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 author = {Lamport, Leslie},
 title = {Latex: A Document Preparation System},
 year = {1986},
 isbn = {0-201-15790-X},
 publisher = {Addison-Wesley Longman Publishing Co., Inc.},
 address = {Boston, MA, USA},
}

@misc{Hinton:arXiv:2015:Distilling,
 title={Distilling the knowledge in a neural network},
 author={Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff},
 OPTeprinttype={arXiv},
 OPTeprint={1503.02531},
 howpublished={Eprint \href{http://arxiv.org/abs/1503.02531}{arXiv:1503.02531}},
 OPTurl={http://arxiv.org/abs/1503.02531},
 year=2015
}

@misc{muennighoff2023mtebmassivetextembedding,
      title={MTEB: Massive Text Embedding Benchmark}, 
      author={Niklas Muennighoff and Nouamane Tazi and Loïc Magne and Nils Reimers},
      year={2023},
      eprint={2210.07316},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2210.07316}, 
}
@article{doi:10.1056/NEJMoa1102873,
author = {NLST},
title = {Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening},
journal = {New England Journal of Medicine},
volume = {365},
number = {5},
pages = {395-409},
year = {2011},
doi = {10.1056/NEJMoa1102873},
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eprint = {https://www.nejm.org/doi/pdf/10.1056/NEJMoa1102873},
}
@misc{zhang2024mgtegeneralizedlongcontexttext,
      title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval}, 
      author={Xin Zhang and Yanzhao Zhang and Dingkun Long and Wen Xie and Ziqi Dai and Jialong Tang and Huan Lin and Baosong Yang and Pengjun Xie and Fei Huang and Meishan Zhang and Wenjie Li and Min Zhang},
      year={2024},
      eprint={2407.19669},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.19669}, 
}
@misc{micikevicius2018mixedprecisiontraining,
      title={Mixed Precision Training}, 
      author={Paulius Micikevicius and Sharan Narang and Jonah Alben and Gregory Diamos and Erich Elsen and David Garcia and Boris Ginsburg and Michael Houston and Oleksii Kuchaiev and Ganesh Venkatesh and Hao Wu},
      year={2018},
      eprint={1710.03740},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/1710.03740}, 
}
@misc{loshchilov2017sgdrstochasticgradientdescent,
      title={SGDR: Stochastic Gradient Descent with Warm Restarts}, 
      author={Ilya Loshchilov and Frank Hutter},
      year={2017},
      eprint={1608.03983},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1608.03983}, 
}
@misc{kingma2017adammethodstochasticoptimization,
      title={Adam: A Method for Stochastic Optimization}, 
      author={Diederik P. Kingma and Jimmy Ba},
      year={2017},
      eprint={1412.6980},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1412.6980}, 
}
@misc{zhang2024doescriticalbatchsize,
      title={How Does Critical Batch Size Scale in Pre-training?}, 
      author={Hanlin Zhang and Depen Morwani and Nikhil Vyas and Jingfeng Wu and Difan Zou and Udaya Ghai and Dean Foster and Sham Kakade},
      year={2024},
      eprint={2410.21676},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2410.21676}, 
}
@misc{radford2021learningtransferablevisualmodels,
      title={Learning Transferable Visual Models From Natural Language Supervision}, 
      author={Alec Radford and Jong Wook Kim and Chris Hallacy and Aditya Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
      year={2021},
      eprint={2103.00020},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2103.00020}, 
}
@misc{jia2021scalingvisualvisionlanguagerepresentation,
