = {Heliyon},
	author = {Deandra, Fathia Agzarine and Ketherin, Ketherin and Rachmasari, Rieska and Sulijaya, Benso and Takahashi, Naoki},
	month = feb,
	year = {2023},
	pages = {e13475},
}

@article{wei_determining_2021,
	title = {Determining {Gut} {Microbial} {Dysbiosis}: a {Review} of {Applied} {Indexes} for {Assessment} of {Intestinal} {Microbiota} {Imbalances}},
	volume = {87},
	issn = {1098-5336},
	shorttitle = {Determining {Gut} {Microbial} {Dysbiosis}},
	doi = {10.1128/AEM.00395-21},
	abstract = {Assessing "dysbiosis" in intestinal microbial communities is increasingly considered a routine analysis in microbiota studies, and it has added relevant information to the prediction and characterization of diseases and other adverse conditions. However, dysbiosis is not a well-defined condition. A variety of different dysbiosis indexes have been suggested and applied, but their underlying methodologies, as well as the cohorts and conditions for which they have been developed, differ considerably. To date, no comprehensive overview and comparison of all the different methodologies and applications of such indexes is available. Here, we list all types of dysbiosis indexes identified in the literature, introduce their methodology, group them into categories, and discuss their potential descriptive and clinical applications as well as their limitations. Thus, our focus is not on the implications of dysbiosis for disease but on the methodological approaches available to determine and quantify this condition.},
	language = {eng},
	number = {11},
	journal = {Applied and Environmental Microbiology},
	author = {Wei, Shaodong and Bahl, Martin Iain and Baunwall, Simon Mark Dahl and Hvas, Christian Lodberg and Licht, Tine Rask},
	month = may,
	year = {2021},
	pmid = {33741632},
	pmcid = {PMC8208139},
	keywords = {Diagnostic Tests, Routine, Dysbiosis, Gastrointestinal Microbiome, Humans, dysbiosis, dysbiosis index, gut, imbalance, intestine, microbiome, microbiota},
	pages = {e00395--21},
}

@article{nickles_comparison_2017,
	title = {Comparison of {Two} {Different} {Sampling} {Methods} for {Subgingival} {Plaque}: {Subgingival} {Paper} {Points} or {Mouthrinse} {Sample}?},
	volume = {88},
	issn = {1943-3670},
	shorttitle = {Comparison of {Two} {Different} {Sampling} {Methods} for {Subgingival} {Plaque}},
	doi = {10.1902/jop.2016.160249},
	abstract = {BACKGROUND: The collection of subgingival plaque samples with paper points is time-consuming and accident-sensitive. However, the collection of saliva is simple and contains pathogens of all intraoral surfaces. The aim of this study is to investigate whether a sampling strategy with mouthrinse (mouthrinse sample [MSP]; test) leads to results comparable with standard sampling method (multiple site test from the deepest pocket of each quadrant [MT4]; control).
METHODS: In 50 patients with periodontitis, subgingival plaque was sampled from the deepest pocket of each quadrant by using paper points and by gaining saliva with saline mouthrinse. Analysis was performed using a commercially available polymerase chain reaction test for 11 periodontal pathogens.
RESULTS: Detection frequency of Aggregatibacter actinomycetemcomitans (MT4/MSP: 42\%/36\%), Porphyromonas gingivalis (78\%/66\%), Tannerella forsythia (98\%/84\%), Treponema denticola (94\%/74\%), Parvimonas micra (86\%/62\%), Campylobacter rectus (90\%/76\%), Eubacterium nodatum (64\%/30\%), Prevotella intermedia (58\%/54\%), and Eikenella corrodens (90\%/82\%) was higher with MT4 than MSP. For Fusobacterium nucleatum (100\%/100\%), there was no difference between test and control. Only detection frequency of Capnocytophaga species (68\%/74\%) was higher with MSP than MT4. Differences were significant for P. gingivalis, T. forsythia, T. denticola, P. micra, C. rectus, and E. nodatum.
CONCLUSIONS: There is no significant difference between MT4 and MSP for detection frequency of key pathogen A. actinomycetemcomitans. Key pathogens P. gingivalis, T. forsythia, T. denticola, P. micra, C. rectus, and E. nodatum show statistically higher detection frequencies with MT4.},
	language = {eng},
	number = {4},
	journal = {Journal of Periodontology},
	author = {Nickles, Katrin and Scharf, Susanne and Röllke, Lasse and Dannewitz, Bettina and Eickholz, Peter},
	month = apr,
	year = {2017},
	pmid = {27786617},
	keywords = {Adolescent, Adult, Aged, Aggregatibacter actinomycetemcomitans, Bacteria, Dental Plaque, Female, Humans, Male, Middle Aged, Mouthwashes, Periodontitis, Polymerase Chain Reaction, Porphyromonas gingivalis, Saliva, aggressive periodontitis, chronic periodontitis, polymerase chain reaction, saliva},
	pages = {399--406},
}

@article{casey_chen_oral_2018,
	title = {Oral microbiota of periodontal health and disease and their changes after nonsurgical periodontal therapy},
	volume = {12},
	copyright = {2018 The Author(s)},
	issn = {1751-7370},
	url = {https://www.nature.com/articles/s41396-017-0037-1/},
	doi = {10.1038/s41396-017-0037-1},
	abstract = {This study examined the microbial diversity and community assembly of oral microbiota in periodontal health and disease and after nonsurgical periodontal treatment. The V4 region of 16S rRNA gene from DNA of 238 saliva and subgingival samples of 21 healthy and 48 diseased subjects was amplified and sequenced. Among 1979 OTUs identified, 28 were overabundant in diseased plaque. Six of these taxa were also overabundant in diseased saliva. Twelve OTUs were overabundant in healthy plaque. There was a trend for disease-associated taxa to decrease and health-associated taxa to increase after treatment with notable variations among individual sites. Network analysis revealed modularity of the microbial communities and identified several health- and disease-specific modules. Ecological drift was a major factor that governed community turnovers in both plaque and saliva. Dispersal limitation and homogeneous selection affected the community assembly in plaque, with the additional contribution of homogenizing dispersal for plaque within individuals. Homogeneous selection and dispersal limitation played important roles, respectively, in healthy saliva and diseased pre-treatment saliva between individuals. Our results revealed distinctions in both taxa and assembly processes of oral microbiota between periodontal health and disease. Furthermore, the community assembly analysis has identified potentially effective approaches for managing periodontitis.},
	language = {en},
	number = {5},
	urldate = {2023-02-23},
	journal = {The ISME Journal},
	author = {{Casey Chen} and Hemme, Chris and Beleno, Joan and Shi, Zhou Jason and Ning, Daliang and Qin, Yujia and Tu, Qichao and Jorgensen, Michael and He, Zhili and Wu, Liyou and Zhou, Jizhong},
	month = may,
	year = {2018},
	note = {Number: 5
Publisher: Nature Publishing Group},
	keywords = {Infectious diseases, Microbial ecology},
	pages = {1210--1224},
}

@article{di_stefano_impact_2022,
	title = {Impact of {Oral} {Microbiome} in {Periodontal} {Health} and {Periodontitis}: {A} {Critical} {Review} on {Prevention} and {Treatment}},
	volume = {23},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {1422-0067},
	shorttitle = {Impact of {Oral} {Microbiome} in {Periodontal} {Health} and {Periodontitis}},
	url = {https://www.mdpi.com/1422-0067/23/9/5142},
	doi = {10.3390/ijms23095142},
	abstract = {The skin, oral cavity, digestive and reproductive tracts of the human body harbor symbiotic and commensal microorganisms living harmoniously with the host. The oral cavity houses one of the most heterogeneous microbial communities found in the human organism, ranking second in terms of species diversity and complexity only to the gastrointestinal microbiota and including bacteria, archaea, fungi, and viruses. The accumulation of microbial plaque in the oral cavity may lead, in susceptible individuals, to a complex host-mediated inflammatory and immune response representing the primary etiological factor of periodontal damage that occurs in periodontitis. Periodontal disease is a chronic inflammatory condition affecting about 20–50\% of people worldwide and manifesting clinically through the detection of gingival inflammation, clinical attachment loss (CAL), radiographic assessed resorption of alveolar bone, periodontal pockets, gingival bleeding upon probing, teeth mobility and their potential loss in advanced stages. This review will evaluate the changes characterizing the oral microbiota in healthy periodontal tissues and those affected by periodontal disease through the evidence present in the literature. An important focus will be placed on the immediate and future impact of these changes on the modulation of the dysbiotic oral microbiome and clinical management of periodontal disease.},
	language = {en},
	number = {9},
	urldate = {2023-02-23},
	journal = {International Journal of Molecular Sciences},
	author = {Di Stefano, Mattia and Polizzi, Alessandro and Santonocito, Simona and Romano, Alessandra and Lombardi, Teresa and Isola, Gaetano},
	month = jan,
	year = {2022},
	note = {Number: 9
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {Microbiome, Periodontal Health, Periodontal defects, dysbiosis, oral disease, oral health, oral microbiome, periodontal disease, periodontal treatment, periodontitis},
	pages = {5142},
}

@article{revilla-leon_artificial_2022,
	title = {Artificial intelligence models for diagnosing gingivitis and periodontal disease: {A} systematic review},
	issn = {0022-3913},
	shorttitle = {Artificial intelligence models for diagnosing gingivitis and periodontal disease},
	url = {https://www.sciencedirect.com/science/article/pii/S0022391322000750},
	doi = {10.1016/j.prosdent.2022.01.026},
	abstract = {Statement of problem
Artificial intelligence (AI) models have been developed for periodontal applications, including diagnosing gingivitis and periodontal disease, but their accuracy and maturity of the technology remain unclear.
Purpose
The purpose of this systematic review was to evaluate the performance of the AI models for detecting dental plaque and diagnosing gingivitis and periodontal disease.
Material and methods
A review was performed in 4 databases: MEDLINE/PubMed, World of Science, Cochrane, and Scopus. A manual search was also conducted. Studies were classified into 4 groups: detecting dental plaque, diagnosis of gingivitis, diagnosis of periodontal disease from intraoral images, and diagnosis of alveolar bone loss from periapical, bitewing, and panoramic radiographs. Two investigators evaluated the studies independently by applying the Joanna Briggs Institute critical appraisal. A third examiner was consulted to resolve any lack of consensus.
Results
Twenty-four articles were included: 2 studies developed AI models for detecting plaque, resulting in accuracy ranging from 73.6\% to 99\%; 7 studies assessed the ability to diagnose gingivitis from intraoral photographs reporting an accuracy between 74\% and 78.20\%; 1 study used fluorescent intraoral images to diagnose gingivitis reporting 67.7\% to 73.72\% accuracy; 3 studies assessed the ability to diagnose periodontal disease from intraoral photographs with an accuracy between 47\% and 81\%, and 11 studies evaluated the performance of AI models for detecting alveolar bone loss from radiographic images reporting an accuracy between 73.4\% and 99\%.
Conclusions
AI models for periodontology applications are still in development but might provide a powerful diagnostic tool.},
	language = {en},
	urldate = {2023-02-23},
	journal = {The Journal of Prosthetic Dentistry},
	author = {Revilla-León, Marta and Gómez-Polo, Miguel and Barmak, Abdul B. and Inam, Wardah and Kan, Joseph Y. K. and Kois, John C. and Akal, Orhan},
	month = mar,
	year = {2022},
}

@article{iroha_molecular_2022,
	title = {Molecular characterization of {Enterobacteriacea} isolated from gingivitis and periodontitis patients and the antimicrobial activity of mouth wash agents},
	volume = {15},
	issn = {2468-2276},
	url = {https://www.sciencedirect.com/science/article/pii/S2468227622000151},
	doi = {10.1016/j.sciaf.2022.e01106},
	abstract = {Enterobacteriaceae communities in gingival crevice grow as biofilms (plaque) on teeth and gum surfaces in periodontal pockets; and are implicated in chronic periodontitis due to periodontal therapy failure. This study was designed to determine the frequency of antibiotic resistant genes in Enterobacteriaceae isolated from gingivitis and chronic periodontitis patients visiting dental clinics (FCDT \& T) in Enugu, and the antibacterial activity of some commercially-produced mouth washing agents. A total of 178 clinical enterobacteria species previously isolated and characterized from gingivitis and periodontitis patients comprising K. pneumoniae (n=65), E. coli (n=44), Salmonella species (n=35) and K. oxytoca (n=34) were collected. Isolated bacteria were re-characterized using standard microbiology procedures which include culturing, Gram staining, biochemical test, and sugar fermentation test. Antibacterial activity of selected brandsof mouth wash agents namely, Oral B, Pearl drop smoker, Dentyl active, and Listerine, were assessed by agar well diffusion assay. Screening for the presence of blaCTX-M, blaSHV, and blaOXA resistance genes was done by polymerase chain reaction (PCR). The mouth wash agents used in this study had antibacterial activity against isolated enterobacteria species at various concentrations with the highest inhibitory zone diameter (IZD) of 23 mm recorded for Salmonella species. PCR analysis revealed the presence of blaCTX-M (100 \%), blaOXA (100 \%), and blaSHV (92.4 \%) genes respectively in the isolated enterobacteria species. Enterobacteriaceae that harbor different antibiotic-resistant genes were present in periodontitis and gingivitis patients visiting FCDT \& T, Enugu. This might possibly contribute to the destruction of gingival and periodontal tissues, and the spreading of multidrug-resistant bacterial strains in patients, thus making treatment difficult.},
	language = {en},
	urldate = {2023-02-23},
	journal = {Scientific African},
	author = {Iroha, I R and Mohammed, I D and Moses, I B and Ngwu, N J and Uzoeto, H O and Oladimeji, A S and Ikemesit, U P and Onuora, L A and Ewa, N J and Edemekong, C. I.},
	month = mar,
	year = {2022},
	keywords = {Antibiotic resistant genes, Enterobacteria species, Gingivitis, Mouth wash agents, Periodontitis},
	pages = {e01106},
}

@article{lafaurie_differences_2022,
	title = {Differences in the subgingival microbiome according to stage of periodontitis: {A} comparison of two geographic regions},
	volume = {17},
	issn = {1932-6203},
	shorttitle = {Differences in the subgingival microbiome according to stage of periodontitis},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0273523},
	doi = {10.1371/journal.pone.0273523},
	abstract = {No microbiological criteria were included in the 2018 EFP-AAP classification of periodontal diseases that could be used to differentiate between stages and grades. Furthermore, differences in the subgingival microbiome depending on stage and grade have not been established. Sixty subgingival biofilm samples were collected in Spain (n = 30) and Colombia (n = 30) from three distinct patient categories: those with periodontal health/gingivitis (n = 20), those with stage I-II periodontitis (n = 20), and those with stage III-IV periodontitis (n = 20). Patients were evaluated by 16S rRNA gene amplification sequencing. Amplicon sequence variants were used to assign taxonomic categories compared to the Human Oral Microbiome Database (threshold ≥97\% identity). Alpha diversity was established by Shannon and Simpson indices, and principal coordinate analysis, ANOSIM, and PERMANOVA of the UNIFRAC distances were performed using QIIME2. Although differences in the alpha diversity were observed between samples according to country, Filifactor alocis, Peptostreptococcaceae [XI][G-4] bacterium HMT 369, Fretibacterium fastidiosum, Lachnospiraceae [G-8] bacterium HMT 500, Peptostreptococcaceae [XI][G-5] [Eubacterium] saphenum, Peptostreptococcus stomatis, and Tannerella forsythia were associated with periodontitis sites in all stages. However, only F. alocis, Peptostreptococcaceae [XI][G-4] bacterium HMT 369, Peptostreptococcaceae [XI][G-9] [Eubacterium] brachy, Peptostreptococcaceae [XI][G-5] [Eubacterium] saphenum, and Desulfobulbus sp. HMT 041 were consistent in stage III-IV periodontitis in both countries. Porphyromonas gingivalis and Tannerella forsythia were differentially expressed in severe lesions in the countries studied. Although some non-cultivable microorganisms showed differential patterns between the different stages of periodontitis, they were not the same in the two countries evaluated. Further studies using larger samples with advanced next-generation techniques for high-throughput sequencing of phyla and non-cultivable bacteria within the subgingival microbiome could provide more insight into the differences between stages of periodontitis.},
	language = {en},
	number = {8},
	urldate = {2023-02-23},
	journal = {PLOS ONE},
	author = {Lafaurie, Gloria Inés and Neuta, Yineth and Ríos, Rafael and Pacheco-Montealegre, Mauricio and Pianeta, Roquelina and Castillo, Diana Marcela and Herrera, David and Reyes, Jinnethe and Diaz, Lorena and Castillo, Yormaris and Sanz, Mariano and Iniesta, Margarita},
	month = aug,
	year = {2022},
	note = {Publisher: Public Library of Science},
	keywords = {Bacteria, Colombia, Eubacterium, Microbial ecology, Microbiome, Peptostreptococcus, Periodontitis, Spain},
	pages = {e0273523},
}

@article{moore_bacteriology_1983,
	title = {Bacteriology of moderate (chronic) periodontitis in mature adult humans},
	volume = {42},
	issn = {0019-9567},
	doi = {10.1128/iai.42.2.510-515.1983},
	abstract = {A total of 171 taxa was represented among 1,900 bacterial isolates from 60 samples of sites affected with moderate periodontitis in 22 mature adult humans. The composition of the subgingival sulcus flora was statistically significantly different from that of the adjacent supragingival flora and the subgingival flora of 14 people with healthy gingiva, but was not significantly different from that of sulci affected with severe periodontitis in 21 young human adults. The sulcus floras of moderate periodontitis and severe periodontitis shared many of their predominant bacterial species, but there were differences in the relative proportions of some of these species. Similar relationships were found for seven taxa of treponemes that were cultured from the samples.},
	language = {eng},
	number = {2},
	journal = {Infection and Immunity},
	author = {Moore, W. E. and Holdeman, L. V. and Cato, E. P. and Smibert, R. M. and Burmeister, J. A. and Ranney, R. R.},
	month = nov,
	year = {1983},
	pmid = {6642641},
	pmcid = {PMC264458},
	keywords = {Adult, Bacteria, Chronic Disease, Gingiva, Humans, Middle Aged, Periodontitis, Reference Values},
	pages = {510--515},
}

@article{moore_bacteriology_1983-1,
	title = {Bacteriology of moderate (chronic) periodontitis in mature adult humans.},
	volume = {42},
	issn = {0019-9567},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC264458/},
	abstract = {A total of 171 taxa was represented among 1,900 bacterial isolates from 60 samples of sites affected with moderate periodontitis in 22 mature adult humans. The composition of the subgingival sulcus flora was statistically significantly different from that of the adjacent supragingival flora and the subgingival flora of 14 people with healthy gingiva, but was not significantly different from that of sulci affected with severe periodontitis in 21 young human adults. The sulcus floras of moderate periodontitis and severe periodontitis shared many of their predominant bacterial species, but there were differences in the relative proportions of some of these species. Similar relationships were found for seven taxa of treponemes that were cultured from the samples.},
	number = {2},
	urldate = {2023-02-23},
	journal = {Infection and Immunity},
	author = {Moore, W E and Holdeman, L V and Cato, E P and Smibert, R M and Burmeister, J A and Ranney, R R},
	month = nov,
	year = {1983},
	pmid = {6642641},
	pmcid = {PMC264458},
	pages = {510--515},
}

@article{chan_1h-nmr_2016,
	title = {{1H}-{NMR} urinary metabolomic profiling for diagnosis of gastric cancer},
	volume = {114},
	copyright = {2016 The Author(s)},
	issn = {1532-1827},
	url = {https://www.nature.com/articles/bjc2015414},
	doi = {10.1038/bjc.2015.414},
	abstract = {Metabolomics has shown promise in gastric cancer (GC) detection. This research sought to identify whether GC has a unique urinary metabolomic profile compared with benign gastric disease (BN) and healthy (HE) patients.},
	language = {en},
	number = {1},
	urldate = {2023-02-20},
	journal = {British Journal of Cancer},
	author = {Chan, Angela W. and Mercier, Pascal and Schiller, Daniel and Bailey, Robert and Robbins, Sarah and Eurich, Dean T. and Sawyer, Michael B. and Broadhurst, David},
	month = jan,
	year = {2016},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Diagnosis, Gastric cancer, Metabolomics, Tumour biomarkers},
	pages = {59--62},
}

@phdthesis{grant_cognitive_2022,
	title = {Cognitive analyses of machine learning systems},
	url = {https://escholarship.org/uc/item/09r411mq},
	abstract = {Machine learning systems are increasingly a part of human lives, and so it is increasingly important to understand the similarities and differences between human intelligence and machine intelligence. However, as machine learning systems are applied to more complex problem settings, understanding them becomes more challenging, and their performance, correctness, and reliability become increasingly difficult to guarantee. Moreover, "human-level performance" in such settings is often itself not well-defined, as many of the cognitive mechanisms underlying human behavior remain opaque. This dissertation bridges gaps in our understanding of human and machine intelligence using cross-disciplinary insights from cognitive science and machine learning.First, I develop two frameworks that borrow methodologically from cognitive science to identify deviations in the expected behavior of machine learning systems. Second, I forge a connection between a classical approach to building computational models of human cognition, hierarchical modeling, and a recent technique for small-sample learning in machine learning, meta-learning. I use this connection to develop algorithmic improvements to machine learning systems on established benchmarks and in new settings that highlight their inability to come close to human standards. Finally, I argue that machine learning should borrow methodologically from cognitive science, as both are now tasked with studying opaque learning and decision-making systems. I use this perspective to construct a computational model of machine learning systems that allows us to formalize and test hypotheses about how these systems operate.},
	language = {en},
	urldate = {2023-02-15},
	school = {UC Berkeley},
	author = {Grant, Erin Marie},
	year = {2022},
}

@inproceedings{nematzadeh_computational_2015,
	address = {Lisbon, Portugal},
	title = {A {Computational} {Cognitive} {Model} of {Novel} {Word} {Generalization}},
	url = {https://aclanthology.org/D15-1207},
	doi = {10.18653/v1/D15-1207},
	urldate = {2023-02-15},
	booktitle = {Proceedings of the 2015 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Nematzadeh, Aida and Grant, Erin and Stevenson, Suzanne},
	month = sep,
	year = {2015},
	pages = {1795--1804},
}

@article{grant_how_nodate,
	title = {How {Can} {Memory}-{Augmented} {Neural} {Networks} {Pass} a {False}-{Belief} {Task}?},
	abstract = {A question-answering system needs to be able to reason about unobserved causes in order to answer questions of the sort that people face in everyday conversations. Recent neural network models that incorporate explicit memory and attention mechanisms have taken steps towards this capability. However, these models have not been tested in scenarios for which reasoning about the unobservable mental states of other agents is necessary to answer a question. We propose a new set of tasks inspired by the well-known false-belief test to examine how a recent question-answering model performs in situations that require reasoning about latent mental states. We ﬁnd that the model is only successful when the training and test data bear substantial similarity, as it memorizes how to answer speciﬁc questions and cannot reason about the causal relationship between actions and latent mental states. We introduce an extension to the model that explicitly simulates the mental representations of different participants in a reasoning task, and show that this capacity increases the model’s performance on our theory of mind test.},
	language = {en},
	author = {Grant, Erin and Nematzadeh, Aida and Grifﬁths, Thomas L},
}

