@misc{fu2020d4rl,
    title={D4RL: Datasets for Deep Data-Driven Reinforcement Learning},
    author={Justin Fu and Aviral Kumar and Ofir Nachum and George Tucker and Sergey Levine},
    year={2020},
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    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

@InProceedings{pmlr-v15-larochelle11a,
  title = 	 {The Neural Autoregressive Distribution Estimator},
  author = 	 {Larochelle, Hugo and Murray, Iain},
  booktitle = 	 {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
  pages = 	 {29--37},
  year = 	 {2011},
  editor = 	 {Gordon, Geoffrey and Dunson, David and Dudík, Miroslav},
  volume = 	 {15},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Fort Lauderdale, FL, USA},
  month = 	 {11--13 Apr},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v15/larochelle11a/larochelle11a.pdf},
  url = 	 {https://proceedings.mlr.press/v15/larochelle11a.html},
  abstract = 	 {We describe a new approach for modeling the distribution of high-dimensional vectors of discrete variables. This model is inspired by the restricted Boltzmann machine (RBM), which has been shown to be a powerful model of such distributions. However, an RBM typically does not provide a tractable distribution estimator, since evaluating the probability it assigns to some given observation requires the computation of the so-called partition function, which itself is intractable for RBMs of even moderate size. Our model circumvents this difficulty by decomposing the joint distribution of observations into tractable conditional distributions and modeling each conditional using a non-linear function similar to a conditional of an RBM. Our model can also be interpreted as an autoencoder wired such that its output can be used to assign valid probabilities to observations. We show that this new model outperforms other multivariate binary distribution estimators on several datasets and performs similarly to a large (but intractable) RBM.  }
}

@inproceedings{Salimans2017PixeCNN,
  title={PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications},
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  booktitle={ICLR},
  year={2017}
}

@article{radford2018improving,
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  year={2018}
}

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  organization={PMLR}
}

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  year={2016}
}

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  title = {Offline Reinforcement Learning as One Big Sequence Modeling Problem},
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  year = {2021},
}

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  journal={Advances in neural information processing systems},
  volume={34},
  pages={15084--15097},
  year={2021}
}

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@misc{ruder2016wordembeddingspart2,
author = {Ruder, Sebastian},
title = {{On word embeddings - Part 2: Approximating the Softmax}},
year = {2016},
howpublished = {\url{http://ruder.io/word-embeddings-softmax}},
}

@INPROCEEDINGS{407206,
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@inproceedings{
chen2020wavegrad,
title={WaveGrad: Estimating Gradients for Waveform Generation},
author={Nanxin Chen and Yu Zhang and Heiga Zen and Ron J Weiss and Mohammad Norouzi and William Chan},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=NsMLjcFaO8O}
}

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@article{ho2020denoising,
  title={Denoising diffusion probabilistic models},
  author={Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
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  volume={33},
  pages={6840--6851},
  year={2020}
}

@article{DBLP:journals/corr/abs-1907-05600,
  author    = {Yang Song and
               Stefano Ermon},
  title     = {Generative Modeling by Estimating Gradients of the Data Distribution},
  journal   = {CoRR},
  volume    = {abs/1907.05600},
  year      = {2019},
  url       = {http://arxiv.org/abs/1907.05600},
  eprinttype = {arXiv},
  eprint    = {1907.05600},
  timestamp = {Thu, 01 Oct 2020 17:52:46 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1907-05600.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@InProceedings{ho2019flow,
  title = 	 {Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design},
  author =       {Ho, Jonathan and Chen, Xi and Srinivas, Aravind and Duan, Yan and Abbeel, Pieter},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {2722--2730},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume = 	 {97},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {09--15 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v97/ho19a/ho19a.pdf},
  url = 	 {https://proceedings.mlr.press/v97/ho19a.html},
  abstract = 	 {Flow-based generative models are powerful exact likelihood models with efficient sampling and inference. Despite their computational efficiency, flow-based models generally have much worse density modeling performance compared to state-of-the-art autoregressive models. In this paper, we investigate and improve upon three limiting design choices employed by flow-based models in prior work: the use of uniform noise for dequantization, the use of inexpressive affine flows, and the use of purely convolutional conditioning networks in coupling layers. Based on our findings, we propose Flow++, a new flow-based model that is now the state-of-the-art non-autoregressive model for unconditional density estimation on standard image benchmarks. Our work has begun to close the significant performance gap that has so far existed between autoregressive models and flow-based models.}
}


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  year={2015}
}

@inproceedings{germain2015made,
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  pages={881--889},
  year={2015},
  organization={PMLR}
}

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  author={Papamakarios, George and Pavlakou, Theo and Murray, Iain},
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  volume={30},
  year={2017}
}

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  year={2018},
  organization={PMLR}
}
@inproceedings{oliva2018transformation,
  title={Transformation autoregressive networks},
  author={Oliva, Junier and Dubey, Avinava and Zaheer, Manzil and Poczos, Barnabas and Salakhutdinov, Ruslan and Xing, Eric and Schneider, Jeff},
  booktitle={International Conference on Machine Learning},
  pages={3898--3907},
  year={2018},
  organization={PMLR}
}

@inproceedings{
grathwohl2018ffjord,
title={Scalable Reversible Generative Models with Free-form Continuous Dynamics},
author={Will Grathwohl and Ricky T. Q. Chen and Jesse Bettencourt and David Duvenaud},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=rJxgknCcK7},
}

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  author={De Cao, Nicola and Aziz, Wilker and Titov, Ivan},
  booktitle={Uncertainty in artificial intelligence},
  pages={1263--1273},
  year={2020},
  organization={PMLR}
}

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  title={Marginalizable Density Models},
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  year={2021}
}