      title={Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision}, 
      author={Chao Jia and Yinfei Yang and Ye Xia and Yi-Ting Chen and Zarana Parekh and Hieu Pham and Quoc V. Le and Yunhsuan Sung and Zhen Li and Tom Duerig},
      year={2021},
      eprint={2102.05918},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2102.05918}, 
}
@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding}, 
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748}, 
}
@misc{zhang2022contrastivelearningmedicalvisual,
      title={Contrastive Learning of Medical Visual Representations from Paired Images and Text}, 
      author={Yuhao Zhang and Hang Jiang and Yasuhide Miura and Christopher D. Manning and Curtis P. Langlotz},
      year={2022},
      eprint={2010.00747},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2010.00747}, 
}
@misc{lee2022uniclipunifiedframeworkcontrastive,
      title={UniCLIP: Unified Framework for Contrastive Language-Image Pre-training}, 
      author={Janghyeon Lee and Jongsuk Kim and Hyounguk Shon and Bumsoo Kim and Seung Hwan Kim and Honglak Lee and Junmo Kim},
      year={2022},
      eprint={2209.13430},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2209.13430}, 
}
@InProceedings{10.1007/978-3-031-43993-3_51,
author="Lin, Weixiong
and Zhao, Ziheng
and Zhang, Xiaoman
and Wu, Chaoyi
and Zhang, Ya
and Wang, Yanfeng
and Xie, Weidi",
editor="Greenspan, Hayit
and Madabhushi, Anant
and Mousavi, Parvin
and Salcudean, Septimiu
and Duncan, James
and Syeda-Mahmood, Tanveer
and Taylor, Russell",
title="PMC-CLIP: Contrastive Language-Image Pre-training Using Biomedical Documents",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023",
year="2023",}
@misc{li2024winnersperformancelungcancer,
      title={No winners: Performance of lung cancer prediction models depends on screening-detected, incidental, and biopsied pulmonary nodule use cases}, 
      author={Thomas Z. Li and Kaiwen Xu and Aravind Krishnan and Riqiang Gao and Michael N. Kammer and Sanja Antic and David Xiao and Michael Knight and Yency Martinez and Rafael Paez and Robert J. Lentz and Stephen Deppen and Eric L. Grogan and Thomas A. Lasko and Kim L. Sandler and Fabien Maldonado and Bennett A. Landman},
      year={2024},
      eprint={2405.10993},
      archivePrefix={arXiv},
      primaryClass={q-bio.QM},
      url={https://arxiv.org/abs/2405.10993}, 
}
@misc{lasko2024unsuperviseddiscoveryclinicaldisease,
      title={Unsupervised Discovery of Clinical Disease Signatures Using Probabilistic Independence}, 
      author={Thomas A. Lasko and John M. Still and Thomas Z. Li and Marco Barbero Mota and William W. Stead and Eric V. Strobl and Bennett A. Landman and Fabien Maldonado},
      year={2024},
      eprint={2402.05802},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2402.05802}, 
}
﻿@Article{Lasko2024,
author={Lasko, Thomas A.
and Strobl, Eric V.
and Stead, William W.},
title={Why do probabilistic clinical models fail to transport between sites},
journal={npj Digital Medicine},
year={2024},
month={Mar},
day={01},
volume={7},
number={1},
pages={53},
issn={2398-6352},
doi={10.1038/s41746-024-01037-4},
url={https://doi.org/10.1038/s41746-024-01037-4}
}
@inproceedings{10.1117/12.2653911,
author = {Thomas Z. Li and Kaiwen Xu and Riqiang Gao and Yucheng Tang and Thomas A. Lasko and Fabien Maldonado and Kim L. Sandler and Bennett A. Landman},
title = {{Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography}},
volume = {12464},
booktitle = {Medical Imaging 2023: Image Processing},