@inproceedings{burns_exploiting_2018,
	address = {Brussels, Belgium},
	title = {Exploiting {Attention} to {Reveal} {Shortcomings} in {Memory} {Models}},
	url = {https://aclanthology.org/W18-5454},
	doi = {10.18653/v1/W18-5454},
	abstract = {The decision making processes of deep networks are difficult to understand and while their accuracy often improves with increased architectural complexity, so too does their opacity. Practical use of machine learning models, especially for question and answering applications, demands a system that is interpretable. We analyze the attention of a memory network model to reconcile contradictory performance on a challenging question-answering dataset that is inspired by theory-of-mind experiments. We equate success on questions to task classification, which explains not only test-time failures but also how well the model generalizes to new training conditions.},
	urldate = {2023-02-15},
	booktitle = {Proceedings of the 2018 {EMNLP} {Workshop} {BlackboxNLP}: {Analyzing} and {Interpreting} {Neural} {Networks} for {NLP}},
	publisher = {Association for Computational Linguistics},
	author = {Burns, Kaylee and Nematzadeh, Aida and Grant, Erin and Gopnik, Alison and Griffiths, Tom},
	month = nov,
	year = {2018},
	pages = {378--380},
}

@inproceedings{langlois_passive_2021,
	title = {Passive attention in artificial neural networks predicts human visual selectivity},
	volume = {34},
	url = {https://proceedings.neurips.cc/paper/2021/hash/e360367584297ee8d2d5afa709cd440e-Abstract.html},
	abstract = {Developments in machine learning interpretability techniques over the past decade have provided new tools to observe the image regions that are most informative for classification and localization in artificial neural networks (ANNs). Are the same regions similarly informative to human observers? Using data from 79 new experiments and 7,810 participants, we show that passive attention techniques reveal a significant overlap with human visual selectivity estimates derived from 6 distinct behavioral tasks including visual discrimination, spatial localization, recognizability, free-viewing, cued-object search, and saliency search fixations. We find that input visualizations derived from relatively simple ANN architectures probed using guided backpropagation methods are the best predictors of a shared component in the joint variability of the human measures. We validate these correlational results with causal manipulations using recognition experiments. We show that images masked with ANN attention maps were easier for humans to classify than control masks in a speeded recognition experiment. Similarly, we find that recognition performance in the same ANN models was likewise influenced by masking input images using human visual selectivity maps. This work contributes a new approach to evaluating the biological and psychological validity of leading ANNs as models of human vision: by examining their similarities and differences in terms of their visual selectivity to the information contained in images.},
	urldate = {2023-02-15},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Langlois, Thomas and Zhao, Haicheng and Grant, Erin and Dasgupta, Ishita and Griffiths, Tom and Jacoby, Nori},
	year = {2021},
	pages = {27094--27106},
}

@article{nematzadeh_evaluating_2018,
	title = {Evaluating theory of mind in question answering},
	journal = {arXiv preprint arXiv:1808.09352},
	author = {Nematzadeh, Aida and Burns, Kaylee and Grant, Erin and Gopnik, Alison and Griffiths, Thomas L.},
	year = {2018},
}

@misc{tuli_are_2021,
	title = {Are {Convolutional} {Neural} {Networks} or {Transformers} more like human vision?},
	url = {http://arxiv.org/abs/2105.07197},
	abstract = {Modern machine learning models for computer vision exceed humans in accuracy on specific visual recognition tasks, notably on datasets like ImageNet. However, high accuracy can be achieved in many ways. The particular decision function found by a machine learning system is determined not only by the data to which the system is exposed, but also the inductive biases of the model, which are typically harder to characterize. In this work, we follow a recent trend of in-depth behavioral analyses of neural network models that go beyond accuracy as an evaluation metric by looking at patterns of errors. Our focus is on comparing a suite of standard Convolutional Neural Networks (CNNs) and a recently-proposed attention-based network, the Vision Transformer (ViT), which relaxes the translation-invariance constraint of CNNs and therefore represents a model with a weaker set of inductive biases. Attention-based networks have previously been shown to achieve higher accuracy than CNNs on vision tasks, and we demonstrate, using new metrics for examining error consistency with more granularity, that their errors are also more consistent with those of humans. These results have implications both for building more human-like vision models, as well as for understanding visual object recognition in humans.},
	urldate = {2023-02-15},
	publisher = {arXiv},
	author = {Tuli, Shikhar and Dasgupta, Ishita and Grant, Erin and Griffiths, Thomas L.},
	month = jul,
	year = {2021},
	note = {arXiv:2105.07197 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@article{griffiths_doing_2019,
	series = {Artificial {Intelligence}},
	title = {Doing more with less: meta-reasoning and meta-learning in humans and machines},
	volume = {29},
	issn = {2352-1546},
	shorttitle = {Doing more with less},
	url = {https://www.sciencedirect.com/science/article/pii/S2352154618302122},
	doi = {10.1016/j.cobeha.2019.01.005},
	abstract = {Artificial intelligence systems use an increasing amount of computation and data to solve very specific problems. By contrast, human minds solve a wide range of problems using a fixed amount of computation and limited experience. We identify two abilities that we see as crucial to this kind of general intelligence: meta-reasoning (deciding how to allocate computational resources) and meta-learning (modeling the learning environment to make better use of limited data). We summarize the relevant AI literature and relate the resulting ideas to recent work in psychology.},
	language = {en},
	urldate = {2023-02-15},
	journal = {Current Opinion in Behavioral Sciences},
	author = {Griffiths, Thomas L and Callaway, Frederick and Chang, Michael B and Grant, Erin and Krueger, Paul M and Lieder, Falk},
	month = oct,
	year = {2019},
	pages = {24--30},
}

@article{griffiths_doing_2019-1,
	title = {Doing more with less: meta-reasoning and meta-learning in humans and machines},
	volume = {29},
	shorttitle = {Doing more with less},
	journal = {Current Opinion in Behavioral Sciences},
	author = {Griffiths, Thomas L. and Callaway, Frederick and Chang, Michael B. and Grant, Erin and Krueger, Paul M. and Lieder, Falk},
	year = {2019},
	note = {Publisher: Elsevier},
	pages = {24--30},
}

@inproceedings{jerfel_reconciling_2019,
	title = {Reconciling meta-learning and continual learning with online mixtures of tasks},
	volume = {32},
	url = {https://proceedings.neurips.cc/paper/2019/hash/7a9a322cbe0d06a98667fdc5160dc6f8-Abstract.html},
	abstract = {Learning-to-learn or meta-learning leverages data-driven inductive bias to increase the efficiency of learning on a novel task. This approach encounters difficulty when transfer is not advantageous, for instance, when tasks are considerably dissimilar or change over time. We use the connection between gradient-based meta-learning and hierarchical Bayes to propose a Dirichlet process mixture of hierarchical Bayesian models over the parameters of an arbitrary parametric model such as a neural network. In contrast to consolidating inductive biases into a single set of hyperparameters, our approach of task-dependent hyperparameter selection better handles latent distribution shift, as demonstrated on a set of evolving, image-based, few-shot learning benchmarks.},
	urldate = {2023-02-15},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Jerfel, Ghassen and Grant, Erin and Griffiths, Tom and Heller, Katherine A},
	year = {2019},
}

@article{grant_recasting_2018,
	title = {Recasting gradient-based meta-learning as hierarchical bayes},
	journal = {arXiv preprint arXiv:1801.08930},
	author = {Grant, Erin and Finn, Chelsea and Levine, Sergey and Darrell, Trevor and Griffiths, Thomas},
	year = {2018},
}

@article{majewska_cross-lingual_2023,
	title = {Cross-{Lingual} {Dialogue} {Dataset} {Creation} via {Outline}-{Based} {Generation}},
	volume = {11},
	issn = {2307-387X},
	url = {https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00539/114596/Cross-Lingual-Dialogue-Dataset-Creation-via},
	doi = {10.1162/tacl_a_00539},
	abstract = {Multilingual task-oriented dialogue (ToD) facilitates access to services and information for many (communities of) speakers. Nevertheless, its potential is not fully realized, as current multilingual ToD datasets—both for modular and end-to-end modeling—suffer from severe limitations. 1) When created from scratch, they are usually small in scale and fail to cover many possible dialogue flows. 2) Translation-based ToD datasets might lack naturalness and cultural specificity in the target language. In this work, to tackle these limitations we propose a novel outline-based annotation process for multilingual ToD datasets, where domain-specific abstract schemata of dialogue are mapped into natural language outlines. These in turn guide the target language annotators in writing dialogues by providing instructions about each turn’s intents and slots. Through this process we annotate a new large-scale dataset for evaluation of multilingual and cross-lingual ToD systems. Our Cross-lingual Outline-based Dialogue dataset (COD) enables natural language understanding, dialogue state tracking, and end-to-end dialogue evaluation in 4 diverse languages: Arabic, Indonesian, Russian, and Kiswahili. Qualitative and quantitative analyses of COD versus an equivalent translation-based dataset demonstrate improvements in data quality, unlocked by the outline-based approach. Finally, we benchmark a series of state-of-the-art systems for cross-lingual ToD, setting reference scores for future work and demonstrating that COD prevents over-inflated performance, typically met with prior translation-based ToD datasets.},
	language = {en},
	urldate = {2023-02-10},
	journal = {Transactions of the Association for Computational Linguistics},
	author = {Majewska, Olga and Razumovskaia, Evgeniia and Ponti, Edoardo M. and Vulić, Ivan and Korhonen, Anna},
	month = jan,
	year = {2023},
	pages = {139--156},
}

@misc{stanczak_same_2022,
	title = {Same {Neurons}, {Different} {Languages}: {Probing} {Morphosyntax} in {Multilingual} {Pre}-trained {Models}},
	shorttitle = {Same {Neurons}, {Different} {Languages}},
	url = {http://arxiv.org/abs/2205.02023},
	doi = {10.48550/arXiv.2205.02023},
	abstract = {The success of multilingual pre-trained models is underpinned by their ability to learn representations shared by multiple languages even in absence of any explicit supervision. However, it remains unclear how these models learn to generalise across languages. In this work, we conjecture that multilingual pre-trained models can derive language-universal abstractions about grammar. In particular, we investigate whether morphosyntactic information is encoded in the same subset of neurons in different languages. We conduct the first large-scale empirical study over 43 languages and 14 morphosyntactic categories with a state-of-the-art neuron-level probe. Our findings show that the cross-lingual overlap between neurons is significant, but its extent may vary across categories and depends on language proximity and pre-training data size.},
	urldate = {2023-02-10},
	publisher = {arXiv},
	author = {Stańczak, Karolina and Ponti, Edoardo and Hennigen, Lucas Torroba and Cotterell, Ryan and Augenstein, Isabelle},
	month = may,
	year = {2022},
	note = {arXiv:2205.02023 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@inproceedings{jawahar_exploring_2021,
	address = {Online},
	title = {Exploring {Text}-to-{Text} {Transformers} for {English} to {Hinglish} {Machine} {Translation} with {Synthetic} {Code}-{Mixing}},
	url = {https://aclanthology.org/2021.calcs-1.6},
	doi = {10.18653/v1/2021.calcs-1.6},
	abstract = {We describe models focused at the understudied problem of translating between monolingual and code-mixed language pairs. More specifically, we offer a wide range of models that convert monolingual English text into Hinglish (code-mixed Hindi and English). Given the recent success of pretrained language models, we also test the utility of two recent Transformer-based encoder-decoder models (i.e., mT5 and mBART) on the task finding both to work well. Given the paucity of training data for code-mixing, we also propose a dependency-free method for generating code-mixed texts from bilingual distributed representations that we exploit for improving language model performance. In particular, armed with this additional data, we adopt a curriculum learning approach where we first finetune the language models on synthetic data then on gold code-mixed data. We find that, although simple, our synthetic code-mixing method is competitive with (and in some cases is even superior to) several standard methods (backtranslation, method based on equivalence constraint theory) under a diverse set of conditions. Our work shows that the mT5 model, finetuned following the curriculum learning procedure, achieves best translation performance (12.67 BLEU). Our models place first in the overall ranking of the English-Hinglish official shared task.},
	urldate = {2023-02-08},
	booktitle = {Proceedings of the {Fifth} {Workshop} on {Computational} {Approaches} to {Linguistic} {Code}-{Switching}},
	publisher = {Association for Computational Linguistics},
	author = {Jawahar, Ganesh and Nagoudi, El Moatez Billah and Abdul-Mageed, Muhammad and Lakshmanan, V.S., Laks},
	month = jun,
	year = {2021},
	pages = {36--46},
}

@inproceedings{feng_pathologies_2018,
	title = {Pathologies of {Neural} {Models} {Make} {Interpretations} {Difficult}},
	url = {http://arxiv.org/abs/1804.07781},
	doi = {10.18653/v1/D18-1407},
	abstract = {One way to interpret neural model predictions is to highlight the most important input features---for example, a heatmap visualization over the words in an input sentence. In existing interpretation methods for NLP, a word's importance is determined by either input perturbation---measuring the decrease in model confidence when that word is removed---or by the gradient with respect to that word. To understand the limitations of these methods, we use input reduction, which iteratively removes the least important word from the input. This exposes pathological behaviors of neural models: the remaining words appear nonsensical to humans and are not the ones determined as important by interpretation methods. As we confirm with human experiments, the reduced examples lack information to support the prediction of any label, but models still make the same predictions with high confidence. To explain these counterintuitive results, we draw connections to adversarial examples and confidence calibration: pathological behaviors reveal difficulties in interpreting neural models trained with maximum likelihood. To mitigate their deficiencies, we fine-tune the models by encouraging high entropy outputs on reduced examples. Fine-tuned models become more interpretable under input reduction without accuracy loss on regular examples.},
	urldate = {2023-02-07},
	booktitle = {Proceedings of the 2018 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
	author = {Feng, Shi and Wallace, Eric and Grissom II, Alvin and Iyyer, Mohit and Rodriguez, Pedro and Boyd-Graber, Jordan},
	year = {2018},
	note = {arXiv:1804.07781 [cs]},
	keywords = {Computer Science - Computation and Language},
	pages = {3719--3728},
}

@inproceedings{feng_pathologies_2018-1,
	title = {Pathologies of {Neural} {Models} {Make} {Interpretations} {Difficult}},
	url = {http://arxiv.org/abs/1804.07781},
	doi = {10.18653/v1/D18-1407},
	abstract = {One way to interpret neural model predictions is to highlight the most important input features---for example, a heatmap visualization over the words in an input sentence. In existing interpretation methods for NLP, a word's importance is determined by either input perturbation---measuring the decrease in model confidence when that word is removed---or by the gradient with respect to that word. To understand the limitations of these methods, we use input reduction, which iteratively removes the least important word from the input. This exposes pathological behaviors of neural models: the remaining words appear nonsensical to humans and are not the ones determined as important by interpretation methods. As we confirm with human experiments, the reduced examples lack information to support the prediction of any label, but models still make the same predictions with high confidence. To explain these counterintuitive results, we draw connections to adversarial examples and confidence calibration: pathological behaviors reveal difficulties in interpreting neural models trained with maximum likelihood. To mitigate their deficiencies, we fine-tune the models by encouraging high entropy outputs on reduced examples. Fine-tuned models become more interpretable under input reduction without accuracy loss on regular examples.},
	urldate = {2023-02-07},
	booktitle = {Proceedings of the 2018 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
	author = {Feng, Shi and Wallace, Eric and Grissom II, Alvin and Iyyer, Mohit and Rodriguez, Pedro and Boyd-Graber, Jordan},
	year = {2018},
	note = {arXiv:1804.07781 [cs]},
	keywords = {Computer Science - Computation and Language},
	pages = {3719--3728},
}

@inproceedings{ravichander_probing_2021,
	address = {Online},
	title = {Probing the {Probing} {Paradigm}: {Does} {Probing} {Accuracy} {Entail} {Task} {Relevance}?},
	shorttitle = {Probing the {Probing} {Paradigm}},
	url = {https://aclanthology.org/2021.eacl-main.295},
	doi = {10.18653/v1/2021.eacl-main.295},
	abstract = {Although neural models have achieved impressive results on several NLP benchmarks, little is understood about the mechanisms they use to perform language tasks. Thus, much recent attention has been devoted to analyzing the sentence representations learned by neural encoders, through the lens of `probing' tasks. However, to what extent was the information encoded in sentence representations, as discovered through a probe, actually used by the model to perform its task? In this work, we examine this probing paradigm through a case study in Natural Language Inference, showing that models can learn to encode linguistic properties even if they are not needed for the task on which the model was trained. We further identify that pretrained word embeddings play a considerable role in encoding these properties rather than the training task itself, highlighting the importance of careful controls when designing probing experiments. Finally, through a set of controlled synthetic tasks, we demonstrate models can encode these properties considerably above chance-level, even when distributed in the data as random noise, calling into question the interpretation of absolute claims on probing tasks.},
	urldate = {2023-02-06},
	booktitle = {Proceedings of the 16th {Conference} of the {European} {Chapter} of the {Association} for {Computational} {Linguistics}: {Main} {Volume}},
	publisher = {Association for Computational Linguistics},
	author = {Ravichander, Abhilasha and Belinkov, Yonatan and Hovy, Eduard},
	month = apr,
	year = {2021},
	pages = {3363--3377},
}

@inproceedings{vulic_probing_2020,
	address = {Online},
	title = {Probing {Pretrained} {Language} {Models} for {Lexical} {Semantics}},
	url = {https://aclanthology.org/2020.emnlp-main.586},
	doi = {10.18653/v1/2020.emnlp-main.586},
	abstract = {The success of large pretrained language models (LMs) such as BERT and RoBERTa has sparked interest in probing their representations, in order to unveil what types of knowledge they implicitly capture. While prior research focused on morphosyntactic, semantic, and world knowledge, it remains unclear to which extent LMs also derive lexical type-level knowledge from words in context. In this work, we present a systematic empirical analysis across six typologically diverse languages and five different lexical tasks, addressing the following questions: 1) How do different lexical knowledge extraction strategies (monolingual versus multilingual source LM, out-of-context versus in-context encoding, inclusion of special tokens, and layer-wise averaging) impact performance? How consistent are the observed effects across tasks and languages? 2) Is lexical knowledge stored in few parameters, or is it scattered throughout the network? 3) How do these representations fare against traditional static word vectors in lexical tasks 4) Does the lexical information emerging from independently trained monolingual LMs display latent similarities? Our main results indicate patterns and best practices that hold universally, but also point to prominent variations across languages and tasks. Moreover, we validate the claim that lower Transformer layers carry more type-level lexical knowledge, but also show that this knowledge is distributed across multiple layers.},
	urldate = {2023-02-06},
	booktitle = {Proceedings of the 2020 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing} ({EMNLP})},
	publisher = {Association for Computational Linguistics},
	author = {Vulić, Ivan and Ponti, Edoardo Maria and Litschko, Robert and Glavaš, Goran and Korhonen, Anna},
	month = nov,
	year = {2020},
	pages = {7222--7240},
}

@misc{kumar_fine-tuning_2022,
	title = {Fine-{Tuning} can {Distort} {Pretrained} {Features} and {Underperform} {Out}-of-{Distribution}},
	url = {http://arxiv.org/abs/2202.10054},
	doi = {10.48550/arXiv.2202.10054},
	abstract = {When transferring a pretrained model to a downstream task, two popular methods are full fine-tuning (updating all the model parameters) and linear probing (updating only the last linear layer -- the "head"). It is well known that fine-tuning leads to better accuracy in-distribution (ID). However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of-distribution (OOD) when the pretrained features are good and the distribution shift is large. On 10 distribution shift datasets (Breeds-Living17, Breeds-Entity30, DomainNet, CIFAR \${\textbackslash}to\$ STL, CIFAR10.1, FMoW, ImageNetV2, ImageNet-R, ImageNet-A, ImageNet-Sketch), fine-tuning obtains on average 2\% higher accuracy ID but 7\% lower accuracy OOD than linear probing. We show theoretically that this tradeoff between ID and OOD accuracy arises even in a simple setting: fine-tuning overparameterized two-layer linear networks. We prove that the OOD error of fine-tuning is high when we initialize with a fixed or random head -- this is because while fine-tuning learns the head, the lower layers of the neural network change simultaneously and distort the pretrained features. Our analysis suggests that the easy two-step strategy of linear probing then full fine-tuning (LP-FT), sometimes used as a fine-tuning heuristic, combines the benefits of both fine-tuning and linear probing. Empirically, LP-FT outperforms both fine-tuning and linear probing on the above datasets (1\% better ID, 10\% better OOD than full fine-tuning).},
	urldate = {2023-02-06},
	publisher = {arXiv},
	author = {Kumar, Ananya and Raghunathan, Aditi and Jones, Robbie and Ma, Tengyu and Liang, Percy},
	month = feb,
	year = {2022},
	note = {arXiv:2202.10054 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@inproceedings{kumar_fine-tuning_2022-1,
	title = {Fine-{Tuning} can {Distort} {Pretrained} {Features} and {Underperform} {Out}-of-{Distribution}},
	url = {https://openreview.net/forum?id=UYneFzXSJWh},
	abstract = {When transferring a pretrained model to a downstream task, two popular methods are full fine-tuning (updating all the model parameters) and linear probing (updating only the last linear layer---the "head"). It is well known that fine-tuning leads to better accuracy in-distribution (ID). However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of-distribution (OOD) when the pretrained features are good and the distribution shift is large. On 10 distribution shift datasets (BREEDS-Living17, BREEDS-Entity30, DomainNet, CIFAR \${\textbackslash}to\$ STL, CIFAR-10.1, FMoW, ImageNetV2, ImageNet-R, ImageNet-A, ImageNet-Sketch), fine-tuning obtains on average 2\% higher accuracy ID but 7\% lower accuracy OOD than linear probing. We show theoretically that this tradeoff between ID and OOD accuracy arises even in a simple setting: fine-tuning overparameterized two-layer linear networks. We prove that the OOD error of fine-tuning is high when we initialize with a fixed or random head---this is because while fine-tuning learns the head, the lower layers of the neural network change simultaneously and distort the pretrained features. Our analysis suggests that the easy two-step strategy of linear probing then full fine-tuning (LP-FT), sometimes used as a fine-tuning heuristic, combines the benefits of both fine-tuning and linear probing. Empirically, LP-FT outperforms both fine-tuning and linear probing on the above datasets (1\% better ID, 10\% better OOD than full fine-tuning).},
	language = {en},
	urldate = {2023-02-06},
	author = {Kumar, Ananya and Raghunathan, Aditi and Jones, Robbie Matthew and Ma, Tengyu and Liang, Percy},
	month = mar,
	year = {2022},
}