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  year={2020}
}

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  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={3858--3871},
  year={2020}
}

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@inproceedings{ghasemipour2021emaq,
  title={Emaq: Expected-max q-learning operator for simple yet effective offline and online rl},
  author={Ghasemipour, Seyed Kamyar Seyed and Schuurmans, Dale and Gu, Shixiang Shane},
  booktitle={International Conference on Machine Learning},
  pages={3682--3691},
  year={2021},
  organization={PMLR}
}

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  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={7537--7547},
  year={2020}
}

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@article{durkan2019neural,
  title={Neural spline flows},
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  volume={32},
  year={2019}
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@article{uria2016neural,
  title={Neural autoregressive distribution estimation},
  author={Uria, Benigno and C{\^o}t{\'e}, Marc-Alexandre and Gregor, Karol and Murray, Iain and Larochelle, Hugo},
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@article{brown2020language,
  title={Language models are few-shot learners},
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  year={2020}
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@article{kingma2021variational,
  title={Variational diffusion models},
  author={Kingma, Diederik and Salimans, Tim and Poole, Ben and Ho, Jonathan},
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  pages={21696--21707},
  year={2021}
}

@inproceedings{jain2020locally,
  title={Locally masked convolution for autoregressive models},
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  pages={1358--1367},
  year={2020},
  organization={PMLR}
}

@inproceedings{uria2014deep,
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@article{DBLP:journals/corr/DinhSB16,
  author    = {Laurent Dinh and
               Jascha Sohl{-}Dickstein and
               Samy Bengio},
  title     = {Density estimation using Real {NVP}},
  journal   = {CoRR},
  volume    = {abs/1605.08803},
  year      = {2016},
  url       = {http://arxiv.org/abs/1605.08803},
  eprinttype = {arXiv},
  eprint    = {1605.08803},
  timestamp = {Mon, 13 Aug 2018 16:47:21 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/DinhSB16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{rezende2015variational,
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  pages={1530--1538},
  year={2015},
  organization={PMLR}
}

@misc{wang2017tacotron,
      title={Tacotron: Towards End-to-End Speech Synthesis}, 
      author={Yuxuan Wang and RJ Skerry-Ryan and Daisy Stanton and Yonghui Wu and Ron J. Weiss and Navdeep Jaitly and Zongheng Yang and Ying Xiao and Zhifeng Chen and Samy Bengio and Quoc Le and Yannis Agiomyrgiannakis and Rob Clark and Rif A. Saurous},
      year={2017},
      eprint={1703.10135},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{
kong2021diffwave,
title={DiffWave: A Versatile Diffusion Model for Audio Synthesis},
author={Zhifeng Kong and Wei Ping and Jiaji Huang and Kexin Zhao and Bryan Catanzaro},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=a-xFK8Ymz5J}
}

@article{kumar2019melgan,
  title={Melgan: Generative adversarial networks for conditional waveform synthesis},
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  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@inproceedings{oord2017parallel,
  title={Parallel wavenet: Fast high-fidelity speech synthesis},
  author={Oord, Aaron and Li, Yazhe and Babuschkin, Igor and Simonyan, Karen and Vinyals, Oriol and Kavukcuoglu, Koray and Driessche, George and Lockhart, Edward and Cobo, Luis and Stimberg, Florian and others},
  booktitle={International conference on machine learning},
  pages={3918--3926},
  year={2018},
  organization={PMLR}
}

@misc{paine2016fast,
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      year={2016},
      eprint={1611.09482},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}

@misc{ljspeech17,
  author       = {Keith Ito and Linda Johnson},
  title        = {The LJ Speech Dataset},
  howpublished = {\url{https://keithito.com/LJ-Speech-Dataset/}},
  year         = 2017
}

@misc{Dua:2019 ,
author = "Dua, Dheeru and Graff, Casey",
year = "2017",
title = "{UCI} Machine Learning Repository",
url = "http://archive.ics.uci.edu/ml",
institution = "University of California, Irvine, School of Information and Computer Sciences" }

@article{lecun-mnisthandwrittendigit-2010,
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  timestamp = {2016-07-12T19:25:30.000+0200},
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  year = 2010
}

@article{DBLP:journals/corr/VaswaniSPUJGKP17,
  author    = {Ashish Vaswani and
               Noam Shazeer and
               Niki Parmar and
               Jakob Uszkoreit and
               Llion Jones and
               Aidan N. Gomez and
               Lukasz Kaiser and
               Illia Polosukhin},
  title     = {Attention Is All You Need},
  journal   = {CoRR},
  volume    = {abs/1706.03762},
  year      = {2017},
  url       = {http://arxiv.org/abs/1706.03762},
  eprinttype = {arXiv},
  eprint    = {1706.03762},
  timestamp = {Sat, 23 Jan 2021 01:20:40 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/VaswaniSPUJGKP17.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{bhat2021adabins,
  title={Adabins: Depth estimation using adaptive bins},
  author={Bhat, Shariq Farooq and Alhashim, Ibraheem and Wonka, Peter},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4009--4018},
  year={2021}
}
@inproceedings{
hoogeboom2021learning,
title={Learning Discrete Distributions by Dequantization},
author={Emiel Hoogeboom and Taco Cohen and Jakub Mikolaj Tomczak},
booktitle={Third Symposium on Advances in Approximate Bayesian Inference},
year={2021},
url={https://openreview.net/forum?id=a0EpGhKt_R}
}

@misc{yamamoto2019wavenetcode,
  title={WaveNet Vocoder},
  author={Yamamoto, Ryuichi},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished={\url{https://github.com/r9y9/wavenet_vocoder}},
  year={2019}
}