editor = {Olivier Colliot and Ivana Išgum},
organization = {International Society for Optics and Photonics},
publisher = {SPIE},
pages = {1246412},
keywords = {lung cancer, longitudinal vision transformer, time distance vision transformer, temporal emphasis model, pulmonary nodules, longitudinal CT},
year = {2023},
doi = {10.1117/12.2653911},
URL = {https://doi.org/10.1117/12.2653911}
}
@InProceedings{10.1007/978-3-031-43895-0_61,
author="Li, Thomas Z.
and Still, John M.
and Xu, Kaiwen
and Lee, Ho Hin
and Cai, Leon Y.
and Krishnan, Aravind R.
and Gao, Riqiang
and Khan, Mirza S.
and Antic, Sanja
and Kammer, Michael
and Sandler, Kim L.
and Maldonado, Fabien
and Landman, Bennett A.
and Lasko, Thomas A.",
editor="Greenspan, Hayit
and Madabhushi, Anant
and Mousavi, Parvin
and Salcudean, Septimiu
and Duncan, James
and Syeda-Mahmood, Tanveer
and Taylor, Russell",
title="Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures from Routine EHRs for Pulmonary Nodule Classification",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="649--659",
isbn="978-3-031-43895-0"
}
@article{mikhael2023sybil,
  title={Sybil: a validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography},
  author={Mikhael, Peter G and Wohlwend, Jeremy and Yala, Adam and Karstens, Ludvig and Xiang, Justin and Takigami, Angelo K and Bourgouin, Patrick P and Chan, PuiYee and Mrah, Sofiane and Amayri, Wael and Juan, Yu-Hsiang and Yang, Cheng-Ta and Wan, Yung-Liang and Lin, Gigin and Sequist, Lecia V and Fintelmann, Florian J. and Barzilay, Regina},
  journal={Journal of Clinical Oncology},
  pages={JCO--22},
  year={2023},
  publisher={Wolters Kluwer Health}
}
@article{JMLR:v9:vandermaaten08a,
  author  = {Laurens van der Maaten and Geoffrey Hinton},
  title   = {Visualizing Data using t-SNE},
  journal = {Journal of Machine Learning Research},
  year    = {2008},
  volume  = {9},
  number  = {86},
  pages   = {2579--2605},
  url     = {http://jmlr.org/papers/v9/vandermaaten08a.html}
}
@misc{li2023multimodalfoundationmodelsspecialists,
      title={Multimodal Foundation Models: From Specialists to General-Purpose Assistants}, 
      author={Chunyuan Li and Zhe Gan and Zhengyuan Yang and Jianwei Yang and Linjie Li and Lijuan Wang and Jianfeng Gao},
      year={2023},
      eprint={2309.10020},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2309.10020}, 
}
@misc{wang2020makestrainingmultimodalclassification,
      title={What Makes Training Multi-Modal Classification Networks Hard?}, 
      author={Weiyao Wang and Du Tran and Matt Feiszli},
      year={2020},
      eprint={1905.12681},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1905.12681}, 
}
@misc{udandarao2024zeroshotexponentialdatapretraining,
      title={No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance}, 
      author={Vishaal Udandarao and Ameya Prabhu and Adhiraj Ghosh and Yash Sharma and Philip H. S. Torr and Adel Bibi and Samuel Albanie and Matthias Bethge},
      year={2024},
      eprint={2404.04125},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2404.04125}, 
}
@misc{chen2024mammoclipleveragingcontrastivelanguageimage,
      title={Mammo-CLIP: Leveraging Contrastive Language-Image Pre-training (CLIP) for Enhanced Breast Cancer Diagnosis with Multi-view Mammography}, 
      author={Xuxin Chen and Yuheng Li and Mingzhe Hu and Ella Salari and Xiaoqian Chen and Richard L. J. Qiu and Bin Zheng and Xiaofeng Yang},
      year={2024},
      eprint={2404.15946},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2404.15946}, 
}