@misc{kalyan_ammus_2021-1,
	title = {{AMMUS} : {A} {Survey} of {Transformer}-based {Pretrained} {Models} in {Natural} {Language} {Processing}},
	shorttitle = {{AMMUS}},
	url = {http://arxiv.org/abs/2108.05542},
	doi = {10.48550/arXiv.2108.05542},
	abstract = {Transformer-based pretrained language models (T-PTLMs) have achieved great success in almost every NLP task. The evolution of these models started with GPT and BERT. These models are built on the top of transformers, self-supervised learning and transfer learning. Transformed-based PTLMs learn universal language representations from large volumes of text data using self-supervised learning and transfer this knowledge to downstream tasks. These models provide good background knowledge to downstream tasks which avoids training of downstream models from scratch. In this comprehensive survey paper, we initially give a brief overview of self-supervised learning. Next, we explain various core concepts like pretraining, pretraining methods, pretraining tasks, embeddings and downstream adaptation methods. Next, we present a new taxonomy of T-PTLMs and then give brief overview of various benchmarks including both intrinsic and extrinsic. We present a summary of various useful libraries to work with T-PTLMs. Finally, we highlight some of the future research directions which will further improve these models. We strongly believe that this comprehensive survey paper will serve as a good reference to learn the core concepts as well as to stay updated with the recent happenings in T-PTLMs.},
	urldate = {2023-02-05},
	publisher = {arXiv},
	author = {Kalyan, Katikapalli Subramanyam and Rajasekharan, Ajit and Sangeetha, Sivanesan},
	month = aug,
	year = {2021},
	note = {arXiv:2108.05542 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{stefanik_what_2022,
	title = {What is {Not} in the {Context}? {Evaluation} of {Few}-shot {Learners} with {Informative} {Demonstrations}},
	shorttitle = {What is {Not} in the {Context}?},
	url = {http://arxiv.org/abs/2212.01692},
	doi = {10.48550/arXiv.2212.01692},
	abstract = {Large language models demonstrate an emergent ability to learn a new task from a small number of input-output demonstrations, referred to as in-context few-shot learning. However, recent work shows that in such settings, models mainly learn to mimic the new task distribution, instead of the mechanics of the new task. We argue that the commonly-used evaluation settings of few-shot models utilizing a random selection of in-context demonstrations is not able to disentangle models' ability to learn new skills from demonstrations, as most of the such-selected demonstrations are not informative for prediction beyond exposing the new task's input and output distribution. Therefore, we introduce an evaluation technique that disentangles few-shot learners' gain from in-context learning by picking the demonstrations sharing a specific, informative concept with the predicted sample, in addition to the performance reached by mainly non-informative samples. We find that regardless of the model size, existing few-shot learners are not able to benefit from observing such informative concepts in demonstrations. We also find that such ability may not be obtained trivially by exposing the informative demonstrations in the training process, leaving the challenge of training true in-context learners open.},
	urldate = {2023-02-05},
	publisher = {arXiv},
	author = {Štefánik, Michal and Kadlčík, Marek},
	month = dec,
	year = {2022},
	note = {arXiv:2212.01692 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@inproceedings{yang_cspcode-switching_2020,
	address = {Online},
	title = {{CSP}:{Code}-{Switching} {Pre}-training for {Neural} {Machine} {Translation}},
	shorttitle = {{CSP}},
	url = {https://www.aclweb.org/anthology/2020.emnlp-main.208},
	doi = {10.18653/v1/2020.emnlp-main.208},
	abstract = {This paper proposes a new pre-training method, called Code-Switching Pre-training (CSP for short) for Neural Machine Translation (NMT). Unlike traditional pre-training method which randomly masks some fragments of the input sentence, the proposed CSP randomly replaces some words in the source sentence with their translation words in the target language. Specifically, we firstly perform lexicon induction with unsupervised word embedding mapping between the source and target languages, and then randomly replace some words in the input sentence with their translation words according to the extracted translation lexicons. CSP adopts the encoder-decoder framework: its encoder takes the code-mixed sentence as input, and its decoder predicts the replaced fragment of the input sentence. In this way, CSP is able to pre-train the NMT model by explicitly making the most of the alignment information extracted from the source and target monolingual corpus. Additionally, we relieve the pretrain-finetune discrepancy caused by the artificial symbols like [mask]. To verify the effectiveness of the proposed method, we conduct extensive experiments on unsupervised and supervised NMT. Experimental results show that CSP achieves significant improvements over baselines without pre-training or with other pre-training methods.},
	language = {en},
	urldate = {2023-02-04},
	booktitle = {Proceedings of the 2020 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing} ({EMNLP})},
	publisher = {Association for Computational Linguistics},
	author = {Yang, Zhen and Hu, Bojie and Han, Ambyera and Huang, Shen and Ju, Qi},
	year = {2020},
	pages = {2624--2636},
}

@misc{noauthor_lince_nodate,
	title = {{LinCE} {Benchmark}},
	url = {https://ritual.uh.edu/lince/},
	urldate = {2023-02-04},
}

@misc{dogruoz_survey_2023,
	title = {A {Survey} of {Code}-switching: {Linguistic} and {Social} {Perspectives} for {Language} {Technologies}},
	shorttitle = {A {Survey} of {Code}-switching},
	url = {http://arxiv.org/abs/2301.01967},
	doi = {10.48550/arXiv.2301.01967},
	abstract = {The analysis of data in which multiple languages are represented has gained popularity among computational linguists in recent years. So far, much of this research focuses mainly on the improvement of computational methods and largely ignores linguistic and social aspects of C-S discussed across a wide range of languages within the long-established literature in linguistics. To fill this gap, we offer a survey of code-switching (C-S) covering the literature in linguistics with a reflection on the key issues in language technologies. From the linguistic perspective, we provide an overview of structural and functional patterns of C-S focusing on the literature from European and Indian contexts as highly multilingual areas. From the language technologies perspective, we discuss how massive language models fail to represent diverse C-S types due to lack of appropriate training data, lack of robust evaluation benchmarks for C-S (across multilingual situations and types of C-S) and lack of end-to-end systems that cover sociolinguistic aspects of C-S as well. Our survey will be a step towards an outcome of mutual benefit for computational scientists and linguists with a shared interest in multilingualism and C-S.},
	urldate = {2023-02-04},
	publisher = {arXiv},
	author = {Doğruöz, A. Seza and Sitaram, Sunayana and Bullock, Barbara E. and Toribio, Almeida Jacqueline},
	month = jan,
	year = {2023},
	note = {arXiv:2301.01967 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{liu_mulzdg_2022,
	title = {{MulZDG}: {Multilingual} {Code}-{Switching} {Framework} for {Zero}-shot {Dialogue} {Generation}},
	shorttitle = {{MulZDG}},
	url = {http://arxiv.org/abs/2208.08629},
	doi = {10.48550/arXiv.2208.08629},
	abstract = {Building dialogue generation systems in a zero-shot scenario remains a huge challenge, since the typical zero-shot approaches in dialogue generation rely heavily on large-scale pre-trained language generation models such as GPT-3 and T5. The research on zero-shot dialogue generation without cumbersome language models is limited due to lacking corresponding parallel dialogue corpora. In this paper, we propose a simple but effective Multilingual learning framework for Zero-shot Dialogue Generation (dubbed as MulZDG) that can effectively transfer knowledge from an English corpus with large-scale training samples to a non-English corpus with zero samples. Besides, MulZDG can be viewed as a multilingual data augmentation method to improve the performance of the resource-rich language. First, we construct multilingual code-switching dialogue datasets via translation utterances randomly selected from monolingual English datasets. Then we employ MulZDG to train a unified multilingual dialogue model based on the code-switching datasets. The MulZDG can conduct implicit semantic alignment between different languages. Experiments on DailyDialog and DSTC7 datasets demonstrate that MulZDG not only achieve competitive performance under zero-shot case compared to training with sufficient examples but also greatly improve the performance of the source language.},
	urldate = {2023-02-04},
	publisher = {arXiv},
	author = {Liu, Yongkang and Feng, Shi and Wang, Daling and Zhang, Yifei},
	month = aug,
	year = {2022},
	note = {arXiv:2208.08629 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{winata_are_2021,
	title = {Are {Multilingual} {Models} {Effective} in {Code}-{Switching}?},
	url = {http://arxiv.org/abs/2103.13309},
	doi = {10.48550/arXiv.2103.13309},
	abstract = {Multilingual language models have shown decent performance in multilingual and cross-lingual natural language understanding tasks. However, the power of these multilingual models in code-switching tasks has not been fully explored. In this paper, we study the effectiveness of multilingual language models to understand their capability and adaptability to the mixed-language setting by considering the inference speed, performance, and number of parameters to measure their practicality. We conduct experiments in three language pairs on named entity recognition and part-of-speech tagging and compare them with existing methods, such as using bilingual embeddings and multilingual meta-embeddings. Our findings suggest that pre-trained multilingual models do not necessarily guarantee high-quality representations on code-switching, while using meta-embeddings achieves similar results with significantly fewer parameters.},
	urldate = {2023-02-04},
	publisher = {arXiv},
	author = {Winata, Genta Indra and Cahyawijaya, Samuel and Liu, Zihan and Lin, Zhaojiang and Madotto, Andrea and Fung, Pascale},
	month = mar,
	year = {2021},
	note = {arXiv:2103.13309 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{trivedi_teaching_2022,
	title = {Teaching {Broad} {Reasoning} {Skills} for {Multi}-{Step} {QA} by {Generating} {Hard} {Contexts}},
	url = {http://arxiv.org/abs/2205.12496},
	doi = {10.48550/arXiv.2205.12496},
	abstract = {Question-answering datasets require a broad set of reasoning skills. We show how to use question decompositions to teach language models these broad reasoning skills in a robust fashion. Specifically, we use widely available QDMR representations to programmatically create hard-to-cheat synthetic contexts for real questions in six multi-step reasoning datasets. These contexts are carefully designed to avoid reasoning shortcuts prevalent in real contexts that prevent models from learning the right skills. This results in a pretraining dataset, named TeaBReaC, containing 525K multi-step questions (with associated formal programs) covering about 900 reasoning patterns. We show that pretraining standard language models (LMs) on TeaBReaC before fine-tuning them on target datasets improves their performance by up to 13 F1 points across 4 multi-step QA datasets, with up to 21 point gain on more complex questions. The resulting models also demonstrate higher robustness, with a 5-8 F1 point improvement on two contrast sets. Furthermore, TeaBReaC pretraining substantially improves model performance and robustness even when starting with numerate LMs pretrained using recent methods (e.g., PReasM, POET). Our work thus shows how to effectively use decomposition-guided contexts to robustly teach multi-step reasoning.},
	urldate = {2023-02-04},
	publisher = {arXiv},
	author = {Trivedi, Harsh and Balasubramanian, Niranjan and Khot, Tushar and Sabharwal, Ashish},
	month = nov,
	year = {2022},
	note = {arXiv:2205.12496 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

@misc{brown_language_2020,
	title = {Language {Models} are {Few}-{Shot} {Learners}},
	url = {http://arxiv.org/abs/2005.14165},
	doi = {10.48550/arXiv.2005.14165},
	abstract = {Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general.},
	urldate = {2023-02-04},
	publisher = {arXiv},
	author = {Brown, Tom B. and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel M. and Wu, Jeffrey and Winter, Clemens and Hesse, Christopher and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario},
	month = jul,
	year = {2020},
	note = {arXiv:2005.14165 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{lu_fantastically_2022,
	title = {Fantastically {Ordered} {Prompts} and {Where} to {Find} {Them}: {Overcoming} {Few}-{Shot} {Prompt} {Order} {Sensitivity}},
	shorttitle = {Fantastically {Ordered} {Prompts} and {Where} to {Find} {Them}},
	url = {http://arxiv.org/abs/2104.08786},
	doi = {10.48550/arXiv.2104.08786},
	abstract = {When primed with only a handful of training samples, very large, pretrained language models such as GPT-3 have shown competitive results when compared to fully-supervised, fine-tuned, large, pretrained language models. We demonstrate that the order in which the samples are provided can make the difference between near state-of-the-art and random guess performance: essentially some permutations are "fantastic" and some not. We analyse this phenomenon in detail, establishing that: it is present across model sizes (even for the largest current models), it is not related to a specific subset of samples, and that a given good permutation for one model is not transferable to another. While one could use a development set to determine which permutations are performant, this would deviate from the true few-shot setting as it requires additional annotated data. Instead, we use the generative nature of language models to construct an artificial development set and based on entropy statistics of the candidate permutations on this set, we identify performant prompts. Our method yields a 13\% relative improvement for GPT-family models across eleven different established text classification tasks.},
	urldate = {2023-02-04},
	publisher = {arXiv},
	author = {Lu, Yao and Bartolo, Max and Moore, Alastair and Riedel, Sebastian and Stenetorp, Pontus},
	month = mar,
	year = {2022},
	note = {arXiv:2104.08786 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

@misc{liu_what_2021,
	title = {What {Makes} {Good} {In}-{Context} {Examples} for {GPT}-\$3\$?},
	url = {http://arxiv.org/abs/2101.06804},
	doi = {10.48550/arXiv.2101.06804},
	abstract = {GPT-\$3\$ has attracted lots of attention due to its superior performance across a wide range of NLP tasks, especially with its powerful and versatile in-context few-shot learning ability. Despite its success, we found that the empirical results of GPT-\$3\$ depend heavily on the choice of in-context examples. In this work, we investigate whether there are more effective strategies for judiciously selecting in-context examples (relative to random sampling) that better leverage GPT-\$3\$'s few-shot capabilities. Inspired by the recent success of leveraging a retrieval module to augment large-scale neural network models, we propose to retrieve examples that are semantically-similar to a test sample to formulate its corresponding prompt. Intuitively, the in-context examples selected with such a strategy may serve as more informative inputs to unleash GPT-\$3\$'s extensive knowledge. We evaluate the proposed approach on several natural language understanding and generation benchmarks, where the retrieval-based prompt selection approach consistently outperforms the random baseline. Moreover, it is observed that the sentence encoders fine-tuned on task-related datasets yield even more helpful retrieval results. Notably, significant gains are observed on tasks such as table-to-text generation (41.9\% on the ToTTo dataset) and open-domain question answering (45.5\% on the NQ dataset). We hope our investigation could help understand the behaviors of GPT-\$3\$ and large-scale pre-trained LMs in general and enhance their few-shot capabilities.},
	urldate = {2023-02-04},
	publisher = {arXiv},
	author = {Liu, Jiachang and Shen, Dinghan and Zhang, Yizhe and Dolan, Bill and Carin, Lawrence and Chen, Weizhu},
	month = jan,
	year = {2021},
	note = {arXiv:2101.06804 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@inproceedings{winata_cross-lingual_2022,
	address = {Online only},
	title = {Cross-lingual {Few}-{Shot} {Learning} on {Unseen} {Languages}},
	url = {https://aclanthology.org/2022.aacl-main.59},
	abstract = {Large pre-trained language models (LMs) have demonstrated the ability to obtain good performance on downstream tasks with limited examples in cross-lingual settings. However, this was mostly studied for relatively resource-rich languages, where at least enough unlabeled data is available to be included in pre-training a multilingual language model. In this paper, we explore the problem of cross-lingual transfer in unseen languages, where no unlabeled data is available for pre-training a model. We use a downstream sentiment analysis task across 12 languages, including 8 unseen languages, to analyze the effectiveness of several few-shot learning strategies across the three major types of model architectures and their learning dynamics. We also compare strategies for selecting languages for transfer and contrast findings across languages seen in pre-training compared to those that are not. Our findings contribute to the body of knowledge on cross-lingual models for low-resource settings that is paramount to increasing coverage, diversity, and equity in access to NLP technology. We show that, in few-shot learning, linguistically similar and geographically similar languages are useful for cross-lingual adaptation, but taking the context from a mixture of random source languages is surprisingly more effective. We also compare different model architectures and show that the encoder-only model, XLM-R, gives the best downstream task performance.},
	urldate = {2023-02-04},
	booktitle = {Proceedings of the 2nd {Conference} of the {Asia}-{Pacific} {Chapter} of the {Association} for {Computational} {Linguistics} and the 12th {International} {Joint} {Conference} on {Natural} {Language} {Processing} ({Volume} 1: {Long} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Winata, Genta and Wu, Shijie and Kulkarni, Mayank and Solorio, Thamar and Preotiuc-Pietro, Daniel},
	month = nov,
	year = {2022},
	pages = {777--791},
}

@misc{ouyang_training_2022,
	title = {Training language models to follow instructions with human feedback},
	url = {http://arxiv.org/abs/2203.02155},
	doi = {10.48550/arXiv.2203.02155},
	abstract = {Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT. In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent.},
	urldate = {2023-02-04},
	publisher = {arXiv},
	author = {Ouyang, Long and Wu, Jeff and Jiang, Xu and Almeida, Diogo and Wainwright, Carroll L. and Mishkin, Pamela and Zhang, Chong and Agarwal, Sandhini and Slama, Katarina and Ray, Alex and Schulman, John and Hilton, Jacob and Kelton, Fraser and Miller, Luke and Simens, Maddie and Askell, Amanda and Welinder, Peter and Christiano, Paul and Leike, Jan and Lowe, Ryan},
	month = mar,
	year = {2022},
	note = {arXiv:2203.02155 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{muennighoff_crosslingual_2022,
	title = {Crosslingual {Generalization} through {Multitask} {Finetuning}},
	url = {http://arxiv.org/abs/2211.01786},
	doi = {10.48550/arXiv.2211.01786},
	abstract = {Multitask prompted finetuning (MTF) has been shown to help large language models generalize to new tasks in a zero-shot setting, but so far explorations of MTF have focused on English data and models. We apply MTF to the pretrained multilingual BLOOM and mT5 model families to produce finetuned variants called BLOOMZ and mT0. We find finetuning large multilingual language models on English tasks with English prompts allows for task generalization to non-English languages that appear only in the pretraining corpus. Finetuning on multilingual tasks with English prompts further improves performance on English and non-English tasks leading to various state-of-the-art zero-shot results. We also investigate finetuning on multilingual tasks with prompts that have been machine-translated from English to match the language of each dataset. We find training on these machine-translated prompts leads to better performance on human-written prompts in the respective languages. Surprisingly, we find models are capable of zero-shot generalization to tasks in languages they have never intentionally seen. We conjecture that the models are learning higher-level capabilities that are both task- and language-agnostic. In addition, we introduce xP3, a composite of supervised datasets in 46 languages with English and machine-translated prompts. Our code, datasets and models are publicly available at https://github.com/bigscience-workshop/xmtf.},
	urldate = {2023-02-04},
	publisher = {arXiv},
	author = {Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M. Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and Tang, Xiangru and Radev, Dragomir and Aji, Alham Fikri and Almubarak, Khalid and Albanie, Samuel and Alyafeai, Zaid and Webson, Albert and Raff, Edward and Raffel, Colin},
	month = nov,
	year = {2022},
	note = {arXiv:2211.01786 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{davis_probing_2022,
	title = {Probing for targeted syntactic knowledge through grammatical error detection},
	url = {http://arxiv.org/abs/2210.16228},
	doi = {10.48550/arXiv.2210.16228},
	abstract = {Targeted studies testing knowledge of subject-verb agreement (SVA) indicate that pre-trained language models encode syntactic information. We assert that if models robustly encode subject-verb agreement, they should be able to identify when agreement is correct and when it is incorrect. To that end, we propose grammatical error detection as a diagnostic probe to evaluate token-level contextual representations for their knowledge of SVA. We evaluate contextual representations at each layer from five pre-trained English language models: BERT, XLNet, GPT-2, RoBERTa, and ELECTRA. We leverage public annotated training data from both English second language learners and Wikipedia edits, and report results on manually crafted stimuli for subject-verb agreement. We find that masked language models linearly encode information relevant to the detection of SVA errors, while the autoregressive models perform on par with our baseline. However, we also observe a divergence in performance when probes are trained on different training sets, and when they are evaluated on different syntactic constructions, suggesting the information pertaining to SVA error detection is not robustly encoded.},
	urldate = {2023-02-04},
	publisher = {arXiv},
	author = {Davis, Christopher and Bryant, Christopher and Caines, Andrew and Rei, Marek and Buttery, Paula},
	month = oct,
	year = {2022},
	note = {arXiv:2210.16228 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{dong_survey_2022,
	title = {A {Survey} for {In}-context {Learning}},
	url = {http://arxiv.org/abs/2301.00234},
	doi = {10.48550/arXiv.2301.00234},
	abstract = {With the increasing ability of large language models (LLMs), in-context learning (ICL) has become a new paradigm for natural language processing (NLP), where LLMs make predictions only based on contexts augmented with a few training examples. It has been a new trend exploring ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress, challenges, and future work in ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques of ICL, including training strategies, prompting strategies, and so on. Finally, we present the challenges of ICL and provide potential directions for further research. We hope our work can encourage more research on uncovering how ICL works and improving ICL in future work.},
	urldate = {2023-02-01},
	publisher = {arXiv},
	author = {Dong, Qingxiu and Li, Lei and Dai, Damai and Zheng, Ce and Wu, Zhiyong and Chang, Baobao and Sun, Xu and Xu, Jingjing and Li, Lei and Sui, Zhifang},
	month = dec,
	year = {2022},
	note = {arXiv:2301.00234 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

@misc{lu_what_2022,
	title = {What {Makes} {Pre}-trained {Language} {Models} {Better} {Zero}/{Few}-shot {Learners}?},
	url = {http://arxiv.org/abs/2209.15206},
	abstract = {In this paper, we propose a theoretical framework to explain the efficacy of prompt learning in zero/few-shot scenarios. First, we prove that conventional pre-training and fine-tuning paradigm fails in few-shot scenarios due to overfitting the unrepresentative labelled data. We then detail the assumption that prompt learning is more effective because it empowers pre-trained language model that is built upon massive text corpora, as well as domain-related human knowledge to participate more in prediction and thereby reduces the impact of limited label information provided by the small training set. We further hypothesize that language discrepancy can measure the quality of prompting. Comprehensive experiments are performed to verify our assumptions. More remarkably, inspired by the theoretical framework, we propose an annotation-agnostic template selection method based on perplexity, which enables us to ``forecast'' the prompting performance in advance. This approach is especially encouraging because existing work still relies on development set to post-hoc evaluate templates. Experiments show that this method leads to significant prediction benefits compared to state-of-the-art zero-shot methods.},
	urldate = {2023-01-29},
	publisher = {arXiv},
	author = {Lu, Jinghui and Zhao, Rui and Mac Namee, Brian and Zhu, Dongsheng and Han, Weidong and Tan, Fei},
	month = sep,
	year = {2022},
	note = {arXiv:2209.15206 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

@inproceedings{winata_code-switched_2019,
	address = {Hong Kong, China},
	title = {Code-{Switched} {Language} {Models} {Using} {Neural} {Based} {Synthetic} {Data} from {Parallel} {Sentences}},
	url = {https://aclanthology.org/K19-1026},
	doi = {10.18653/v1/K19-1026},
	abstract = {Training code-switched language models is difficult due to lack of data and complexity in the grammatical structure. Linguistic constraint theories have been used for decades to generate artificial code-switching sentences to cope with this issue. However, this require external word alignments or constituency parsers that create erroneous results on distant languages. We propose a sequence-to-sequence model using a copy mechanism to generate code-switching data by leveraging parallel monolingual translations from a limited source of code-switching data. The model learns how to combine words from parallel sentences and identifies when to switch one language to the other. Moreover, it captures code-switching constraints by attending and aligning the words in inputs, without requiring any external knowledge. Based on experimental results, the language model trained with the generated sentences achieves state-of-the-art performance and improves end-to-end automatic speech recognition.},
	urldate = {2023-01-29},
	booktitle = {Proceedings of the 23rd {Conference} on {Computational} {Natural} {Language} {Learning} ({CoNLL})},
	publisher = {Association for Computational Linguistics},
	author = {Winata, Genta Indra and Madotto, Andrea and Wu, Chien-Sheng and Fung, Pascale},
	month = nov,
	year = {2019},
	pages = {271--280},
}