@@INPROCEEDINGS{10376801,

  author={Liu, Jie and Zhang, Yixiao and Chen, Jie-Neng and Xiao, Junfei and Lu, Yongyi and Landman, Bennett A. and Yuan, Yixuan and Yuille, Alan and Tang, Yucheng and Zhou, Zongwei},

  booktitle={2023 IEEE/CVF International Conference on Computer Vision (ICCV)}, 

  title={CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection}, 

  year={2023},

  volume={},

  number={},

  pages={21095-21107},

  keywords={Training;Computer vision;Computational modeling;Computed tomography;Transfer learning;Semantics;Encoding},

  doi={10.1109/ICCV51070.2023.01934}}


@article{Jang_2024,
   title={Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders},
   volume={14},
   ISSN={2045-2322},
   url={http://dx.doi.org/10.1038/s41598-024-73695-z},
   DOI={10.1038/s41598-024-73695-z},
   number={1},
   journal={Scientific Reports},
   publisher={Springer Science and Business Media LLC},
   author={Jang, Jongseong and Kyung, Daeun and Kim, Seung Hwan and Lee, Honglak and Bae, Kyunghoon and Choi, Edward},
   year={2024},}

@article{Lu2024,
  author = {Lu, Ming Y. and Chen, Bowen and Williamson, Drew F. K. and Chen, Richard J. and Liang, Ivy and Ding, Tong and Jaume, Guillaume and Odintsov, Igor and Le, Long Phi and Gerber, Georg and Parwani, Anil V. and Zhang, Andrew and Mahmood, Faisal},
  title = {A visual-language foundation model for computational pathology},
  journal = {Nature Medicine},
  volume = {30},
  number = {3},
  pages = {863--874},
  year = {2024},
  doi = {10.1038/s41591-024-02856-4},
  url = {https://doi.org/10.1038/s41591-024-02856-4},
  issn = {1546-170X},
}


@INPROCEEDINGS{9710099,

  author={Huang, Shih-Cheng and Shen, Liyue and Lungren, Matthew P. and Yeung, Serena},

  booktitle={2021 IEEE/CVF International Conference on Computer Vision (ICCV)}, 

  title={GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-efficient Medical Image Recognition}, 

  year={2021},

  volume={},

  number={},

  pages={3922-3931},

  keywords={Representation learning;Deep learning;Training;Image segmentation;Image recognition;Image analysis;Neural networks;Medical;biological;and cell microscopy;Representation learning;Transfer/Low-shot/Semi/Unsupervised Learning;Vision + language},