@misc{min_rethinking_2022,
	title = {Rethinking the {Role} of {Demonstrations}: {What} {Makes} {In}-{Context} {Learning} {Work}?},
	shorttitle = {Rethinking the {Role} of {Demonstrations}},
	url = {http://arxiv.org/abs/2202.12837},
	abstract = {Large language models (LMs) are able to in-context learn -- perform a new task via inference alone by conditioning on a few input-label pairs (demonstrations) and making predictions for new inputs. However, there has been little understanding of how the model learns and which aspects of the demonstrations contribute to end task performance. In this paper, we show that ground truth demonstrations are in fact not required -- randomly replacing labels in the demonstrations barely hurts performance on a range of classification and multi-choce tasks, consistently over 12 different models including GPT-3. Instead, we find that other aspects of the demonstrations are the key drivers of end task performance, including the fact that they provide a few examples of (1) the label space, (2) the distribution of the input text, and (3) the overall format of the sequence. Together, our analysis provides a new way of understanding how and why in-context learning works, while opening up new questions about how much can be learned from large language models through inference alone.},
	urldate = {2023-01-24},
	publisher = {arXiv},
	author = {Min, Sewon and Lyu, Xinxi and Holtzman, Ari and Artetxe, Mikel and Lewis, Mike and Hajishirzi, Hannaneh and Zettlemoyer, Luke},
	month = oct,
	year = {2022},
	note = {arXiv:2202.12837 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

@article{noauthor_effectiveness_2021,
	title = {Effectiveness of code-switching in language classroom in {India} at primary level: {A} case of {L2} teachers’ perspectives},
	volume = {11},
	shorttitle = {Effectiveness of code-switching in language classroom in {India} at primary level},
	url = {https://pegegog.net/index.php/pegegog/article/view/1500/467},
	doi = {10.47750/pegegog.11.04.37},
	abstract = {The current study explores the effectiveness of code-switching (CS) in language classroom, a case of second language (L2) teachers’ perspectives. Code-switching (CS) refers to a usage of the two languages in conversation and it also relates to a ‘language mixing’. CS may occur between sentences, known as ‘inter-sentential code-switching’; and it may also occur within a sentence, known as ‘intra-sentential code-switching’. Code-switching is a linguistic feature of multi-lingual societies, as they are gifted with more privileges to use various languages. The current study points out the perceptions of language teachers towards code-switching in the classroom in the process of teaching and the purposes of code-switching in teaching. The data for the study includes the responses of the attitudinal test questions designed on a Likert Scale of 20 teacher- respondents from the two government schools of Delhi-NCR. The outcome of the study illustrates that the predominance of code-switching in the classrooms is used to interpret complex ideas, translate questions, seek confirmation, check students understanding, also to build solidarity and code-switching is most prevalent in primary education. Hence, code-switching is a distinctive linguistic requirement in education but there is a negative towards the use of CS in the classroom.},
	language = {en},
	number = {4},
	urldate = {2023-01-23},
	journal = {Pegem Journal of Education and Instruction},
	month = oct,
	year = {2021},
}

@article{pajak_multilingual_2022,
	title = {Multilingual fine-tuning for {Grammatical} {Error} {Correction}},
	volume = {200},
	issn = {0957-4174},
	url = {https://www.sciencedirect.com/science/article/pii/S0957417422003773},
	doi = {10.1016/j.eswa.2022.116948},
	abstract = {Finding a single model capable of comprehending multiple languages is an area of active research in Natural Language Processing (NLP). Recently developed models such as mBART, mT5 or xProphetNet can solve problems connected with, for instance, machine translation and summarization for many languages. However, good multilingual solutions to the problem of Grammatical Error Correction (GEC) are still missing — this paper aims at filling this gap. We first review current annotated GEC datasets and then apply existing pre-trained multilingual models to correct grammatical errors in multiple languages. In our experiments, we compare how different pre-training approaches impact the final GEC quality. Our result is a single model that can correct seven different languages and is the best (in terms of F-score) currently reported multilingual GEC model. Additionally, our multilingual model achieves better results than the SOTA monolingual model for Romanian.},
	language = {en},
	urldate = {2023-01-23},
	journal = {Expert Systems with Applications},
	author = {Pająk, Krzysztof and Pająk, Dominik},
	month = aug,
	year = {2022},
	keywords = {Grammatical error correction, Multilingual denoising autoencoder transformer, Sequence-to-sequence},
	pages = {116948},
}

@inproceedings{nguyen_canvec_2020,
	address = {Marseille, France},
	title = {{CanVEC} - the {Canberra} {Vietnamese}-{English} {Code}-switching {Natural} {Speech} {Corpus}},
	isbn = {979-10-95546-34-4},
	url = {https://aclanthology.org/2020.lrec-1.507},
	abstract = {This paper introduces the Canberra Vietnamese-English Code-switching corpus (CanVEC), an original corpus of natural mixed speech that we semi-automatically annotated with language information, part of speech (POS) tags and Vietnamese translations. The corpus, which was built to inform a sociolinguistic study on language variation and code-switching, consists of 10 hours of recorded speech (87k tokens) between 45 Vietnamese-English bilinguals living in Canberra, Australia. We describe how we collected and annotated the corpus by pipelining several monolingual toolkits to considerably speed up the annotation process. We also describe how we evaluated the automatic annotations to ensure corpus reliability. We make the corpus available for research purposes.},
	language = {English},
	urldate = {2023-01-23},
	booktitle = {Proceedings of the {Twelfth} {Language} {Resources} and {Evaluation} {Conference}},
	publisher = {European Language Resources Association},
	author = {Nguyen, Li and Bryant, Christopher},
	month = may,
	year = {2020},
	pages = {4121--4129},
}

@inproceedings{nawar_cufeqalb-2015_2015,
	address = {Beijing, China},
	title = {{CUFE}@{QALB}-2015 {Shared} {Task}: {Arabic} {Error} {Correction} {System}},
	shorttitle = {{CUFE}@{QALB}-2015 {Shared} {Task}},
	url = {https://aclanthology.org/W15-3215},
	doi = {10.18653/v1/W15-3215},
	urldate = {2023-01-23},
	booktitle = {Proceedings of the {Second} {Workshop} on {Arabic} {Natural} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Nawar, Michael},
	month = jul,
	year = {2015},
	pages = {133--137},
}

@article{zhao_maskgec_2020,
	title = {{MaskGEC}: {Improving} {Neural} {Grammatical} {Error} {Correction} via {Dynamic} {Masking}},
	volume = {34},
	copyright = {Copyright (c) 2020 Association for the Advancement of Artificial Intelligence},
	issn = {2374-3468},
	shorttitle = {{MaskGEC}},
	url = {https://ojs.aaai.org/index.php/AAAI/article/view/5476},
	doi = {10.1609/aaai.v34i01.5476},
	abstract = {Grammatical error correction (GEC) is a promising natural language processing (NLP) application, whose goal is to change the sentences with grammatical errors into the correct ones. Neural machine translation (NMT) approaches have been widely applied to this translation-like task. However, such methods need a fairly large parallel corpus of error-annotated sentence pairs, which is not easy to get especially in the field of Chinese grammatical error correction. In this paper, we propose a simple yet effective method to improve the NMT-based GEC models by dynamic masking. By adding random masks to the original source sentences dynamically in the training procedure, more diverse instances of error-corrected sentence pairs are generated to enhance the generalization ability of the grammatical error correction model without additional data. The experiments on NLPCC 2018 Task 2 show that our MaskGEC model improves the performance of the neural GEC models. Besides, our single model for Chinese GEC outperforms the current state-of-the-art ensemble system in NLPCC 2018 Task 2 without any extra knowledge.},
	language = {en},
	number = {01},
	urldate = {2023-01-23},
	journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
	author = {Zhao, Zewei and Wang, Houfeng},
	month = apr,
	year = {2020},
	note = {Number: 01},
	pages = {1226--1233},
}

@inproceedings{cotet_neural_2020,
	title = {Neural {Grammatical} {Error} {Correction} for {Romanian}},
	doi = {10.1109/ICTAI50040.2020.00101},
	abstract = {Resources for Grammatical Error Correction (GEC) in non-English languages are scarce, while available spellcheckers in these languages are mostly limited to simple corrections and rules. In this paper we introduce a first GEC corpus for Romanian consisting of 10k pairs of sentences. In addition, the German version of ERRANT (ERRor ANnotation Toolkit) scorer was adapted for Romanian to analyze this corpus and extract edits needed for evaluation. Multiple neural models were experimented, together with pretraining strategies, which proved effective for GEC in low-resource settings. Our baseline consists of a small Transformer model trained only on the GEC dataset ( F0.5=44.38), whereas the best performing model is produced by pretraining a larger Transformer model on artificially generated data, followed by finetuning on the actual corpus ( F0.5=53.76). The proposed method for generating additional training examples is easily extensible and can be applied to any language, as it requires only a POS tagger.},
	booktitle = {2020 {IEEE} 32nd {International} {Conference} on {Tools} with {Artificial} {Intelligence} ({ICTAI})},
	author = {Cotet, Teodor-Mihai and Ruseti, Stefan and Dascalu, Mihai},
	month = nov,
	year = {2020},
	note = {ISSN: 2375-0197},
	keywords = {Adaptation models, Artificial intelligence, Conferences, Data models, ERRANT, Error correction, Grammatical Error Correction, Romanian, Tools, Training, Transformer, low-resource language},
	pages = {625--631},
}

@misc{sun_unified_2022,
	title = {A {Unified} {Strategy} for {Multilingual} {Grammatical} {Error} {Correction} with {Pre}-trained {Cross}-{Lingual} {Language} {Model}},
	url = {http://arxiv.org/abs/2201.10707},
	doi = {10.48550/arXiv.2201.10707},
	abstract = {Synthetic data construction of Grammatical Error Correction (GEC) for non-English languages relies heavily on human-designed and language-specific rules, which produce limited error-corrected patterns. In this paper, we propose a generic and language-independent strategy for multilingual GEC, which can train a GEC system effectively for a new non-English language with only two easy-to-access resources: 1) a pretrained cross-lingual language model (PXLM) and 2) parallel translation data between English and the language. Our approach creates diverse parallel GEC data without any language-specific operations by taking the non-autoregressive translation generated by PXLM and the gold translation as error-corrected sentence pairs. Then, we reuse PXLM to initialize the GEC model and pretrain it with the synthetic data generated by itself, which yields further improvement. We evaluate our approach on three public benchmarks of GEC in different languages. It achieves the state-of-the-art results on the NLPCC 2018 Task 2 dataset (Chinese) and obtains competitive performance on Falko-Merlin (German) and RULEC-GEC (Russian). Further analysis demonstrates that our data construction method is complementary to rule-based approaches.},
	urldate = {2023-01-23},
	publisher = {arXiv},
	author = {Sun, Xin and Ge, Tao and Ma, Shuming and Li, Jingjing and Wei, Furu and Wang, Houfeng},
	month = jan,
	year = {2022},
	note = {arXiv:2201.10707 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@inproceedings{qorib_frustratingly_2022,
	address = {Seattle, United States},
	title = {Frustratingly {Easy} {System} {Combination} for {Grammatical} {Error} {Correction}},
	url = {https://aclanthology.org/2022.naacl-main.143},
	doi = {10.18653/v1/2022.naacl-main.143},
	abstract = {In this paper, we formulate system combination for grammatical error correction (GEC) as a simple machine learning task: binary classification. We demonstrate that with the right problem formulation, a simple logistic regression algorithm can be highly effective for combining GEC models. Our method successfully increases the F0.5 score from the highest base GEC system by 4.2 points on the CoNLL-2014 test set and 7.2 points on the BEA-2019 test set. Furthermore, our method outperforms the state of the art by 4.0 points on the BEA-2019 test set, 1.2 points on the CoNLL-2014 test set with original annotation, and 3.4 points on the CoNLL-2014 test set with alternative annotation. We also show that our system combination generates better corrections with higher F0.5 scores than the conventional ensemble.},
	urldate = {2023-01-22},
	booktitle = {Proceedings of the 2022 {Conference} of the {North} {American} {Chapter} of the {Association} for {Computational} {Linguistics}: {Human} {Language} {Technologies}},
	publisher = {Association for Computational Linguistics},
	author = {Qorib, Muhammad and Na, Seung-Hoon and Ng, Hwee Tou},
	month = jul,
	year = {2022},
	pages = {1964--1974},
}

@inproceedings{yuan_document-level_2021,
	address = {Online},
	title = {Document-level grammatical error correction},
	url = {https://aclanthology.org/2021.bea-1.8},
	abstract = {Document-level context can provide valuable information in grammatical error correction (GEC), which is crucial for correcting certain errors and resolving inconsistencies. In this paper, we investigate context-aware approaches and propose document-level GEC systems. Additionally, we employ a three-step training strategy to benefit from both sentence-level and document-level data. Our system outperforms previous document-level and all other NMT-based single-model systems, achieving state of the art on a common test set.},
	urldate = {2023-01-22},
	booktitle = {Proceedings of the 16th {Workshop} on {Innovative} {Use} of {NLP} for {Building} {Educational} {Applications}},
	publisher = {Association for Computational Linguistics},
	author = {Yuan, Zheng and Bryant, Christopher},
	month = apr,
	year = {2021},
	pages = {75--84},
}

@misc{wang_comprehensive_2020,
	title = {A {Comprehensive} {Survey} of {Grammar} {Error} {Correction}},
	url = {http://arxiv.org/abs/2005.06600},
	doi = {10.48550/arXiv.2005.06600},
	abstract = {Grammar error correction (GEC) is an important application aspect of natural language processing techniques. The past decade has witnessed significant progress achieved in GEC for the sake of increasing popularity of machine learning and deep learning, especially in late 2010s when near human-level GEC systems are available. However, there is no prior work focusing on the whole recapitulation of the progress. We present the first survey in GEC for a comprehensive retrospect of the literature in this area. We first give the introduction of five public datasets, data annotation schema, two important shared tasks and four standard evaluation metrics. More importantly, we discuss four kinds of basic approaches, including statistical machine translation based approach, neural machine translation based approach, classification based approach and language model based approach, six commonly applied performance boosting techniques for GEC systems and two data augmentation methods. Since GEC is typically viewed as a sister task of machine translation, many GEC systems are based on neural machine translation (NMT) approaches, where the neural sequence-to-sequence model is applied. Similarly, some performance boosting techniques are adapted from machine translation and are successfully combined with GEC systems for enhancement on the final performance. Furthermore, we conduct an analysis in level of basic approaches, performance boosting techniques and integrated GEC systems based on their experiment results respectively for more clear patterns and conclusions. Finally, we discuss five prospective directions for future GEC researches.},
	urldate = {2023-01-22},
	publisher = {arXiv},
	author = {Wang, Yu and Wang, Yuelin and Liu, Jie and Liu, Zhuo},
	month = may,
	year = {2020},
	note = {arXiv:2005.06600 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{davis_probing_2022-1,
	title = {Probing for targeted syntactic knowledge through grammatical error detection},
	url = {http://arxiv.org/abs/2210.16228},
	doi = {10.48550/arXiv.2210.16228},
	abstract = {Targeted studies testing knowledge of subject-verb agreement (SVA) indicate that pre-trained language models encode syntactic information. We assert that if models robustly encode subject-verb agreement, they should be able to identify when agreement is correct and when it is incorrect. To that end, we propose grammatical error detection as a diagnostic probe to evaluate token-level contextual representations for their knowledge of SVA. We evaluate contextual representations at each layer from five pre-trained English language models: BERT, XLNet, GPT-2, RoBERTa, and ELECTRA. We leverage public annotated training data from both English second language learners and Wikipedia edits, and report results on manually crafted stimuli for subject-verb agreement. We find that masked language models linearly encode information relevant to the detection of SVA errors, while the autoregressive models perform on par with our baseline. However, we also observe a divergence in performance when probes are trained on different training sets, and when they are evaluated on different syntactic constructions, suggesting the information pertaining to SVA error detection is not robustly encoded.},
	urldate = {2023-01-22},
	publisher = {arXiv},
	author = {Davis, Christopher and Bryant, Christopher and Caines, Andrew and Rei, Marek and Buttery, Paula},
	month = oct,
	year = {2022},
	note = {arXiv:2210.16228 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@book{leacock_automated_2014,
	address = {Cham},
	series = {Synthesis {Lectures} on {Human} {Language} {Technologies}},
	title = {Automated {Grammatical} {Error} {Detection} for {Language} {Learners}, {Second} {Edition}},
	isbn = {978-3-031-01025-5 978-3-031-02153-4},
	url = {https://link.springer.com/10.1007/978-3-031-02153-4},
	language = {en},
	urldate = {2023-01-22},
	publisher = {Springer International Publishing},
	author = {Leacock, Claudia and Chodorow, Martin and Gamon, Michael and Tetreault, Joel},
	year = {2014},
	doi = {10.1007/978-3-031-02153-4},
}

@article{nguyen_automatic_2021,
	title = {Automatic {Language} {Identification} in {Code}-{Switched} {Hindi}-{English} {Social} {Media} {Text}},
	volume = {7},
	issn = {2059-481X},
	url = {http://openhumanitiesdata.metajnl.com/articles/10.5334/johd.44/},
	doi = {10.5334/johd.44},
	language = {en},
	urldate = {2023-01-22},
	journal = {Journal of Open Humanities Data},
	author = {Nguyen, Li and Bryant, Christopher and Kidwai, Sana and Biberauer, Theresa},
	month = jun,
	year = {2021},
	pages = {7},
}

@misc{bryant_grammatical_2022,
	title = {Grammatical {Error} {Correction}: {A} {Survey} of the {State} of the {Art}},
	shorttitle = {Grammatical {Error} {Correction}},
	url = {http://arxiv.org/abs/2211.05166},
	doi = {10.48550/arXiv.2211.05166},
	abstract = {Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject-verb agreement, but also orthographic and semantic errors, such as misspellings and word choice errors respectively. The field has seen significant progress in the last decade, motivated in part by a series of five shared tasks, which drove the development of rule-based methods, statistical classifiers, statistical machine translation, and finally neural machine translation systems which represent the current dominant state of the art. In this survey paper, we condense the field into a single article and first outline some of the linguistic challenges of the task, introduce the most popular datasets that are available to researchers (for both English and other languages), and summarise the various methods and techniques that have been developed with a particular focus on artificial error generation. We next describe the many different approaches to evaluation as well as concerns surrounding metric reliability, especially in relation to subjective human judgements, before concluding with an overview of recent progress and suggestions for future work and remaining challenges. We hope that this survey will serve as comprehensive resource for researchers who are new to the field or who want to be kept apprised of recent developments.},
	urldate = {2023-01-22},
	publisher = {arXiv},
	author = {Bryant, Christopher and Yuan, Zheng and Qorib, Muhammad Reza and Cao, Hannan and Ng, Hwee Tou and Briscoe, Ted},
	month = nov,
	year = {2022},
	note = {arXiv:2211.05166 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

@article{wu_oolong_2022,
	title = {Oolong: {Investigating} {What} {Makes} {Crosslingual} {Transfer} {Hard} with {Controlled} {Studies}},
	shorttitle = {Oolong},
	journal = {arXiv preprint arXiv:2202.12312},
	author = {Wu, Zhengxuan and Papadimitriou, Isabel and Tamkin, Alex},
	year = {2022},
}

@misc{chen_calcs_2022,
	title = {{CALCS} 2021 {Shared} {Task}: {Machine} {Translation} for {Code}-{Switched} {Data}},
	shorttitle = {{CALCS} 2021 {Shared} {Task}},
	url = {http://arxiv.org/abs/2202.09625},
	doi = {10.48550/arXiv.2202.09625},
	abstract = {To date, efforts in the code-switching literature have focused for the most part on language identification, POS, NER, and syntactic parsing. In this paper, we address machine translation for code-switched social media data. We create a community shared task. We provide two modalities for participation: supervised and unsupervised. For the supervised setting, participants are challenged to translate English into Hindi-English (Eng-Hinglish) in a single direction. For the unsupervised setting, we provide the following language pairs: English and Spanish-English (Eng-Spanglish), and English and Modern Standard Arabic-Egyptian Arabic (Eng-MSAEA) in both directions. We share insights and challenges in curating the "into" code-switching language evaluation data. Further, we provide baselines for all language pairs in the shared task. The leaderboard for the shared task comprises 12 individual system submissions corresponding to 5 different teams. The best performance achieved is 12.67\% BLEU score for English to Hinglish and 25.72\% BLEU score for MSAEA to English.},
	urldate = {2023-01-22},
	publisher = {arXiv},
	author = {Chen, Shuguang and Aguilar, Gustavo and Srinivasan, Anirudh and Diab, Mona and Solorio, Thamar},
	month = feb,
	year = {2022},
	note = {arXiv:2202.09625 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@article{sharma_ceasing_2022,
	title = {Ceasing hate with {MoH}: {Hate} {Speech} {Detection} in {Hindi}–{English} code-switched language},
	volume = {59},
	issn = {0306-4573},
	shorttitle = {Ceasing hate with {MoH}},
	url = {https://www.sciencedirect.com/science/article/pii/S0306457321002417},
	doi = {10.1016/j.ipm.2021.102760},
	abstract = {Warning: This manuscript may contain upsetting language. Social media has become a bedrock for people to voice their opinions worldwide. Due to the greater sense of freedom with the anonymity feature, it is possible to disregard social etiquette online and attack others without facing severe consequences, inevitably propagating hate speech. The current measures to sift the online content and offset the hatred spread do not go far enough. One factor contributing to this is the prevalence of regional languages in social media and the paucity of language flexible hate speech detectors. The proposed work focuses on analyzing hate speech in Hindi–English code-switched language. Our method explores transformation techniques to capture precise text representation. To contain the structure of data and yet use it with existing algorithms, we developed ‘MoH’ or (Map Only Hindi), which means ‘Love’ in Hindi. ‘MoH’ pipeline which consists of language identification, Roman to Devanagari Hindi transliteration using a knowledge base of Roman Hindi words, and finally employs the fine-tuned Multilingual Bert, and MuRIL language models. We conducted several quantitative experiment studies on three datasets, and evaluated performance using Precision, Recall and F1 metrics. The first experiment studies ‘MoH’ mapped text’s performance with classical machine learning models and shows an average increase of 13\% in F1 scores. The second compares the proposed work’s scores with those of the baseline models and shows a rise in performance by 6\%. Finally, the third compares the proposed ‘MoH’ technique with various data simulations using the existing transliteration library. Here, ‘MoH’ outperforms the rest by 15\%. Our results demonstrate a significant improvement in the state-of-the-art scores on all three datasets.},
	language = {en},
	number = {1},
	urldate = {2023-01-22},
	journal = {Information Processing \& Management},
	author = {Sharma, Arushi and Kabra, Anubha and Jain, Minni},
	month = jan,
	year = {2022},
	keywords = {And machine learning, Bert, Cyber hate, Data simulations, MuRIL, Social media, Text classification, Transfer learning},
	pages = {102760},
}

@misc{muennighoff_crosslingual_2022-1,
	title = {Crosslingual {Generalization} through {Multitask} {Finetuning}},
	url = {http://arxiv.org/abs/2211.01786},
	doi = {10.48550/arXiv.2211.01786},
	abstract = {Multitask prompted finetuning (MTF) has been shown to help large language models generalize to new tasks in a zero-shot setting, but so far explorations of MTF have focused on English data and models. We apply MTF to the pretrained multilingual BLOOM and mT5 model families to produce finetuned variants called BLOOMZ and mT0. We find finetuning large multilingual language models on English tasks with English prompts allows for task generalization to non-English languages that appear only in the pretraining corpus. Finetuning on multilingual tasks with English prompts further improves performance on English and non-English tasks leading to various state-of-the-art zero-shot results. We also investigate finetuning on multilingual tasks with prompts that have been machine-translated from English to match the language of each dataset. We find training on these machine-translated prompts leads to better performance on human-written prompts in the respective languages. Surprisingly, we find models are capable of zero-shot generalization to tasks in languages they have never intentionally seen. We conjecture that the models are learning higher-level capabilities that are both task- and language-agnostic. In addition, we introduce xP3, a composite of supervised datasets in 46 languages with English and machine-translated prompts. Our code, datasets and models are publicly available at https://github.com/bigscience-workshop/xmtf.},
	urldate = {2023-01-22},
	publisher = {arXiv},
	author = {Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M. Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and Tang, Xiangru and Radev, Dragomir and Aji, Alham Fikri and Almubarak, Khalid and Albanie, Samuel and Alyafeai, Zaid and Webson, Albert and Raff, Edward and Raffel, Colin},
	month = nov,
	year = {2022},
	note = {arXiv:2211.01786 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{winata_code-switched_2019-1,
	title = {Code-{Switched} {Language} {Models} {Using} {Neural} {Based} {Synthetic} {Data} from {Parallel} {Sentences}},
	url = {http://arxiv.org/abs/1909.08582},
	doi = {10.48550/arXiv.1909.08582},
	abstract = {Training code-switched language models is difficult due to lack of data and complexity in the grammatical structure. Linguistic constraint theories have been used for decades to generate artificial code-switching sentences to cope with this issue. However, this require external word alignments or constituency parsers that create erroneous results on distant languages. We propose a sequence-to-sequence model using a copy mechanism to generate code-switching data by leveraging parallel monolingual translations from a limited source of code-switching data. The model learns how to combine words from parallel sentences and identifies when to switch one language to the other. Moreover, it captures code-switching constraints by attending and aligning the words in inputs, without requiring any external knowledge. Based on experimental results, the language model trained with the generated sentences achieves state-of-the-art performance and improves end-to-end automatic speech recognition.},
	urldate = {2023-01-22},
	publisher = {arXiv},
	author = {Winata, Genta Indra and Madotto, Andrea and Wu, Chien-Sheng and Fung, Pascale},
	month = sep,
	year = {2019},
	note = {arXiv:1909.08582 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@book{solorio_proceedings_2021,
	address = {Online},
	title = {Proceedings of the {Fifth} {Workshop} on {Computational} {Approaches} to {Linguistic} {Code}-{Switching}},
	url = {https://aclanthology.org/2021.calcs-1.0},
	urldate = {2023-01-22},
	publisher = {Association for Computational Linguistics},
	editor = {Solorio, Thamar and Chen, Shuguang and Black, Alan W. and Diab, Mona and Sitaram, Sunayana and Soto, Victor and Yilmaz, Emre and Srinivasan, Anirudh},
	month = jun,
	year = {2021},
}

@misc{noauthor_cs_nodate,
	title = {{CS} {Workshop}},
	url = {https://code-switching.github.io/2021},
	urldate = {2023-01-22},
}

@misc{winata_code-switching_2023,
	title = {Code-switching {Research} {Resources}},
	copyright = {Apache-2.0},
	url = {https://github.com/gentaiscool/code-switching-papers},
	abstract = {A curated list of research papers and resources on code-switching},
	urldate = {2023-01-22},
	author = {Winata, Genta Indra},
	month = jan,
	year = {2023},
	note = {original-date: 2018-06-08T08:38:26Z},
	keywords = {bilingual, code-mixed, code-mixing, code-switch, code-switching, language, nlp, papers, research, speech},
}

@misc{aguilar_lince_2020,
	title = {{LinCE}: {A} {Centralized} {Benchmark} for {Linguistic} {Code}-switching {Evaluation}},
	shorttitle = {{LinCE}},
	url = {http://arxiv.org/abs/2005.04322},
	doi = {10.48550/arXiv.2005.04322},
	abstract = {Recent trends in NLP research have raised an interest in linguistic code-switching (CS); modern approaches have been proposed to solve a wide range of NLP tasks on multiple language pairs. Unfortunately, these proposed methods are hardly generalizable to different code-switched languages. In addition, it is unclear whether a model architecture is applicable for a different task while still being compatible with the code-switching setting. This is mainly because of the lack of a centralized benchmark and the sparse corpora that researchers employ based on their specific needs and interests. To facilitate research in this direction, we propose a centralized benchmark for Linguistic Code-switching Evaluation (LinCE) that combines ten corpora covering four different code-switched language pairs (i.e., Spanish-English, Nepali-English, Hindi-English, and Modern Standard Arabic-Egyptian Arabic) and four tasks (i.e., language identification, named entity recognition, part-of-speech tagging, and sentiment analysis). As part of the benchmark centralization effort, we provide an online platform at ritual.uh.edu/lince, where researchers can submit their results while comparing with others in real-time. In addition, we provide the scores of different popular models, including LSTM, ELMo, and multilingual BERT so that the NLP community can compare against state-of-the-art systems. LinCE is a continuous effort, and we will expand it with more low-resource languages and tasks.},
	urldate = {2023-01-22},
	publisher = {arXiv},
	author = {Aguilar, Gustavo and Kar, Sudipta and Solorio, Thamar},
	month = may,
	year = {2020},
	note = {arXiv:2005.04322 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@inproceedings{dogruoz_survey_2021,
	address = {Online},
	title = {A {Survey} of {Code}-switching: {Linguistic} and {Social} {Perspectives} for {Language} {Technologies}},
	shorttitle = {A {Survey} of {Code}-switching},
	url = {https://aclanthology.org/2021.acl-long.131},
	doi = {10.18653/v1/2021.acl-long.131},
	abstract = {The analysis of data in which multiple languages are represented has gained popularity among computational linguists in recent years. So far, much of this research focuses mainly on the improvement of computational methods and largely ignores linguistic and social aspects of C-S discussed across a wide range of languages within the long-established literature in linguistics. To fill this gap, we offer a survey of code-switching (C-S) covering the literature in linguistics with a reflection on the key issues in language technologies. From the linguistic perspective, we provide an overview of structural and functional patterns of C-S focusing on the literature from European and Indian contexts as highly multilingual areas. From the language technologies perspective, we discuss how massive language models fail to represent diverse C-S types due to lack of appropriate training data, lack of robust evaluation benchmarks for C-S (across multilingual situations and types of C-S) and lack of end-to- end systems that cover sociolinguistic aspects of C-S as well. Our survey will be a step to- wards an outcome of mutual benefit for computational scientists and linguists with a shared interest in multilingualism and C-S.},
	urldate = {2023-01-22},
	booktitle = {Proceedings of the 59th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics} and the 11th {International} {Joint} {Conference} on {Natural} {Language} {Processing} ({Volume} 1: {Long} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Doğruöz, A. Seza and Sitaram, Sunayana and Bullock, Barbara E. and Toribio, Almeida Jacqueline},
	month = aug,
	year = {2021},
	pages = {1654--1666},
}

@misc{xu_can_2021,
	title = {Can {You} {Traducir} {This}? {Machine} {Translation} for {Code}-{Switched} {Input}},
	shorttitle = {Can {You} {Traducir} {This}?},
	url = {http://arxiv.org/abs/2105.04846},
	doi = {10.48550/arXiv.2105.04846},
	abstract = {Code-Switching (CSW) is a common phenomenon that occurs in multilingual geographic or social contexts, which raises challenging problems for natural language processing tools. We focus here on Machine Translation (MT) of CSW texts, where we aim to simultaneously disentangle and translate the two mixed languages. Due to the lack of actual translated CSW data, we generate artificial training data from regular parallel texts. Experiments show this training strategy yields MT systems that surpass multilingual systems for code-switched texts. These results are confirmed in an alternative task aimed at providing contextual translations for a L2 writing assistant.},
	urldate = {2023-01-22},
	publisher = {arXiv},
	author = {Xu, Jitao and Yvon, François},
	month = may,
	year = {2021},
	note = {arXiv:2105.04846 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{patwa_semeval-2020_2020,
	title = {{SemEval}-2020 {Task} 9: {Overview} of {Sentiment} {Analysis} of {Code}-{Mixed} {Tweets}},
	shorttitle = {{SemEval}-2020 {Task} 9},
	url = {http://arxiv.org/abs/2008.04277},
	doi = {10.48550/arXiv.2008.04277},
	abstract = {In this paper, we present the results of the SemEval-2020 Task 9 on Sentiment Analysis of Code-Mixed Tweets (SentiMix 2020). We also release and describe our Hinglish (Hindi-English) and Spanglish (Spanish-English) corpora annotated with word-level language identification and sentence-level sentiment labels. These corpora are comprised of 20K and 19K examples, respectively. The sentiment labels are - Positive, Negative, and Neutral. SentiMix attracted 89 submissions in total including 61 teams that participated in the Hinglish contest and 28 submitted systems to the Spanglish competition. The best performance achieved was 75.0\% F1 score for Hinglish and 80.6\% F1 for Spanglish. We observe that BERT-like models and ensemble methods are the most common and successful approaches among the participants.},
	urldate = {2023-01-22},
	publisher = {arXiv},
	author = {Patwa, Parth and Aguilar, Gustavo and Kar, Sudipta and Pandey, Suraj and PYKL, Srinivas and Gambäck, Björn and Chakraborty, Tanmoy and Solorio, Thamar and Das, Amitava},
	month = aug,
	year = {2020},
	note = {arXiv:2008.04277 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@inproceedings{molina_overview_2016,
	address = {Austin, Texas},
	title = {Overview for the {Second} {Shared} {Task} on {Language} {Identification} in {Code}-{Switched} {Data}},
	url = {https://aclanthology.org/W16-5805},
	doi = {10.18653/v1/W16-5805},
	urldate = {2023-01-22},
	booktitle = {Proceedings of the {Second} {Workshop} on {Computational} {Approaches} to {Code} {Switching}},
	publisher = {Association for Computational Linguistics},
	author = {Molina, Giovanni and AlGhamdi, Fahad and Ghoneim, Mahmoud and Hawwari, Abdelati and Rey-Villamizar, Nicolas and Diab, Mona and Solorio, Thamar},
	month = nov,
	year = {2016},
	pages = {40--49},
}

@inproceedings{solorio_overview_2014,
	address = {Doha, Qatar},
	title = {Overview for the {First} {Shared} {Task} on {Language} {Identification} in {Code}-{Switched} {Data}},
	url = {https://aclanthology.org/W14-3907},
	doi = {10.3115/v1/W14-3907},
	urldate = {2023-01-22},
	booktitle = {Proceedings of the {First} {Workshop} on {Computational} {Approaches} to {Code} {Switching}},
	publisher = {Association for Computational Linguistics},
	author = {Solorio, Thamar and Blair, Elizabeth and Maharjan, Suraj and Bethard, Steven and Diab, Mona and Ghoneim, Mahmoud and Hawwari, Abdelati and AlGhamdi, Fahad and Hirschberg, Julia and Chang, Alison and Fung, Pascale},
	month = oct,
	year = {2014},
	pages = {62--72},
}

@misc{winata_decades_2022,
	title = {The {Decades} {Progress} on {Code}-{Switching} {Research} in {NLP}: {A} {Systematic} {Survey} on {Trends} and {Challenges}},
	shorttitle = {The {Decades} {Progress} on {Code}-{Switching} {Research} in {NLP}},
	url = {http://arxiv.org/abs/2212.09660},
	doi = {10.48550/arXiv.2212.09660},
	abstract = {Code-Switching, a common phenomenon in written text and conversation, has been studied over decades by the natural language processing (NLP) research community. Initially, code-switching is intensively explored by leveraging linguistic theories and, currently, more machine-learning oriented approaches to develop models. We introduce a comprehensive systematic survey on code-switching research in natural language processing to understand the progress of the past decades and conceptualize the challenges and tasks on the code-switching topic. Finally, we summarize the trends and findings and conclude with a discussion for future direction and open questions for further investigation.},
	urldate = {2023-01-22},
	publisher = {arXiv},
	author = {Winata, Genta Indra and Aji, Alham Fikri and Yong, Zheng-Xin and Solorio, Thamar},
	month = dec,
	year = {2022},
	note = {arXiv:2212.09660 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{noauthor_multilingualism_nodate,
	title = {Multilingualism and {Translanguaging} in {Chinese} {Language} {Classrooms} {\textbar} {SpringerLink}},
	url = {https://link.springer.com/book/10.1007/978-3-030-02529-8},
	urldate = {2023-01-22},
}

@article{anonymous_multilingual_2022,
	title = {Multilingual {Previously} {Fact}-{Checked} {Claim} {Retrieval}},
	url = {https://openreview.net/forum?id=AiDIIz9EMwL},
	abstract = {Fact-checkers are often hampered by the sheer amount of online content that needs to be fact-checked. NLP can help them by retrieving already existing fact-checks relevant to the content being investigated. This paper introduces a new multilingual dataset for previously fact-checked claim retrieval. We collected 28k posts in 27 languages from social media, 206k fact-checks in 39 languages written by professional fact-checkers, as well as 31k connections between these two groups. This is the most extensive and the most linguistically diverse dataset of this kind to date. We evaluated how different unsupervised methods fare on this dataset and its various dimensions. We show that evaluating such a diverse dataset has its complexities and proper care needs to be taken before interpreting the results. We also evaluated a supervised fine-tuning approach, improving upon the unsupervised method significantly.},
	language = {en},
	urldate = {2023-01-22},
	author = {Anonymous},
	month = dec,
	year = {2022},
}

@misc{noauthor_previous_nodate,
	title = {Previous shared tasks {\textbar} {CoNLL}},
	url = {https://www.conll.org/previous-tasks},
	urldate = {2023-01-22},
}

@inproceedings{ng_conll-2014_2014,
	address = {Baltimore, Maryland},
	title = {The {CoNLL}-2014 {Shared} {Task} on {Grammatical} {Error} {Correction}},
	url = {https://aclanthology.org/W14-1701},
	doi = {10.3115/v1/W14-1701},
	urldate = {2023-01-22},
	booktitle = {Proceedings of the {Eighteenth} {Conference} on {Computational} {Natural} {Language} {Learning}: {Shared} {Task}},
	publisher = {Association for Computational Linguistics},
	author = {Ng, Hwee Tou and Wu, Siew Mei and Briscoe, Ted and Hadiwinoto, Christian and Susanto, Raymond Hendy and Bryant, Christopher},
	month = jun,
	year = {2014},
	pages = {1--14},
}

@misc{gretter_tlt-school_2020,
	title = {{TLT}-school: a {Corpus} of {Non} {Native} {Children} {Speech}},
	shorttitle = {{TLT}-school},
	url = {http://arxiv.org/abs/2001.08051},
	doi = {10.48550/arXiv.2001.08051},
	abstract = {This paper describes "TLT-school" a corpus of speech utterances collected in schools of northern Italy for assessing the performance of students learning both English and German. The corpus was recorded in the years 2017 and 2018 from students aged between nine and sixteen years, attending primary, middle and high school. All utterances have been scored, in terms of some predefined proficiency indicators, by human experts. In addition, most of utterances recorded in 2017 have been manually transcribed carefully. Guidelines and procedures used for manual transcriptions of utterances will be described in detail, as well as results achieved by means of an automatic speech recognition system developed by us. Part of the corpus is going to be freely distributed to scientific community particularly interested both in non-native speech recognition and automatic assessment of second language proficiency.},
	urldate = {2023-01-21},
	publisher = {arXiv},
	author = {Gretter, Roberto and Matassoni, Marco and Bannò, Stefano and Falavigna, Daniele},
	month = jan,
	year = {2020},
	note = {arXiv:2001.08051 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@inproceedings{casademont_moner_swedish_2022,
	address = {Louvain-la-Neuve, Belgium},
	title = {Swedish {MuClaGED}: {A} new dataset for {Grammatical} {Error} {Detection} in {Swedish}},
	shorttitle = {Swedish {MuClaGED}},
	url = {https://aclanthology.org/2022.nlp4call-1.4},
	urldate = {2023-01-21},
	booktitle = {Proceedings of the 11th {Workshop} on {NLP} for {Computer} {Assisted} {Language} {Learning}},
	publisher = {LiU Electronic Press},
	author = {Casademont Moner, Judith and Volodina, Elena},
	month = dec,
	year = {2022},
	pages = {36--45},
}

@article{das_code-mixing_2015,
	title = {Code-{Mixing} in {Social} {Media} {Text}: {The} {Last} {Language} {Identification} {Frontier}?},
	issn = {1965-0906},
	shorttitle = {Code-{Mixing} in {Social} {Media} {Text}},
	url = {https://ntnuopen.ntnu.no/ntnu-xmlui/handle/11250/2391067},
	abstract = {ABSTRACT:  Automatic understanding of noisy social media text is one of the prime present-day research areas. Most research has so far concentrated on English texts ; however, more than half of the users are writing in other languages, making language identification a pre-requisite for comprehensive processing of social media text. Though language identification has been considered an almost solved problem in other applications, language detectors fail in the social media context due to phenomena such as code-mixing, code-switching, lexical borrowings, Anglicisms, and phonetic typing. This paper reports an initial study to understand the characteristics of code-mixing in the social media context and presents a system developed to automatically detect language boundaries in code-mixed social media text, here exemplified by Facebook messages in mixed English-Bengali and English-Hindi.},
	language = {eng},
	urldate = {2023-01-21},
	journal = {41-64},
	author = {Das, Amitava and Gambäck, Björn},
	year = {2015},
	note = {Accepted: 2016-01-23T20:15:12Z
Publisher: Association pour le Traitement Automatique des Langues (ATALA)},
}

@inproceedings{adouane_when_2020,
	address = {Marseille, France},
	title = {When is {Multi}-task {Learning} {Beneficial} for {Low}-{Resource} {Noisy} {Code}-switched {User}-generated {Algerian} {Texts}?},
	isbn = {979-10-95546-66-5},
	url = {https://aclanthology.org/2020.calcs-1.3},
	abstract = {We investigate when is it beneficial to simultaneously learn representations for several tasks, in low-resource settings. For this, we work with noisy user-generated texts in Algerian, a low-resource non-standardised Arabic variety. That is, to mitigate the problem of the data scarcity, we experiment with jointly learning progressively 4 tasks, namely code-switch detection, named entity recognition, spell normalisation and correction, and identifying users' sentiments. The selection of these tasks is motivated by the lack of labelled data for automatic morpho-syntactic or semantic sequence-tagging tasks for Algerian, in contrast to the case of much multi-task learning for NLP. Our empirical results show that multi-task learning is beneficial for some tasks in particular settings, and that the effect of each task on another, the order of the tasks, and the size of the training data of the task with more data do matter. Moreover, the data augmentation that we performed with no external resources has been shown to be beneficial for certain tasks.},
	language = {English},
	urldate = {2023-01-21},
	booktitle = {Proceedings of the {The} 4th {Workshop} on {Computational} {Approaches} to {Code} {Switching}},
	publisher = {European Language Resources Association},
	author = {Adouane, Wafia and Bernardy, Jean-Philippe},
	month = may,
	year = {2020},
	pages = {17--25},
}

@article{das_identifying_nodate,
	title = {Identifying {Languages} at the {Word} {Level} in {Code}-{Mixed} {Indian} {Social} {Media} {Text}},
	abstract = {Language identiﬁcation at the document level has been considered an almost solved problem in some application areas, but language detectors fail in the social media context due to phenomena such as utterance internal code-switching, lexical borrowings, and phonetic typing; all implying that language identiﬁcation in social media has to be carried out at the word level. The paper reports a study to detect language boundaries at the word level in chat message corpora in mixed EnglishBengali and English-Hindi. We introduce a code-mixing index to evaluate the level of blending in the corpora and describe the performance of a system developed to separate multiple languages.},
	language = {en},
	author = {Das, Amitava and Gambäck, Björn},
}

@article{sharma_ceasing_2022-1,
	title = {Ceasing hate with {MoH}: {Hate} {Speech} {Detection} in {Hindi}–{English} code-switched language},
	volume = {59},
	issn = {0306-4573},
	shorttitle = {Ceasing hate with {MoH}},
	url = {https://www.sciencedirect.com/science/article/pii/S0306457321002417},
	doi = {10.1016/j.ipm.2021.102760},
	abstract = {Warning: This manuscript may contain upsetting language. Social media has become a bedrock for people to voice their opinions worldwide. Due to the greater sense of freedom with the anonymity feature, it is possible to disregard social etiquette online and attack others without facing severe consequences, inevitably propagating hate speech. The current measures to sift the online content and offset the hatred spread do not go far enough. One factor contributing to this is the prevalence of regional languages in social media and the paucity of language flexible hate speech detectors. The proposed work focuses on analyzing hate speech in Hindi–English code-switched language. Our method explores transformation techniques to capture precise text representation. To contain the structure of data and yet use it with existing algorithms, we developed ‘MoH’ or (Map Only Hindi), which means ‘Love’ in Hindi. ‘MoH’ pipeline which consists of language identification, Roman to Devanagari Hindi transliteration using a knowledge base of Roman Hindi words, and finally employs the fine-tuned Multilingual Bert, and MuRIL language models. We conducted several quantitative experiment studies on three datasets, and evaluated performance using Precision, Recall and F1 metrics. The first experiment studies ‘MoH’ mapped text’s performance with classical machine learning models and shows an average increase of 13\% in F1 scores. The second compares the proposed work’s scores with those of the baseline models and shows a rise in performance by 6\%. Finally, the third compares the proposed ‘MoH’ technique with various data simulations using the existing transliteration library. Here, ‘MoH’ outperforms the rest by 15\%. Our results demonstrate a significant improvement in the state-of-the-art scores on all three datasets.},
	language = {en},
	number = {1},
	urldate = {2023-01-21},
	journal = {Information Processing \& Management},
	author = {Sharma, Arushi and Kabra, Anubha and Jain, Minni},
	month = jan,
	year = {2022},
	keywords = {And machine learning, Bert, Cyber hate, Data simulations, MuRIL, Social media, Text classification, Transfer learning},
	pages = {102760},
}