  doi={10.1109/ICCV48922.2021.00391}}

@misc{conneau2020unsupervisedcrosslingualrepresentationlearning,
      title={Unsupervised Cross-lingual Representation Learning at Scale}, 
      author={Alexis Conneau and Kartikay Khandelwal and Naman Goyal and Vishrav Chaudhary and Guillaume Wenzek and Francisco Guzmán and Edouard Grave and Myle Ott and Luke Zettlemoyer and Veselin Stoyanov},
      year={2020},
      eprint={1911.02116},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/1911.02116}, 
}
@misc{kudo2018sentencepiecesimplelanguageindependent,
      title={SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing}, 
      author={Taku Kudo and John Richardson},
      year={2018},
      eprint={1808.06226},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/1808.06226}, 
}
@article{Acosta2022,
  author = {Julián N. Acosta and Guido J. Falcone and Pranav Rajpurkar and Eric J. Topol},
  title = {Multimodal biomedical AI},
  journal = {Nature Medicine},
  volume = {28},
  number = {9},
  pages = {1773--1784},
  year = {2022},
  doi = {10.1038/s41591-022-01981-2},
  url = {https://www.nature.com/articles/s41591-022-01981-2}
}
@misc{murray2018multipleimputationreviewpractical,
      title={Multiple Imputation: A Review of Practical and Theoretical Findings}, 
      author={Jared S. Murray},
      year={2018},
      eprint={1801.04058},
      archivePrefix={arXiv},
      primaryClass={stat.ME},
      url={https://arxiv.org/abs/1801.04058}, 
}
@article{Ardila2019,
  author = {Diego Ardila and Atilla P. Kiraly and Sujeeth Bharadwaj and Bokyung Choi and Joshua J. Reicher and Lily Peng and Daniel Tse and Mozziyar Etemadi and Wenxing Ye and Greg Corrado and David P. Naidich and Shravya Shetty},
  title = {End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography},
  journal = {Nature Medicine},
  year = {2019},
  volume = {25},
  number = {6},
  pages = {954--961},
  doi = {10.1038/s41591-019-0447-x},
  url = {https://doi.org/10.1038/s41591-019-0447-x},
  issn = {1546-170X}
}
@article{
doi:10.1073/pnas.1903070116,
author = {Mikhail Belkin  and Daniel Hsu  and Siyuan Ma  and Soumik Mandal },
title = {Reconciling modern machine-learning practice and the classical bias–variance trade-off},
journal = {Proceedings of the National Academy of Sciences},
volume = {116},
number = {32},
pages = {15849-15854},
year = {2019},
doi = {10.1073/pnas.1903070116},
URL = {https://www.pnas.org/doi/abs/10.1073/pnas.1903070116},
eprint = {https://www.pnas.org/doi/pdf/10.1073/pnas.1903070116},
abstract = {While breakthroughs in machine learning and artificial intelligence are changing society, our fundamental understanding has lagged behind. It is traditionally believed that fitting models to the training data exactly is to be avoided as it leads to poor performance on unseen data. However, powerful modern classifiers frequently have near-perfect fit in training, a disconnect that spurred recent intensive research and controversy on whether theory provides practical insights. In this work, we show how classical theory and modern practice can be reconciled within a single unified performance curve and propose a mechanism underlying its emergence. We believe this previously unknown pattern connecting the structure and performance of learning architectures will help shape design and understanding of learning algorithms. Breakthroughs in machine learning are rapidly changing science and society, yet our fundamental understanding of this technology has lagged far behind. Indeed, one of the central tenets of the field, the bias–variance trade-off, appears to be at odds with the observed behavior of methods used in modern machine-learning practice. The bias–variance trade-off implies that a model should balance underfitting and overfitting: Rich enough to express underlying structure in data and simple enough to avoid fitting spurious patterns. However, in modern practice, very rich models such as neural networks are trained to exactly fit (i.e., interpolate) the data. Classically, such models would be considered overfitted, and yet they often obtain high accuracy on test data. This apparent contradiction has raised questions about the mathematical foundations of machine learning and their relevance to practitioners. In this paper, we reconcile the classical understanding and the modern practice within a unified performance curve. This “double-descent” curve subsumes the textbook U-shaped bias–variance trade-off curve by showing how increasing model capacity beyond the point of interpolation results in improved performance. We provide evidence for the existence and ubiquity of double descent for a wide spectrum of models and datasets, and we posit a mechanism for its emergence. This connection between the performance and the structure of machine-learning models delineates the limits of classical analyses and has implications for both the theory and the practice of machine learning.}}

@INPROCEEDINGS {10484420,
author = { Tejero-de-Pablos, Antonio },
booktitle = { 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) },
title = {{ Complementary-Contradictory Feature Regularization against Multimodal Overfitting }},
year = {2024},
volume = {},
ISSN = {},
pages = {5667-5676},
abstract = { Understanding multimodal learning is essential to design intelligent systems that can effectively combine various data types (visual, audio, etc.). Multimodal learning is not trivial, as adding new modalities does not always result in a significant improvement in performance, i.e., multimodal overfitting. To tackle this, several works proposed regularizing each modality’s learning speed and feature distribution. However, in these methods, characterizing quantitatively and qualitatively multimodal overfitting is not intuitive. We hypothesize that, rather than regularizing abstract hyperparameters, regularizing the features learned is a more straightforward methodology against multimodal overfitting. For the given input modalities and task, we constrain "complementary" (useful) and "contradictory" (obstacle) features via a masking operation on the multimodal latent space. In addition, we leverage latent discretization so the size of the complementary-contradictory spaces becomes learnable, allowing the estimation of a modal complementarity measure. Our method successfully improves the performance of datasets with modality overfitting in different tasks, providing insight into "what"and "how much" is learned from each modality. Furthermore, it facilitates transfer learning to new datasets. Our code and a detailed manual are available at https://github.com/CyberAgentAILab/CM-VQVAE. },
keywords = {Visualization;Computer vision;Codes;Transfer learning;Semantics;Estimation;Manuals},
doi = {10.1109/WACV57701.2024.00558},
url = {https://doi.ieeecomputersociety.org/10.1109/WACV57701.2024.00558},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month =Jan}