@article{zhu_leveraging_2022,
	title = {Leveraging bilingual-view parallel translation for code-switched emotion detection with adversarial dual-channel encoder},
	volume = {235},
	issn = {0950-7051},
	url = {https://www.sciencedirect.com/science/article/pii/S0950705121006985},
	doi = {10.1016/j.knosys.2021.107436},
	abstract = {Code-switched emotion detection, a task analyzing the emotion in code-switched texts, has gain increasing research attention within recent years. Prior works utilize various neural models with sophisticated features to pursuit high performance of the task, while they still overlook some crucial characteristics of the code-switched texts. In this work, we present an innovative approach for improving code-switched emotion detection. We first consider a bilingual-view parallel translation for code-switched text enhancement, i.e., translating the code-switched texts into two languages. Then we propose an adversarial dual-channel encoder architecture, where two private encoders take as inputs the parallel texts in two languages, respectively. The private encoders and the shared encoder work collaboratively, and effectively retrieve the features from monolingual and bilingual perspectives under adversarial training. We conduct extensive experiments on five code-switched benchmark datasets. Results show that our model outperforms the strongly-performing baselines that leverage external code-switched or bilingual word embedding with over 1.5\% F1 score on the Chinese–English, Spanish–English and Hindi–English code-mixed data, becoming the new state-of-the-art system. Further analyses including ablation, qualitative and error studies, demonstrate the effectiveness of our proposed encoder for code-switched texts, as well as the bilingual-view parallel translation strategy.},
	language = {en},
	urldate = {2023-01-21},
	journal = {Knowledge-Based Systems},
	author = {Zhu, Xun and Lou, Yinxia and Deng, Hongtao and Ji, Donghong},
	month = jan,
	year = {2022},
	keywords = {Adversarial training, Bilingual-view translation, Code-switched text, Data mining, Emotion detection, Natural language processing, Sentiment analysis},
	pages = {107436},
}

@inproceedings{mathur_detecting_2018,
	address = {Melbourne, Australia},
	title = {Detecting {Offensive} {Tweets} in {Hindi}-{English} {Code}-{Switched} {Language}},
	url = {https://aclanthology.org/W18-3504},
	doi = {10.18653/v1/W18-3504},
	abstract = {The exponential rise of social media websites like Twitter, Facebook and Reddit in linguistically diverse geographical regions has led to hybridization of popular native languages with English in an effort to ease communication. The paper focuses on the classification of offensive tweets written in Hinglish language, which is a portmanteau of the Indic language Hindi with the Roman script. The paper introduces a novel tweet dataset, titled Hindi-English Offensive Tweet (HEOT) dataset, consisting of tweets in Hindi-English code switched language split into three classes: non-offensive, abusive and hate-speech. Further, we approach the problem of classification of the tweets in HEOT dataset using transfer learning wherein the proposed model employing Convolutional Neural Networks is pre-trained on tweets in English followed by retraining on Hinglish tweets.},
	urldate = {2023-01-21},
	booktitle = {Proceedings of the {Sixth} {International} {Workshop} on {Natural} {Language} {Processing} for {Social} {Media}},
	publisher = {Association for Computational Linguistics},
	author = {Mathur, Puneet and Shah, Rajiv and Sawhney, Ramit and Mahata, Debanjan},
	month = jul,
	year = {2018},
	pages = {18--26},
}

@inproceedings{khanuja_gluecos_2020,
	address = {Online},
	title = {{GLUECoS}: {An} {Evaluation} {Benchmark} for {Code}-{Switched} {NLP}},
	shorttitle = {{GLUECoS}},
	url = {https://aclanthology.org/2020.acl-main.329},
	doi = {10.18653/v1/2020.acl-main.329},
	abstract = {Code-switching is the use of more than one language in the same conversation or utterance. Recently, multilingual contextual embedding models, trained on multiple monolingual corpora, have shown promising results on cross-lingual and multilingual tasks. We present an evaluation benchmark, GLUECoS, for code-switched languages, that spans several NLP tasks in English-Hindi and English-Spanish. Specifically, our evaluation benchmark includes Language Identification from text, POS tagging, Named Entity Recognition, Sentiment Analysis, Question Answering and a new task for code-switching, Natural Language Inference. We present results on all these tasks using cross-lingual word embedding models and multilingual models. In addition, we fine-tune multilingual models on artificially generated code-switched data. Although multilingual models perform significantly better than cross-lingual models, our results show that in most tasks, across both language pairs, multilingual models fine-tuned on code-switched data perform best, showing that multilingual models can be further optimized for code-switching tasks.},
	urldate = {2023-01-21},
	booktitle = {Proceedings of the 58th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics}},
	publisher = {Association for Computational Linguistics},
	author = {Khanuja, Simran and Dandapat, Sandipan and Srinivasan, Anirudh and Sitaram, Sunayana and Choudhury, Monojit},
	month = jul,
	year = {2020},
	pages = {3575--3585},
}

@article{pikuliak_cross-lingual_2021,
	title = {Cross-lingual learning for text processing: {A} survey},
	volume = {165},
	issn = {09574174},
	shorttitle = {Cross-lingual learning for text processing},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0957417420305893},
	doi = {10.1016/j.eswa.2020.113765},
	abstract = {Many intelligent systems in business, government or academy process natural language as an input during inference or they might even communicate with users in natural language. The natural language processing is currently often done with machine learning models. However, machine learning needs training data and such data are often scarce for low-resource languages. The lack of data and resulting poor performance of natural language processing can be solved with cross-lingual learning. Cross-lingual learning is a paradigm for transferring knowledge from one natural language to another. The transfer of knowledge can help us overcome the lack of data in the target languages and create intelligent systems and machine learning models for languages, where it was not possible previously.},
	language = {en},
	urldate = {2023-01-21},
	journal = {Expert Systems with Applications},
	author = {Pikuliak, Matúš and Šimko, Marián and Bieliková, Mária},
	month = mar,
	year = {2021},
	pages = {113765},
}

@misc{noauthor_nlp4call_nodate,
	title = {{NLP4Call} 2023 : {Natural} {Language} {Processing} ({NLP}) for {Computer}-{Assisted} {Language} {Learning} ({NLP4CALL})},
	url = {http://wikicfp.com/cfp/servlet/event.showcfp?eventid=168273&copyownerid=176756},
	urldate = {2023-01-21},
}

@inproceedings{bryant_automatic_2017,
	address = {Vancouver, Canada},
	title = {Automatic {Annotation} and {Evaluation} of {Error} {Types} for {Grammatical} {Error} {Correction}},
	url = {https://aclanthology.org/P17-1074},
	doi = {10.18653/v1/P17-1074},
	abstract = {Until now, error type performance for Grammatical Error Correction (GEC) systems could only be measured in terms of recall because system output is not annotated. To overcome this problem, we introduce ERRANT, a grammatical ERRor ANnotation Toolkit designed to automatically extract edits from parallel original and corrected sentences and classify them according to a new, dataset-agnostic, rule-based framework. This not only facilitates error type evaluation at different levels of granularity, but can also be used to reduce annotator workload and standardise existing GEC datasets. Human experts rated the automatic edits as “Good” or “Acceptable” in at least 95\% of cases, so we applied ERRANT to the system output of the CoNLL-2014 shared task to carry out a detailed error type analysis for the first time.},
	urldate = {2023-01-21},
	booktitle = {Proceedings of the 55th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics} ({Volume} 1: {Long} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Bryant, Christopher and Felice, Mariano and Briscoe, Ted},
	month = jul,
	year = {2017},
	pages = {793--805},
}

@article{rozovskaya_grammar_2019,
	title = {Grammar {Error} {Correction} in {Morphologically} {Rich} {Languages}: {The} {Case} of {Russian}},
	volume = {7},
	issn = {2307-387X},
	shorttitle = {Grammar {Error} {Correction} in {Morphologically} {Rich} {Languages}},
	url = {https://doi.org/10.1162/tacl_a_00251},
	doi = {10.1162/tacl_a_00251},
	abstract = {Until now, most of the research in grammar error correction focused on English, and the problem has hardly been explored for other languages. We address the task of correcting writing mistakes in morphologically rich languages, with a focus on Russian. We present a corrected and error-tagged corpus of Russian learner writing and develop models that make use of existing state-of-the-art methods that have been well studied for English. Although impressive results have recently been achieved for grammar error correction of non-native English writing, these results are limited to domains where plentiful training data are available. Because annotation is extremely costly, these approaches are not suitable for the majority of domains and languages. We thus focus on methods that use “minimal supervision”; that is, those that do not rely on large amounts of annotated training data, and show how existing minimal-supervision approaches extend to a highly inflectional language such as Russian. The results demonstrate that these methods are particularly useful for correcting mistakes in grammatical phenomena that involve rich morphology.},
	urldate = {2023-01-21},
	journal = {Transactions of the Association for Computational Linguistics},
	author = {Rozovskaya, Alla and Roth, Dan},
	month = mar,
	year = {2019},
	pages = {1--17},
}

@inproceedings{boyd_using_2018,
	address = {Brussels, Belgium},
	title = {Using {Wikipedia} {Edits} in {Low} {Resource} {Grammatical} {Error} {Correction}},
	url = {https://aclanthology.org/W18-6111},
	doi = {10.18653/v1/W18-6111},
	abstract = {We develop a grammatical error correction (GEC) system for German using a small gold GEC corpus augmented with edits extracted from Wikipedia revision history. We extend the automatic error annotation tool ERRANT (Bryant et al., 2017) for German and use it to analyze both gold GEC corrections and Wikipedia edits (Grundkiewicz and Junczys-Dowmunt, 2014) in order to select as additional training data Wikipedia edits containing grammatical corrections similar to those in the gold corpus. Using a multilayer convolutional encoder-decoder neural network GEC approach (Chollampatt and Ng, 2018), we evaluate the contribution of Wikipedia edits and find that carefully selected Wikipedia edits increase performance by over 5\%.},
	urldate = {2023-01-21},
	booktitle = {Proceedings of the 2018 {EMNLP} {Workshop} {W}-{NUT}: {The} 4th {Workshop} on {Noisy} {User}-generated {Text}},
	publisher = {Association for Computational Linguistics},
	author = {Boyd, Adriane},
	month = nov,
	year = {2018},
	pages = {79--84},
}

@misc{rajput_hate_2022,
	title = {Hate {Me} {Not}: {Detecting} {Hate} {Inducing} {Memes} in {Code} {Switched} {Languages}},
	shorttitle = {Hate {Me} {Not}},
	url = {http://arxiv.org/abs/2204.11356},
	doi = {10.48550/arXiv.2204.11356},
	abstract = {The rise in the number of social media users has led to an increase in the hateful content posted online. In countries like India, where multiple languages are spoken, these abhorrent posts are from an unusual blend of code-switched languages. This hate speech is depicted with the help of images to form "Memes" which create a long-lasting impact on the human mind. In this paper, we take up the task of hate and offense detection from multimodal data, i.e. images (Memes) that contain text in code-switched languages. We firstly present a novel triply annotated Indian political Memes (IPM) dataset, which comprises memes from various Indian political events that have taken place post-independence and are classified into three distinct categories. We also propose a binary-channelled CNN cum LSTM based model to process the images using the CNN model and text using the LSTM model to get state-of-the-art results for this task.},
	urldate = {2023-01-21},
	publisher = {arXiv},
	author = {Rajput, Kshitij and Kapoor, Raghav and Rai, Kaushal and Kaur, Preeti},
	month = apr,
	year = {2022},
	note = {arXiv:2204.11356 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Social and Information Networks},
}

@inproceedings{felice_grammatical_2014,
	title = {Grammatical error correction using hybrid systems and type filtering},
	booktitle = {Proceedings of the {Eighteenth} {Conference} on {Computational} {Natural} {Language} {Learning}: {Shared} {Task}},
	author = {Felice, Mariano and Yuan, Zheng and Andersen, Øistein E. and Yannakoudakis, Helen and Kochmar, Ekaterina},
	year = {2014},
	pages = {15--24},
}

@inproceedings{garrette_unsupervised_2015,
	address = {Denver, Colorado},
	title = {Unsupervised {Code}-{Switching} for {Multilingual} {Historical} {Document} {Transcription}},
	url = {https://aclanthology.org/N15-1109},
	doi = {10.3115/v1/N15-1109},
	urldate = {2023-01-21},
	booktitle = {Proceedings of the 2015 {Conference} of the {North} {American} {Chapter} of the {Association} for {Computational} {Linguistics}: {Human} {Language} {Technologies}},
	publisher = {Association for Computational Linguistics},
	author = {Garrette, Dan and Alpert-Abrams, Hannah and Berg-Kirkpatrick, Taylor and Klein, Dan},
	month = may,
	year = {2015},
	pages = {1036--1041},
}

@misc{noauthor_bea_nodate,
	title = {{BEA} 2019 {Shared} {Task}: {Grammatical} {Error} {Correction}},
	url = {https://www.cl.cam.ac.uk/research/nl/bea2019st/},
	urldate = {2023-01-21},
}

@misc{noauthor_gluecos_nodate,
	title = {{GLUECoS}},
	url = {https://microsoft.github.io/GLUECoS/},
	urldate = {2023-01-20},
}

@misc{caines_teacher-student_2020,
	title = {The {Teacher}-{Student} {Chatroom} {Corpus}},
	url = {http://arxiv.org/abs/2011.07109},
	doi = {10.48550/arXiv.2011.07109},
	abstract = {The Teacher-Student Chatroom Corpus (TSCC) is a collection of written conversations captured during one-to-one lessons between teachers and learners of English. The lessons took place in an online chatroom and therefore involve more interactive, immediate and informal language than might be found in asynchronous exchanges such as email correspondence. The fact that the lessons were one-to-one means that the teacher was able to focus exclusively on the linguistic abilities and errors of the student, and to offer personalised exercises, scaffolding and correction. The TSCC contains more than one hundred lessons between two teachers and eight students, amounting to 13.5K conversational turns and 133K words: it is freely available for research use. We describe the corpus design, data collection procedure and annotations added to the text. We perform some preliminary descriptive analyses of the data and consider possible uses of the TSCC.},
	urldate = {2023-01-20},
	publisher = {arXiv},
	author = {Caines, Andrew and Yannakoudakis, Helen and Edmondson, Helena and Allen, Helen and Pérez-Paredes, Pascual and Byrne, Bill and Buttery, Paula},
	month = nov,
	year = {2020},
	note = {arXiv:2011.07109 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@inproceedings{caines_crowdsourcing_2016,
	address = {Portorož, Slovenia},
	title = {Crowdsourcing a {Multi}-lingual {Speech} {Corpus}: {Recording}, {Transcription} and {Annotation} of the {CrowdIS} {Corpora}},
	shorttitle = {Crowdsourcing a {Multi}-lingual {Speech} {Corpus}},
	url = {https://aclanthology.org/L16-1340},
	abstract = {We announce the release of the CROWDED CORPUS: a pair of speech corpora collected via crowdsourcing, containing a native speaker corpus of English (CROWDED\_ENGLISH), and a corpus of German/English bilinguals (CROWDED\_BILINGUAL). Release 1 of the CROWDED CORPUS contains 1000 recordings amounting to 33,400 tokens collected from 80 speakers and is freely available to other researchers. We recruited participants via the Crowdee application for Android. Recruits were prompted to respond to business-topic questions of the type found in language learning oral tests. We then used the CrowdFlower web application to pass these recordings to crowdworkers for transcription and annotation of errors and sentence boundaries. Finally, the sentences were tagged and parsed using standard natural language processing tools. We propose that crowdsourcing is a valid and economical method for corpus collection, and discuss the advantages and disadvantages of this approach.},
	urldate = {2023-01-20},
	booktitle = {Proceedings of the {Tenth} {International} {Conference} on {Language} {Resources} and {Evaluation} ({LREC}'16)},
	publisher = {European Language Resources Association (ELRA)},
	author = {Caines, Andrew and Bentz, Christian and Graham, Calbert and Polzehl, Tim and Buttery, Paula},
	month = may,
	year = {2016},
	pages = {2145--2152},
}

@book{bullock_cambridge_2009,
	address = {Cambridge},
	series = {Cambridge {Handbooks} in {Language} and {Linguistics}},
	title = {The {Cambridge} {Handbook} of {Linguistic} {Code}-switching},
	isbn = {978-0-521-87591-2},
	url = {https://www.cambridge.org/core/books/cambridge-handbook-of-linguistic-codeswitching/8C8AC699496D7687F5625819856859A7},
	abstract = {Code-switching – the alternating use of two languages in the same stretch of discourse by a bilingual speaker – is a dominant topic in the study of bilingualism and a phenomenon that generates a great deal of pointed discussion in the public domain. This handbook provides the most comprehensive guide to this bilingual phenomenon to date. Drawing on empirical data from a wide range of language pairings, the leading researchers in the study of bilingualism examine the linguistic, social and cognitive implications of code-switching in up-to-date and accessible survey chapters. The Cambridge Handbook of Linguistic Code-switching will serve as a vital resource for advanced undergraduate and graduate students, as a wide-ranging overview for linguists, psychologists and speech scientists and as an informative guide for educators interested in bilingual speech practices.},
	urldate = {2023-01-20},
	publisher = {Cambridge University Press},
	editor = {Bullock, Barbara E. and Toribio, Almeida Jacqueline},
	year = {2009},
	doi = {10.1017/CBO9780511576331},
}

@misc{noauthor_17th_nodate,
	title = {17th {Workshop} on {Innovative} {Use} of {NLP} for {Building} {Educational} {Applications}},
	url = {https://sig-edu.org/bea/2022},
	abstract = {ACL Special Interest Group for Building Educational Applications.},
	language = {en},
	urldate = {2023-01-20},
	journal = {SIGEDU},
}

@misc{noauthor_17th_nodate-1,
	title = {The 17th {Workshop} on {Innovative} {Use} of {NLP} for {Building} {Educational} {Applications} ({BEA17}) {\textbar} {ACL} {Member} {Portal}},
	url = {https://www.aclweb.org/portal/content/17th-workshop-innovative-use-nlp-building-educational-applications-bea17},
	urldate = {2023-01-20},
}

@inproceedings{baur_overview_2019,
	title = {Overview of the 2019 {Spoken} {CALL} {Shared} {Task}},
	url = {http://www.isca-speech.org/archive/SLaTE_2019/abstracts/SLaTE_2019_paper_07.html},
	doi = {10.21437/SLaTE.2019-1},
	abstract = {We present an overview of the third edition of the Spoken CALL Shared Task. Groups competed on a prompt-response task using English-language data collected, through an online CALL game, from Swiss German teens in their second and third years of learning English. Each item consists of a written German prompt and an audio ﬁle containing a spoken response. The task is to accept linguistically correct responses and reject linguistically incorrect ones, with “linguistically correct” deﬁned by a gold standard derived from human annotations. Scoring was performed using a metric based on the idea of maximising the ratios correct-accept-rate/false-accept-rate and correct-reject-rate/false-reject-rate. The third edition received sixteen entries, with the best score substantially improving on last year’s edition of the task. We analyse factors which make it difﬁcult to label items correctly, concluding that, as in the previous edition, good speech recognition is most important. Finally, we suggest a strategy for continuing the task.},
	language = {en},
	urldate = {2023-01-20},
	booktitle = {{SLaTE} 2019: 8th {ISCA} {Workshop} on {Speech} and {Language} {Technology} in {Education}},
	publisher = {ISCA},
	author = {Baur, Claudia and Caines, Andrew and Chua, Cathy and Gerlach, Johanna and Qian, Mengjie and Rayner, Manny and Russell, Martin and Strik, Helmer and Wei, Xizi},
	month = sep,
	year = {2019},
	pages = {1--5},
}

@article{baur_overview_2017,
	title = {Overview of the 2017 {Spoken} {CALL} {Shared} {Task}},
	url = {https://archive-ouverte.unige.ch/unige:97428},
	abstract = {We present an overview of the shared task for spoken CALL. Groups competed on a prompt-response task using English-language data collected, through an online CALL game, from Swiss German teens in their second and third years of learning English. Each item consists of a written German prompt and an audio file containing a spoken response. The task is to accept linguistically correct responses and reject linguistically incorrect ones, with “linguistically correct” being defined by a gold standard derived from human annotations; scoring was performed using a metric defined as the ratio of the relative rejection rates on incorrect and correct responses. The task received twenty entries from nine different groups. We present the task itself, the results, a tentative analysis of what makes items challenging, a comparison between different metrics, and suggestions for a continuation.},
	language = {eng},
	urldate = {2023-01-20},
	journal = {Workshop on Speech and Language Technology in Education (SLaTE)},
	author = {Baur, Claudia and Chua, Cathy and Gerlach, Johanna and Rayner, Emmanuel and Russel, Martin and Strik, Helmer and Wei, Xizi},
	year = {2017},
}

@misc{pires_how_2019,
	title = {How multilingual is {Multilingual} {BERT}?},
	url = {http://arxiv.org/abs/1906.01502},
	abstract = {In this paper, we show that Multilingual BERT (M-BERT), released by Devlin et al. (2018) as a single language model pre-trained from monolingual corpora in 104 languages, is surprisingly good at zero-shot cross-lingual model transfer, in which task-specific annotations in one language are used to fine-tune the model for evaluation in another language. To understand why, we present a large number of probing experiments, showing that transfer is possible even to languages in different scripts, that transfer works best between typologically similar languages, that monolingual corpora can train models for code-switching, and that the model can find translation pairs. From these results, we can conclude that M-BERT does create multilingual representations, but that these representations exhibit systematic deficiencies affecting certain language pairs.},
	urldate = {2023-01-20},
	publisher = {arXiv},
	author = {Pires, Telmo and Schlinger, Eva and Garrette, Dan},
	month = jun,
	year = {2019},
	note = {arXiv:1906.01502 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{lample_cross-lingual_2019,
	title = {Cross-lingual {Language} {Model} {Pretraining}},
	url = {http://arxiv.org/abs/1901.07291},
	abstract = {Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding. In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our approach pushes the state of the art by an absolute gain of 4.9\% accuracy. On unsupervised machine translation, we obtain 34.3 BLEU on WMT'16 German-English, improving the previous state of the art by more than 9 BLEU. On supervised machine translation, we obtain a new state of the art of 38.5 BLEU on WMT'16 Romanian-English, outperforming the previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available.},
	urldate = {2023-01-20},
	publisher = {arXiv},
	author = {Lample, Guillaume and Conneau, Alexis},
	month = jan,
	year = {2019},
	note = {arXiv:1901.07291 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@inproceedings{winata_learning_2019,
	address = {Florence, Italy},
	title = {Learning {Multilingual} {Meta}-{Embeddings} for {Code}-{Switching} {Named} {Entity} {Recognition}},
	url = {https://aclanthology.org/W19-4320},
	doi = {10.18653/v1/W19-4320},
	abstract = {In this paper, we propose Multilingual Meta-Embeddings (MME), an effective method to learn multilingual representations by leveraging monolingual pre-trained embeddings. MME learns to utilize information from these embeddings via a self-attention mechanism without explicit language identification. We evaluate the proposed embedding method on the code-switching English-Spanish Named Entity Recognition dataset in a multilingual and cross-lingual setting. The experimental results show that our proposed method achieves state-of-the-art performance on the multilingual setting, and it has the ability to generalize to an unseen language task.},
	urldate = {2023-01-20},
	booktitle = {Proceedings of the 4th {Workshop} on {Representation} {Learning} for {NLP} ({RepL4NLP}-2019)},
	publisher = {Association for Computational Linguistics},
	author = {Winata, Genta Indra and Lin, Zhaojiang and Fung, Pascale},
	month = aug,
	year = {2019},
	pages = {181--186},
}

@misc{aryal_sentiment_2022,
	title = {Sentiment {Classification} of {Code}-{Switched} {Text} using {Pre}-trained {Multilingual} {Embeddings} and {Segmentation}},
	url = {http://arxiv.org/abs/2210.16461},
	doi = {10.48550/arXiv.2210.16461},
	abstract = {With increasing globalization and immigration, various studies have estimated that about half of the world population is bilingual. Consequently, individuals concurrently use two or more languages or dialects in casual conversational settings. However, most research is natural language processing is focused on monolingual text. To further the work in code-switched sentiment analysis, we propose a multi-step natural language processing algorithm utilizing points of code-switching in mixed text and conduct sentiment analysis around those identified points. The proposed sentiment analysis algorithm uses semantic similarity derived from large pre-trained multilingual models with a handcrafted set of positive and negative words to determine the polarity of code-switched text. The proposed approach outperforms a comparable baseline model by 11.2\% for accuracy and 11.64\% for F1-score on a Spanish-English dataset. Theoretically, the proposed algorithm can be expanded for sentiment analysis of multiple languages with limited human expertise.},
	urldate = {2023-01-20},
	publisher = {arXiv},
	author = {Aryal, Saurav K. and Prioleau, Howard and Washington, Gloria},
	month = oct,
	year = {2022},
	note = {arXiv:2210.16461 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

@article{severyn_multi-lingual_2016,
	title = {Multi-lingual opinion mining on {YouTube}},
	volume = {52},
	number = {1},
	journal = {Information Processing \& Management},
	author = {Severyn, Aliaksei and Moschitti, Alessandro and Uryupina, Olga and Plank, Barbara and Filippova, Katja},
	year = {2016},
	note = {Publisher: Elsevier},
	pages = {46--60},
}

@inproceedings{winata_are_2021,
	address = {Online},
	title = {Are {Multilingual} {Models} {Effective} in {Code}-{Switching}?},
	url = {https://aclanthology.org/2021.calcs-1.20},
	doi = {10.18653/v1/2021.calcs-1.20},
	abstract = {Multilingual language models have shown decent performance in multilingual and cross-lingual natural language understanding tasks. However, the power of these multilingual models in code-switching tasks has not been fully explored. In this paper, we study the effectiveness of multilingual language models to understand their capability and adaptability to the mixed-language setting by considering the inference speed, performance, and number of parameters to measure their practicality. We conduct experiments in three language pairs on named entity recognition and part-of-speech tagging and compare them with existing methods, such as using bilingual embeddings and multilingual meta-embeddings. Our findings suggest that pre-trained multilingual models do not necessarily guarantee high-quality representations on code-switching, while using meta-embeddings achieves similar results with significantly fewer parameters.},
	urldate = {2023-01-20},
	booktitle = {Proceedings of the {Fifth} {Workshop} on {Computational} {Approaches} to {Linguistic} {Code}-{Switching}},
	publisher = {Association for Computational Linguistics},
	author = {Winata, Genta Indra and Cahyawijaya, Samuel and Liu, Zihan and Lin, Zhaojiang and Madotto, Andrea and Fung, Pascale},
	month = jun,
	year = {2021},
	pages = {142--153},
}

@article{artetxe_massively_2019,
	title = {Massively {Multilingual} {Sentence} {Embeddings} for {Zero}-{Shot} {Cross}-{Lingual} {Transfer} and {Beyond}},
	volume = {7},
	issn = {2307-387X},
	url = {http://arxiv.org/abs/1812.10464},
	doi = {10.1162/tacl_a_00288},
	abstract = {We introduce an architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different families and written in 28 different scripts. Our system uses a single BiLSTM encoder with a shared BPE vocabulary for all languages, which is coupled with an auxiliary decoder and trained on publicly available parallel corpora. This enables us to learn a classifier on top of the resulting embeddings using English annotated data only, and transfer it to any of the 93 languages without any modification. Our experiments in cross-lingual natural language inference (XNLI dataset), cross-lingual document classification (MLDoc dataset) and parallel corpus mining (BUCC dataset) show the effectiveness of our approach. We also introduce a new test set of aligned sentences in 112 languages, and show that our sentence embeddings obtain strong results in multilingual similarity search even for low-resource languages. Our implementation, the pre-trained encoder and the multilingual test set are available at https://github.com/facebookresearch/LASER},
	urldate = {2023-01-20},
	journal = {Transactions of the Association for Computational Linguistics},
	author = {Artetxe, Mikel and Schwenk, Holger},
	month = nov,
	year = {2019},
	note = {arXiv:1812.10464 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning},
	pages = {597--610},
}

@article{authors_investigating_nodate,
	title = {Investigating {Effects} of {Randomness} on the  {Stability} of {Learning} with {Limited} {Labelled} {Data}},
	language = {en},
	author = {Authors, Anonymous},
}

@inproceedings{grundkiewicz_neural_2019,
	address = {Florence, Italy},
	title = {Neural {Grammatical} {Error} {Correction} {Systems} with {Unsupervised} {Pre}-training on {Synthetic} {Data}},
	url = {https://aclanthology.org/W19-4427},
	doi = {10.18653/v1/W19-4427},
	abstract = {Considerable effort has been made to address the data sparsity problem in neural grammatical error correction. In this work, we propose a simple and surprisingly effective unsupervised synthetic error generation method based on confusion sets extracted from a spellchecker to increase the amount of training data. Synthetic data is used to pre-train a Transformer sequence-to-sequence model, which not only improves over a strong baseline trained on authentic error-annotated data, but also enables the development of a practical GEC system in a scenario where little genuine error-annotated data is available. The developed systems placed first in the BEA19 shared task, achieving 69.47 and 64.24 F0.5 in the restricted and low-resource tracks respectively, both on the W\&I+LOCNESS test set. On the popular CoNLL 2014 test set, we report state-of-the-art results of 64.16 M{\textbackslash}mbox\${\textasciicircum}2\$ for the submitted system, and 61.30 M{\textbackslash}mbox\${\textasciicircum}2\$ for the constrained system trained on the NUCLE and Lang-8 data.},
	urldate = {2023-01-20},
	booktitle = {Proceedings of the {Fourteenth} {Workshop} on {Innovative} {Use} of {NLP} for {Building} {Educational} {Applications}},
	publisher = {Association for Computational Linguistics},
	author = {Grundkiewicz, Roman and Junczys-Dowmunt, Marcin and Heafield, Kenneth},
	month = aug,
	year = {2019},
	pages = {252--263},
}

@misc{naplava_grammatical_2019,
	title = {Grammatical {Error} {Correction} in {Low}-{Resource} {Scenarios}},
	url = {http://arxiv.org/abs/1910.00353},
	doi = {10.48550/arXiv.1910.00353},
	abstract = {Grammatical error correction in English is a long studied problem with many existing systems and datasets. However, there has been only a limited research on error correction of other languages. In this paper, we present a new dataset AKCES-GEC on grammatical error correction for Czech. We then make experiments on Czech, German and Russian and show that when utilizing synthetic parallel corpus, Transformer neural machine translation model can reach new state-of-the-art results on these datasets. AKCES-GEC is published under CC BY-NC-SA 4.0 license at https://hdl.handle.net/11234/1-3057 and the source code of the GEC model is available at https://github.com/ufal/low-resource-gec-wnut2019.},
	urldate = {2023-01-20},
	publisher = {arXiv},
	author = {Náplava, Jakub and Straka, Milan},
	month = oct,
	year = {2019},
	note = {arXiv:1910.00353 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@article{mustafa_code-switching_2022,
	title = {Code-{Switching} in {Automatic} {Speech} {Recognition}: {The} {Issues} and {Future} {Directions}},
	volume = {12},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2076-3417},
	shorttitle = {Code-{Switching} in {Automatic} {Speech} {Recognition}},
	url = {https://www.mdpi.com/2076-3417/12/19/9541},
	doi = {10.3390/app12199541},
	abstract = {Code-switching (CS) in spoken language is where the speech has two or more languages within an utterance. It is an unsolved issue in automatic speech recognition (ASR) research as ASR needs to recognise speech in bilingual and multilingual settings, where the accuracy of ASR systems declines with CS due to pronunciation variation. There are very few reviews carried out on CS, with none conducted on bilingual and multilingual CS ASR systems. This study investigates the importance of CS in bilingual and multilingual speech recognition systems. To meet the objective of this study, two research questions were formulated, which cover both the current issues and the direction of the research. Our review focuses on databases, acoustic and language modelling, and evaluation metrics. Using selected keywords, this research has identified 274 papers and selected 42 experimental papers for review, of which 24 (representing 57\%) have discussed CS, while the rest look at multilingual ASR research. The selected papers cover many well-resourced and under-resourced languages, and novel techniques to manage CS in ASR systems, which are mapping, combining and merging the phone sets of the languages experimented with in the research. Our review also examines the performance of those methods. This review found a significant variation in the performance of CS in terms of word error rates, indicating an inconsistency in the ability of ASRs to handle CS. In the conclusion, we suggest several future directions that address the issues identified in this review.},
	language = {en},
	number = {19},
	urldate = {2023-01-20},
	journal = {Applied Sciences},
	author = {Mustafa, Mumtaz Begum and Yusoof, Mansoor Ali and Khalaf, Hasan Kahtan and Rahman Mahmoud Abushariah, Ahmad Abdel and Kiah, Miss Laiha Mat and Ting, Hua Nong and Muthaiyah, Saravanan},
	month = jan,
	year = {2022},
	note = {Number: 19
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {automatic speech recognition system, bilingual speech recognition, code-switching, evaluation metrics, language and acoustic models, multilingual speech recognition},
	pages = {9541},
}

@misc{noauthor_kinit_nodate,
	title = {{KInIT} {Diakritikovač} - {Automatické} doplňovanie diakritiky pomocou umelej inteligencie},
	url = {https://diakritikovac.kinit.sk/},
	abstract = {Doplnok pre Google dokumenty pre rýchle a automatizované doplňovanie aj odstraňovanie diakritiky v slovenských online dokumentoch.},
	urldate = {2023-01-20},
	journal = {KInIT Diakritikovač},
}

@article{diwan_multilingual_nodate,
	title = {Multilingual, {Code}-switching and {Low}-{Resource} {NLP} and {ASR}},
	language = {en},
	author = {Diwan, Anuj Jitendra},
}

@misc{chowdhury_towards_2021,
	title = {Towards {One} {Model} to {Rule} {All}: {Multilingual} {Strategy} for {Dialectal} {Code}-{Switching} {Arabic} {ASR}},
	shorttitle = {Towards {One} {Model} to {Rule} {All}},
	url = {http://arxiv.org/abs/2105.14779},
	doi = {10.48550/arXiv.2105.14779},
	abstract = {With the advent of globalization, there is an increasing demand for multilingual automatic speech recognition (ASR), handling language and dialectal variation of spoken content. Recent studies show its efficacy over monolingual systems. In this study, we design a large multilingual end-to-end ASR using self-attention based conformer architecture. We trained the system using Arabic (Ar), English (En) and French (Fr) languages. We evaluate the system performance handling: (i) monolingual (Ar, En and Fr); (ii) multi-dialectal (Modern Standard Arabic, along with dialectal variation such as Egyptian and Moroccan); (iii) code-switching -- cross-lingual (Ar-En/Fr) and dialectal (MSA-Egyptian dialect) test cases, and compare with current state-of-the-art systems. Furthermore, we investigate the influence of different embedding/character representations including character vs word-piece; shared vs distinct input symbol per language. Our findings demonstrate the strength of such a model by outperforming state-of-the-art monolingual dialectal Arabic and code-switching Arabic ASR.},
	urldate = {2023-01-19},
	publisher = {arXiv},
	author = {Chowdhury, Shammur Absar and Hussein, Amir and Abdelali, Ahmed and Ali, Ahmed},
	month = jul,
	year = {2021},
	note = {arXiv:2105.14779 [cs, eess]},
	keywords = {Computer Science - Computation and Language, Computer Science - Human-Computer Interaction, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
}

@inproceedings{krishnan_multilingual_2021,
	address = {Punta Cana, Dominican Republic},
	title = {Multilingual {Code}-{Switching} for {Zero}-{Shot} {Cross}-{Lingual} {Intent} {Prediction} and {Slot} {Filling}},
	url = {https://aclanthology.org/2021.mrl-1.18},
	doi = {10.18653/v1/2021.mrl-1.18},
	abstract = {Predicting user intent and detecting the corresponding slots from text are two key problems in Natural Language Understanding (NLU). Since annotated datasets are only available for a handful of languages, our work focuses particularly on a zero-shot scenario where the target language is unseen during training. In the context of zero-shot learning, this task is typically approached using representations from pre-trained multilingual language models such as mBERT or by fine-tuning on data automatically translated into the target language. We propose a novel method which augments monolingual source data using multilingual code-switching via random translations, to enhance generalizability of large multilingual language models when fine-tuning them for downstream tasks. Experiments on the MultiATIS++ benchmark show that our method leads to an average improvement of +4.2\% in accuracy for the intent task and +1.8\% in F1 for the slot-filling task over the state-of-the-art across 8 typologically diverse languages. We also study the impact of code-switching into different families of languages on downstream performance. Furthermore, we present an application of our method for crisis informatics using a new human-annotated tweet dataset of slot filling in English and Haitian Creole, collected during the Haiti earthquake.},
	urldate = {2023-01-19},
	booktitle = {Proceedings of the 1st {Workshop} on {Multilingual} {Representation} {Learning}},
	publisher = {Association for Computational Linguistics},
	author = {Krishnan, Jitin and Anastasopoulos, Antonios and Purohit, Hemant and Rangwala, Huzefa},
	month = nov,
	year = {2021},
	pages = {211--223},
}

@inproceedings{rei_compositional_2016,
	address = {Berlin, Germany},
	title = {Compositional {Sequence} {Labeling} {Models} for {Error} {Detection} in {Learner} {Writing}},
	url = {https://aclanthology.org/P16-1112},
	doi = {10.18653/v1/P16-1112},
	urldate = {2023-01-19},
	booktitle = {Proceedings of the 54th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics} ({Volume} 1: {Long} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Rei, Marek and Yannakoudakis, Helen},
	month = aug,
	year = {2016},
	pages = {1181--1191},
}

@article{andersen_benets_nodate,
	title = {Beneﬁts of alternative evaluation methods for {Automated} {Essay} {Scoring}},
	abstract = {Automated essay scoring (AES), where natural language processing is applied to score written text, can underpin educational resources in blended and distance learning. AES performance has typically been reported in terms of correlation coeﬃcients or agreement statistics calculated between a system and an expert human examiner. We describe the beneﬁts of alternative methods to evaluate AES systems and, more importantly, facilitate comparison between AES systems and expert human examiners. We employ these methods, together with multi-marked test data labelled by 5 expert human examiners, to guide machine learning model development and selection, resulting in models that outperform expert human examiners.},
	language = {en},
	author = {Andersen, Øistein E and Yuan, Zheng and Watson, Rebecca},
}

@inproceedings{mayfield_should_2020,
	address = {Seattle, WA, USA → Online},
	title = {Should {You} {Fine}-{Tune} {BERT} for {Automated} {Essay} {Scoring}?},
	url = {https://aclanthology.org/2020.bea-1.15},
	doi = {10.18653/v1/2020.bea-1.15},
	abstract = {Most natural language processing research now recommends large Transformer-based models with fine-tuning for supervised classification tasks; older strategies like bag-of-words features and linear models have fallen out of favor. Here we investigate whether, in automated essay scoring (AES) research, deep neural models are an appropriate technological choice. We find that fine-tuning BERT produces similar performance to classical models at significant additional cost. We argue that while state-of-the-art strategies do match existing best results, they come with opportunity costs in computational resources. We conclude with a review of promising areas for research on student essays where the unique characteristics of Transformers may provide benefits over classical methods to justify the costs.},
	urldate = {2023-01-19},
	booktitle = {Proceedings of the {Fifteenth} {Workshop} on {Innovative} {Use} of {NLP} for {Building} {Educational} {Applications}},
	publisher = {Association for Computational Linguistics},
	author = {Mayfield, Elijah and Black, Alan W},
	month = jul,
	year = {2020},
	pages = {151--162},
}

@inproceedings{yuan_multi-class_2021,
	address = {Online and Punta Cana, Dominican Republic},
	title = {Multi-{Class} {Grammatical} {Error} {Detection} for {Correction}: {A} {Tale} of {Two} {Systems}},
	shorttitle = {Multi-{Class} {Grammatical} {Error} {Detection} for {Correction}},
	url = {https://aclanthology.org/2021.emnlp-main.687},
	doi = {10.18653/v1/2021.emnlp-main.687},
	abstract = {In this paper, we show how a multi-class grammatical error detection (GED) system can be used to improve grammatical error correction (GEC) for English. Specifically, we first develop a new state-of-the-art binary detection system based on pre-trained ELECTRA, and then extend it to multi-class detection using different error type tagsets derived from the ERRANT framework. Output from this detection system is used as auxiliary input to fine-tune a novel encoder-decoder GEC model, and we subsequently re-rank the N-best GEC output to find the hypothesis that most agrees with the GED output. Results show that fine-tuning the GEC system using 4-class GED produces the best model, but re-ranking using 55-class GED leads to the best performance overall. This suggests that different multi-class GED systems benefit GEC in different ways. Ultimately, our system outperforms all other previous work that combines GED and GEC, and achieves a new single-model NMT-based state of the art on the BEA-test benchmark.},
	urldate = {2023-01-19},
	booktitle = {Proceedings of the 2021 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Yuan, Zheng and Taslimipoor, Shiva and Davis, Christopher and Bryant, Christopher},
	month = nov,
	year = {2021},
	pages = {8722--8736},
}

@inproceedings{rothe_simple_2021,
	address = {Online},
	title = {A {Simple} {Recipe} for {Multilingual} {Grammatical} {Error} {Correction}},
	url = {https://aclanthology.org/2021.acl-short.89},
	doi = {10.18653/v1/2021.acl-short.89},
	abstract = {This paper presents a simple recipe to trainstate-of-the-art multilingual Grammatical Error Correction (GEC) models. We achieve this by first proposing a language-agnostic method to generate a large number of synthetic examples. The second ingredient is to use large-scale multilingual language models (up to 11B parameters). Once fine-tuned on language-specific supervised sets we surpass the previous state-of-the-art results on GEC benchmarks in four languages: English, Czech, German and Russian. Having established a new set of baselines for GEC, we make our results easily reproducible and accessible by releasing a CLANG-8 dataset. It is produced by using our best model, which we call gT5, to clean the targets of a widely used yet noisy Lang-8 dataset. cLang-8 greatly simplifies typical GEC training pipelines composed of multiple fine-tuning stages – we demonstrate that performing a single fine-tuning stepon cLang-8 with the off-the-shelf language models yields further accuracy improvements over an already top-performing gT5 model for English.},
	urldate = {2023-01-19},
	booktitle = {Proceedings of the 59th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics} and the 11th {International} {Joint} {Conference} on {Natural} {Language} {Processing} ({Volume} 2: {Short} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Rothe, Sascha and Mallinson, Jonathan and Malmi, Eric and Krause, Sebastian and Severyn, Aliaksei},
	month = aug,
	year = {2021},
	pages = {702--707},
}

@inproceedings{katsumata_stronger_2020,
	address = {Suzhou, China},
	title = {Stronger {Baselines} for {Grammatical} {Error} {Correction} {Using} a {Pretrained} {Encoder}-{Decoder} {Model}},
	url = {https://aclanthology.org/2020.aacl-main.83},
	abstract = {Studies on grammatical error correction (GEC) have reported on the effectiveness of pretraining a Seq2Seq model with a large amount of pseudodata. However, this approach requires time-consuming pretraining of GEC because of the size of the pseudodata. In this study, we explored the utility of bidirectional and auto-regressive transformers (BART) as a generic pretrained encoder-decoder model for GEC. With the use of this generic pretrained model for GEC, the time-consuming pretraining can be eliminated. We find that monolingual and multilingual BART models achieve high performance in GEC, with one of the results being comparable to the current strong results in English GEC.},
	urldate = {2023-01-19},
	booktitle = {Proceedings of the 1st {Conference} of the {Asia}-{Pacific} {Chapter} of the {Association} for {Computational} {Linguistics} and the 10th {International} {Joint} {Conference} on {Natural} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Katsumata, Satoru and Komachi, Mamoru},
	month = dec,
	year = {2020},
	pages = {827--832},
}

@misc{noauthor_computer_nodate,
	title = {Computer {Laboratory} – {Natural} {Language} and {Information} {Processing} {Research} {Group}: {Resources}},
	url = {https://www.cl.cam.ac.uk/research/nl/resources/},
	urldate = {2023-01-19},
}

@article{nguyen_building_2022,
	title = {Building {Educational} {Technologies} for {Code}-{Switching}: {Current} {Practices}, {Difficulties} and {Future} {Directions}},
	volume = {7},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2226-471X},
	shorttitle = {Building {Educational} {Technologies} for {Code}-{Switching}},
	url = {https://www.mdpi.com/2226-471X/7/3/220},
	doi = {10.3390/languages7030220},
	abstract = {Code-switching (CSW) is the phenomenon where speakers use two or more languages in a single discourse or utterance—an increasingly recognised natural product of multilingualism in many settings. In language teaching and learning in particular, code-switching has been shown to bring in many pedagogical benefits, including accelerating students’ confidence, increasing their access to content, as well as improving their participation and engagement. Unfortunately, however, current educational technologies are not yet able to keep up with this ‘multilingual turn’ in education, and are partly responsible for the constraint of this practice to only classroom contexts. In an effort to make progress in this area, we offer a data-driven position paper discussing the current state of affairs, difficulties of the existing educational natural language processing (NLP) tools for CSW and possible directions for future work. We specifically focus on two cases of feedback and assessment technologies, demonstrating how the current state-of-the-art in these domains fails with code-switching data due to a lack of appropriate training data, lack of robust evaluation benchmarks and lack of end-to-end user-facing educational applications. We present some empirical user cases of how CSW manifests and suggest possible technological solutions for each of these scenarios.},
	language = {en},
	number = {3},
	urldate = {2023-01-19},
	journal = {Languages},
	author = {Nguyen, Li and Yuan, Zheng and Seed, Graham},
	month = sep,
	year = {2022},
	note = {Number: 3
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {English as a second language, automated assessment, automated feedback, code-switching, educational technology, language education, plurilingualism, translanguaging},
	pages = {220},
}

@article{pikuliak_cross-lingual_2021-1,
	title = {Cross-lingual learning for text processing: {A} survey},
	volume = {165},
	issn = {0957-4174},
	shorttitle = {Cross-lingual learning for text processing},
	url = {https://www.sciencedirect.com/science/article/pii/S0957417420305893},
	doi = {10.1016/j.eswa.2020.113765},
	abstract = {Many intelligent systems in business, government or academy process natural language as an input during inference or they might even communicate with users in natural language. The natural language processing is currently often done with machine learning models. However, machine learning needs training data and such data are often scarce for low-resource languages. The lack of data and resulting poor performance of natural language processing can be solved with cross-lingual learning. Cross-lingual learning is a paradigm for transferring knowledge from one natural language to another. The transfer of knowledge can help us overcome the lack of data in the target languages and create intelligent systems and machine learning models for languages, where it was not possible previously. Despite its increasing popularity and potential, no comprehensive survey on cross-lingual learning was conducted so far. We survey 173 text processing cross-lingual learning papers and examine tasks, datasets and languages that were used. The most important contribution of our work is that we identify and analyze four types of cross-lingual transfer based on “what” is being transferred. Such insight might help other NLP researchers and practitioners to understand how to use cross-lingual learning for wide range of problems. In addition, we identify what we consider to be the most important research directions that might help the community to focus their future work in cross-lingual learning. We present a comprehensive table of all the surveyed papers with various data related to the cross-lingual learning techniques they use. The table can be used to find relevant papers and compare the approaches to cross-lingual learning. To the best of our knowledge, no survey of cross-lingual text processing techniques was done in this scope before.},
	language = {en},
	urldate = {2023-01-18},
	journal = {Expert Systems with Applications},
	author = {Pikuliak, Matúš and Šimko, Marián and Bieliková, Mária},
	month = mar,
	year = {2021},
	keywords = {Cross-lingual learning, Deep learning, Machine learning, Multilingual learning, Natural language processing, Text processing, Transfer learning},
	pages = {113765},
}

@misc{conneau_word_2018,
	title = {Word {Translation} {Without} {Parallel} {Data}},
	url = {http://arxiv.org/abs/1710.04087},
	abstract = {State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed encouraging results, they are not on par with their supervised counterparts and are limited to pairs of languages sharing a common alphabet. In this work, we show that we can build a bilingual dictionary between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way. Without using any character information, our model even outperforms existing supervised methods on cross-lingual tasks for some language pairs. Our experiments demonstrate that our method works very well also for distant language pairs, like English-Russian or English-Chinese. We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation. Our code, embeddings and dictionaries are publicly available.},
	urldate = {2022-08-28},
	publisher = {arXiv},
	author = {Conneau, Alexis and Lample, Guillaume and Ranzato, Marc'Aurelio and Denoyer, Ludovic and Jégou, Hervé},
	month = jan,
	year = {2018},
	note = {arXiv:1710.04087 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{lample_cross-lingual_2019-1,
	title = {Cross-lingual {Language} {Model} {Pretraining}},
	url = {http://arxiv.org/abs/1901.07291},
	abstract = {Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding. In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our approach pushes the state of the art by an absolute gain of 4.9\% accuracy. On unsupervised machine translation, we obtain 34.3 BLEU on WMT'16 German-English, improving the previous state of the art by more than 9 BLEU. On supervised machine translation, we obtain a new state of the art of 38.5 BLEU on WMT'16 Romanian-English, outperforming the previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available.},
	urldate = {2022-08-28},
	publisher = {arXiv},
	author = {Lample, Guillaume and Conneau, Alexis},
	month = jan,
	year = {2019},
	note = {arXiv:1901.07291 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@article{garcia_tutorial_2016,
	title = {A {Tutorial} on the {BM25F} {Model}},
	abstract = {This is a tutorial on the Best Match 25 Model with Extension to Multiple Weighted Fields , also known as the BM25F Model. Unlike BM25, the model is applicable to structured documents consisting of multiple fields. The model preserves term frequency nonlinearity and removes the independence assumption between same term occurrences.},
	author = {Garcia, Edel},
	month = oct,
	year = {2016},
}

@misc{noauthor_facebookresearchmuse_2022,
	title = {facebookresearch/{MUSE}},
	url = {https://github.com/facebookresearch/MUSE},
	abstract = {A library for Multilingual Unsupervised or Supervised word Embeddings},
	urldate = {2022-08-24},
	publisher = {Meta Research},
	month = aug,
	year = {2022},
	note = {original-date: 2017-10-23T18:20:39Z},
}

@article{artetxe_massively_2019-1,
	title = {Massively {Multilingual} {Sentence} {Embeddings} for {Zero}-{Shot} {Cross}-{Lingual} {Transfer} and {Beyond}},
	volume = {7},
	issn = {2307-387X},
	url = {http://arxiv.org/abs/1812.10464},
	doi = {10.1162/tacl_a_00288},
	abstract = {We introduce an architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different families and written in 28 different scripts. Our system uses a single BiLSTM encoder with a shared BPE vocabulary for all languages, which is coupled with an auxiliary decoder and trained on publicly available parallel corpora. This enables us to learn a classifier on top of the resulting embeddings using English annotated data only, and transfer it to any of the 93 languages without any modification. Our experiments in cross-lingual natural language inference (XNLI dataset), cross-lingual document classification (MLDoc dataset) and parallel corpus mining (BUCC dataset) show the effectiveness of our approach. We also introduce a new test set of aligned sentences in 112 languages, and show that our sentence embeddings obtain strong results in multilingual similarity search even for low-resource languages. Our implementation, the pre-trained encoder and the multilingual test set are available at https://github.com/facebookresearch/LASER},
	urldate = {2022-08-24},
	journal = {Transactions of the Association for Computational Linguistics},
	author = {Artetxe, Mikel and Schwenk, Holger},
	month = nov,
	year = {2019},
	note = {arXiv:1812.10464 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning},
	pages = {597--610},
}

@inproceedings{pan_multilingual_2021,
	address = {Online},
	title = {Multilingual {BERT} {Post}-{Pretraining} {Alignment}},
	url = {https://aclanthology.org/2021.naacl-main.20},
	doi = {10.18653/v1/2021.naacl-main.20},
	abstract = {We propose a simple method to align multilingual contextual embeddings as a post-pretraining step for improved cross-lingual transferability of the pretrained language models. Using parallel data, our method aligns embeddings on the word level through the recently proposed Translation Language Modeling objective as well as on the sentence level via contrastive learning and random input shuffling. We also perform sentence-level code-switching with English when finetuning on downstream tasks. On XNLI, our best model (initialized from mBERT) improves over mBERT by 4.7\% in the zero-shot setting and achieves comparable result to XLM for translate-train while using less than 18\% of the same parallel data and 31\% fewer model parameters. On MLQA, our model outperforms XLM-R\_Base, which has 57\% more parameters than ours.},
	urldate = {2022-08-17},
	booktitle = {Proceedings of the 2021 {Conference} of the {North} {American} {Chapter} of the {Association} for {Computational} {Linguistics}: {Human} {Language} {Technologies}},
	publisher = {Association for Computational Linguistics},
	author = {Pan, Lin and Hang, Chung-Wei and Qi, Haode and Shah, Abhishek and Potdar, Saloni and Yu, Mo},
	month = jun,
	year = {2021},
	pages = {210--219},
}

@misc{reimers_making_2020,
	title = {Making {Monolingual} {Sentence} {Embeddings} {Multilingual} using {Knowledge} {Distillation}},
	url = {http://arxiv.org/abs/2004.09813},
	abstract = {We present an easy and efficient method to extend existing sentence embedding models to new languages. This allows to create multilingual versions from previously monolingual models. The training is based on the idea that a translated sentence should be mapped to the same location in the vector space as the original sentence. We use the original (monolingual) model to generate sentence embeddings for the source language and then train a new system on translated sentences to mimic the original model. Compared to other methods for training multilingual sentence embeddings, this approach has several advantages: It is easy to extend existing models with relatively few samples to new languages, it is easier to ensure desired properties for the vector space, and the hardware requirements for training is lower. We demonstrate the effectiveness of our approach for 50+ languages from various language families. Code to extend sentence embeddings models to more than 400 languages is publicly available.},
	urldate = {2022-08-17},
	publisher = {arXiv},
	author = {Reimers, Nils and Gurevych, Iryna},
	month = oct,
	year = {2020},
	note = {arXiv:2004.09813 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@article{artetxe_massively_2019-2,
	title = {Massively {Multilingual} {Sentence} {Embeddings} for {Zero}-{Shot} {Cross}-{Lingual} {Transfer} and {Beyond}},
	volume = {7},
	issn = {2307-387X},
	url = {http://arxiv.org/abs/1812.10464},
	doi = {10.1162/tacl_a_00288},
	abstract = {We introduce an architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different families and written in 28 different scripts. Our system uses a single BiLSTM encoder with a shared BPE vocabulary for all languages, which is coupled with an auxiliary decoder and trained on publicly available parallel corpora. This enables us to learn a classifier on top of the resulting embeddings using English annotated data only, and transfer it to any of the 93 languages without any modification. Our experiments in cross-lingual natural language inference (XNLI dataset), cross-lingual document classification (MLDoc dataset) and parallel corpus mining (BUCC dataset) show the effectiveness of our approach. We also introduce a new test set of aligned sentences in 112 languages, and show that our sentence embeddings obtain strong results in multilingual similarity search even for low-resource languages. Our implementation, the pre-trained encoder and the multilingual test set are available at https://github.com/facebookresearch/LASER},
	urldate = {2022-08-16},
	journal = {Transactions of the Association for Computational Linguistics},
	author = {Artetxe, Mikel and Schwenk, Holger},
	month = nov,
	year = {2019},
	note = {arXiv:1812.10464 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning},
	pages = {597--610},
}

@article{palagini2022sleep,
  title={Sleep, insomnia and mental health},
  author={Palagini, Laura and Hertenstein, Elisabeth and Riemann, Dieter and Nissen, Christoph},
  journal={Journal of sleep research},
  volume={31},
  number={4},
  pages={e13628},
  year={2022},
  publisher={Wiley Online Library}
}

@article{blank2015health,
  title={Health correlates of insomnia symptoms and comorbid mental disorders in a nationally representative sample of US adolescents},
  author={Blank, Madeleine and Zhang, Jihui and Lamers, Femke and Taylor, Adrienne D and Hickie, Ian B and Merikangas, Kathleen R},
  journal={Sleep},
  volume={38},
  number={2},
  pages={197--204},
  year={2015},
  publisher={Oxford University Press}
}


@misc{feng_language-agnostic_2022,
	title = {Language-agnostic {BERT} {Sentence} {Embedding}},
	url = {http://arxiv.org/abs/2007.01852},
	abstract = {While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning (Reimers and Gurevych, 2019), BERT based cross-lingual sentence embeddings have yet to be explored. We systematically investigate methods for learning multilingual sentence embeddings by combining the best methods for learning monolingual and cross-lingual representations including: masked language modeling (MLM), translation language modeling (TLM) (Conneau and Lample, 2019), dual encoder translation ranking (Guo et al., 2018), and additive margin softmax (Yang et al., 2019a). We show that introducing a pre-trained multilingual language model dramatically reduces the amount of parallel training data required to achieve good performance by 80\%. Composing the best of these methods produces a model that achieves 83.7\% bi-text retrieval accuracy over 112 languages on Tatoeba, well above the 65.5\% achieved by Artetxe and Schwenk (2019b), while still performing competitively on monolingual transfer learning benchmarks (Conneau and Kiela, 2018). Parallel data mined from CommonCrawl using our best model is shown to train competitive NMT models for en-zh and en-de. We publicly release our best multilingual sentence embedding model for 109+ languages at https://tfhub.dev/google/LaBSE.},
	urldate = {2022-08-16},
	publisher = {arXiv},
	author = {Feng, Fangxiaoyu and Yang, Yinfei and Cer, Daniel and Arivazhagan, Naveen and Wang, Wei},
	month = mar,
	year = {2022},
	note = {arXiv:2007.01852 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@inproceedings{yang_multilingual_2020,
	address = {Online},
	title = {Multilingual {Universal} {Sentence} {Encoder} for {Semantic} {Retrieval}},
	url = {https://aclanthology.org/2020.acl-demos.12},
	doi = {10.18653/v1/2020.acl-demos.12},
	abstract = {We present easy-to-use retrieval focused multilingual sentence embedding models, made available on TensorFlow Hub. The models embed text from 16 languages into a shared semantic space using a multi-task trained dual-encoder that learns tied cross-lingual representations via translation bridge tasks (Chidambaram et al., 2018). The models achieve a new state-of-the-art in performance on monolingual and cross-lingual semantic retrieval (SR). Competitive performance is obtained on the related tasks of translation pair bitext retrieval (BR) and retrieval question answering (ReQA). On transfer learning tasks, our multilingual embeddings approach, and in some cases exceed, the performance of English only sentence embeddings.},
	urldate = {2022-08-16},
	booktitle = {Proceedings of the 58th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics}: {System} {Demonstrations}},
	publisher = {Association for Computational Linguistics},
	author = {Yang, Yinfei and Cer, Daniel and Ahmad, Amin and Guo, Mandy and Law, Jax and Constant, Noah and Hernandez Abrego, Gustavo and Yuan, Steve and Tar, Chris and Sung, Yun-hsuan and Strope, Brian and Kurzweil, Ray},
	month = jul,
	year = {2020},
	pages = {87--94},
}

@misc{kazemi_claim_2021,
	title = {Claim {Matching} {Beyond} {English} to {Scale} {Global} {Fact}-{Checking}},
	url = {http://arxiv.org/abs/2106.00853},
	abstract = {Manual fact-checking does not scale well to serve the needs of the internet. This issue is further compounded in non-English contexts. In this paper, we discuss claim matching as a possible solution to scale fact-checking. We define claim matching as the task of identifying pairs of textual messages containing claims that can be served with one fact-check. We construct a novel dataset of WhatsApp tipline and public group messages alongside fact-checked claims that are first annotated for containing "claim-like statements" and then matched with potentially similar items and annotated for claim matching. Our dataset contains content in high-resource (English, Hindi) and lower-resource (Bengali, Malayalam, Tamil) languages. We train our own embedding model using knowledge distillation and a high-quality "teacher" model in order to address the imbalance in embedding quality between the low- and high-resource languages in our dataset. We provide evaluations on the performance of our solution and compare with baselines and existing state-of-the-art multilingual embedding models, namely LASER and LaBSE. We demonstrate that our performance exceeds LASER and LaBSE in all settings. We release our annotated datasets, codebooks, and trained embedding model to allow for further research.},
	urldate = {2022-08-15},
	publisher = {arXiv},
	author = {Kazemi, Ashkan and Garimella, Kiran and Gaffney, Devin and Hale, Scott A.},
	month = jun,
	year = {2021},
	note = {arXiv:2106.00853 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@article{bhowmik_leveraging_2021,
	title = {Leveraging {Vector} {Space} {Similarity} for {Learning} {Cross}-{Lingual} {Word} {Embeddings}: {A} {Systematic} {Review}},
	volume = {1},
	issn = {2673-6470},
	shorttitle = {Leveraging {Vector} {Space} {Similarity} for {Learning} {Cross}-{Lingual} {Word} {Embeddings}},
	url = {https://www.mdpi.com/2673-6470/1/3/11},
	doi = {10.3390/digital1030011},
	abstract = {This article presents a systematic literature review on quantifying the proximity between independently trained monolingual word embedding spaces. A search was carried out in the broader context of inducing bilingual lexicons from cross-lingual word embeddings, especially for lowresource languages. The returned articles were then classiﬁed. Cross-lingual word embeddings have drawn the attention of researchers in the ﬁeld of natural language processing (NLP). Although existing methods have yielded satisfactory results for resource-rich languages and languages related to them, some researchers have pointed out that the same is not true for low-resource and distant languages. In this paper, we report the research on methods proposed to provide better representation for low-resource and distant languages in the cross-lingual word embedding space.},
	language = {en},
	number = {3},
	urldate = {2022-08-15},
	journal = {Digital},
	author = {Bhowmik, Kowshik and Ralescu, Anca},
	month = jul,
	year = {2021},
	pages = {145--161},
}

@article{jawanpuria_learning_2019,
	title = {Learning {Multilingual} {Word} {Embeddings} in {Latent} {Metric} {Space}: {A} {Geometric} {Approach}},
	volume = {7},
	shorttitle = {Learning {Multilingual} {Word} {Embeddings} in {Latent} {Metric} {Space}},
	url = {https://aclanthology.org/Q19-1007},
	doi = {10.1162/tacl_a_00257},
	abstract = {We propose a novel geometric approach for learning bilingual mappings given monolingual embeddings and a bilingual dictionary. Our approach decouples the source-to-target language transformation into (a) language-specific rotations on the original embeddings to align them in a common, latent space, and (b) a language-independent similarity metric in this common space to better model the similarity between the embeddings. Overall, we pose the bilingual mapping problem as a classification problem on smooth Riemannian manifolds. Empirically, our approach outperforms previous approaches on the bilingual lexicon induction and cross-lingual word similarity tasks. We next generalize our framework to represent multiple languages in a common latent space. Language-specific rotations for all the languages and a common similarity metric in the latent space are learned jointly from bilingual dictionaries for multiple language pairs. We illustrate the effectiveness of joint learning for multiple languages in an indirect word translation setting.},
	urldate = {2022-08-15},
	journal = {Transactions of the Association for Computational Linguistics},
	author = {Jawanpuria, Pratik and Balgovind, Arjun and Kunchukuttan, Anoop and Mishra, Bamdev},
	year = {2019},
	note = {Place: Cambridge, MA
Publisher: MIT Press},
	pages = {107--120},
}

@misc{noauthor_cross-lingual_nodate,
	title = {Cross-{Lingual} {Similarity} and {Semantic} {Search} {Engine} with {Multilingual} {Universal} {Sentence} {Encoder} {\textbar} {TensorFlow} {Hub}},
	url = {https://www.tensorflow.org/hub/tutorials/cross_lingual_similarity_with_tf_hub_multilingual_universal_encoder},
	language = {en},
	urldate = {2022-08-15},
	journal = {TensorFlow},
}

@article{stajner_cross-lingual_2019,
	title = {Cross-lingual document similarity estimation and dictionary generation with comparable corpora},
	volume = {58},
	issn = {0219-1377, 0219-3116},
	url = {http://link.springer.com/10.1007/s10115-018-1179-9},
	doi = {10.1007/s10115-018-1179-9},
	abstract = {This paper proposes an approach for performing bilingual dictionary generation even when trained on widely available comparable bilingual corpora. We also show its capability to provide cross-lingual similarity estimates that correlate well with human judgments. We implement an approach using a nonlinear bilingual translation model that we train using comparable corpora. We propose a method using word embeddings and kernel approximation to train scalable non-linear transformations. We demonstrate that this novel method works better on a majority of evaluated language pairs.},
	language = {en},
	number = {3},
	urldate = {2022-08-15},
	journal = {Knowledge and Information Systems},
	author = {Štajner, Tadej and Mladenić, Dunja},
	month = mar,
	year = {2019},
	pages = {729--743},
}

@inproceedings{jungnickel_evaluation_2019,
	title = {Evaluation and {Comparison} of {Cross}-lingual {Text} {Processing} {Pipelines}},
	doi = {10.1109/SSCI44817.2019.9002921},
	abstract = {With the trend of globalization and digitalization, many transnational companies are continuously collecting and storing unstructured text data in different languages. To exploit the business value of such high-volume multilingual text data, cross-lingual information extraction utilizes machine translation and other natural language processing (NLP) techniques to analyze this data. However, results of these analysis heavily depend on the order in which the tasks are performed as well as the used machine translation and NLP approaches or trained models. In this paper, we defined and evaluated a series of cross-lingual text processing pipelines for English and Chinese language. We therefore combine multiple commercial machine translation services with different automatic keyphrase extraction and named entity recognition techniques and evaluate their performance with regards to the order of execution. Hence, we evaluate the combination of machine translation systems and natural language processing techniques with two processing sequences in our experiment. One is to translate the document before extracting keyphrase and named entities. The other is to translate the processing results. The experiment outcomes indicate that translating documents is a better choice than the other way around in both tasks. However, there exists a substantial disparity between the performance of the cross-lingual text processing pipelines and the corresponding monolingual references.},
	booktitle = {2019 {IEEE} {Symposium} {Series} on {Computational} {Intelligence} ({SSCI})},
	author = {Jungnickel, Robert and Pomp, André and Kirmse, Andreas and Li, Xiang and Samsonov, Vladimir and Meisen, Tobias},
	month = dec,
	year = {2019},
	keywords = {Data mining, Dictionaries, Natural language processing, Pipelines, Task analysis, Text processing, cross-lingual text processing, keyphrase extraction, natural language processing, processing pipelines, translation},
	pages = {417--425},
}

@article{noauthor_notitle_nodate,
}

@article{parhizi_state_2015,
	title = {State of the {Art} in {Research} on {Microgrids}: {A} {Review}},
	volume = {3},
	issn = {2169-3536},
	shorttitle = {State of the {Art} in {Research} on {Microgrids}},
	doi = {10.1109/ACCESS.2015.2443119},
	abstract = {The significant benefits associated with microgrids have led to vast efforts to expand their penetration in electric power systems. Although their deployment is rapidly growing, there are still many challenges to efficiently design, control, and operate microgrids when connected to the grid, and also when in islanded mode, where extensive research activities are underway to tackle these issues. It is necessary to have an across-the-board view of the microgrid integration in power systems. This paper presents a review of issues concerning microgrids and provides an account of research in areas related to microgrids, including distributed generation, microgrid value propositions, applications of power electronics, economic issues, microgrid operation and control, microgrid clusters, and protection and communications issues.},
	journal = {IEEE Access},
	author = {Parhizi, Sina and Lotfi, Hossein and Khodaei, Amin and Bahramirad, Shay},
	year = {2015},
	note = {Conference Name: IEEE Access},
	keywords = {Commuincation networks, Control systems, Distributed power generation, Microgrids, Power electronics, Power system economics, Rersearch and development, communications, distributed generation, economics, operation and control, protection},
	pages = {890--925},
}

@misc{noauthor_review_nodate,
	title = {A {Review} on {Microgrids}’ {Challenges} \& {Perspectives} {\textbar} {IEEE} {Journals} \& {Magazine} {\textbar} {IEEE} {Xplore}},
	url = {https://ieeexplore.ieee.org/document/9648165},
	urldate = {2022-07-22},
}

@inproceedings{suyanto_study_2017,
	title = {Study {Trends} and {Challenges} of the {Development} of {Microgrids}},
	doi = {10.1109/ICAdLT.2017.8547028},
	abstract = {With the global energy crisis and the increasingly serious environmental problems, renewable energy becomes a future energy trends. As an important form of renewable energy used, distributed generation (DG) has been rapid development in the world. However, large scale integration of DGs will bring operating and challenges to the power system network. A microgrid is an important means to this problem, and gained much attention worldwide. This paper introduces the basic concept of microgrid, and analyze the new trends and challenges of the development of microgrids, as well as some technologies. These trends and challenges include higher renewable energy integration, multi energy forms, multilevel architecture, demand side management and generalized storage.},
	booktitle = {2017 6th {IEEE} {International} {Conference} on {Advanced} {Logistics} and {Transport} ({ICALT})},
	author = {Suyanto, Heri and Irawati, Rina},
	month = jul,
	year = {2017},
	keywords = {DSM, Energy storage, Market research, Microgrids, Photovoltaic systems, Reliability, Renewable energy sources, multi energy Introduction, renewable energy, storage},
	pages = {160--164},
}

@article{noauthor_notitle_nodate-1,
}