website/static/assets/normalizing-flows.bib (138 lines of code) (raw):
@article{bingham2018pyro,
author={Bingham, Eli and Chen, Jonathan P. and Jankowiak, Martin and Obermeyer, Fritz and
Pradhan, Neeraj and Karaletsos, Theofanis and Singh, Rohit and Szerlip, Paul and
Horsfall, Paul and Goodman, Noah D.},
title={{Pyro: Deep Universal Probabilistic Programming}},
journal={Journal of Machine Learning Research},
year={2018},
note={library},
howpublished="\url{https://pyro.ai/}"
}
@article{bond2021deep,
title={Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models},
author={Bond-Taylor, Sam and Leach, Adam and Long, Yang and Willcocks, Chris G},
journal={arXiv preprint arXiv:2103.04922},
year={2021},
note={survey},
howpublished="\url{https://arxiv.org/abs/2103.04922}"
}
@inproceedings{dinh2014nice,
title={Nice: Non-linear independent components estimation},
author={Dinh, Laurent and Krueger, David and Bengio, Yoshua},
booktitle={Workshop contribution at the International Conference on Learning Representations (ICLR)},
year={2015},
note={methodology},
howpublished="\url{https://arxiv.org/abs/1410.8516}"
}
@inproceedings{dinh2016density,
title={Density estimation using real {NVP}},
author={Dinh, Laurent and Sohl-Dickstein, Jascha and Bengio, Samy},
booktitle={Conference paper at the International Conference on Learning Representations (ICLR)},
year={2017},
note={methodology},
howpublished="\url{https://arxiv.org/abs/1605.08803}"
}
@inproceedings{durkan2019neural,
title={Neural spline flows},
author={Durkan, Conor and Bekasov, Artur and Murray, Iain and Papamakarios, George},
booktitle={33rd Conference on Neural Information Processing Systems (NeurIPS)},
year={2019},
note={methodology},
howpublished="\url{https://arxiv.org/abs/1906.04032}"
}
@inproceedings{germain2015made,
title={MADE: Masked autoencoder for distribution estimation},
author={Germain, Mathieu and Gregor, Karol and Murray, Iain and Larochelle, Hugo},
booktitle={International Conference on Machine Learning (ICML)},
year={2015},
note={methodology},
howpublished="\url{https://arxiv.org/abs/1502.03509}"
}
@inproceedings{jin2019unsupervised,
title={Unsupervised learning of PCFGs with normalizing flow},
author={Jin, Lifeng and Doshi-Velez, Finale and Miller, Timothy and Schwartz, Lane and Schuler, William},
booktitle={57th Annual Meeting of the Association for Computational Linguistics},
year={2019},
note={applications},
howpublished="\url{https://www.aclweb.org/anthology/P19-1234/}"
}
@inproceedings{kim2020wavenode,
title={WaveNODE: A Continuous Normalizing Flow for Speech Synthesis},
author={Kim, Hyeongju and Lee, Hyeongseung and Kang, Woo Hyun and Cheon, Sung Jun and Choi, Byoung Jin and Kim, Nam Soo},
booktitle={2nd workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (ICML 2020)},
year={2020},
note={applications},
howpublished="\url{https://arxiv.org/abs/2006.04598}"
}
@inproceedings{kingma2016improving,
title={Improving variational inference with inverse autoregressive flow},
author={Kingma, Diederik P and Salimans, Tim and Jozefowicz, Rafal and Chen, Xi and Sutskever, Ilya and Welling, Max},
booktitle={29th Conference on Neural Information Processing Systems (NeurIPS)},
year={2016},
note={methodology},
howpublished="\url{https://arxiv.org/abs/1606.04934}"
}
@article{kobyzev2020normalizing,
title={Normalizing flows: An introduction and review of current methods},
author={Kobyzev, Ivan and Prince, Simon and Brubaker, Marcus},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2020},
note={survey},
howpublished="\url{https://arxiv.org/abs/1908.09257}"
}
@book{koller2009probabilistic,
title={Probabilistic graphical models: principles and techniques},
author={Koller, Daphne and Friedman, Nir},
year={2009},
publisher={MIT press},
note={other},
howpublished="\url{https://mitpress.mit.edu/books/probabilistic-graphical-models}"
}
@inproceedings{papamakarios2017masked,
title={Masked autoregressive flow for density estimation},
author={Papamakarios, George and Pavlakou, Theo and Murray, Iain},
booktitle={30th Conference on Neural Information Processing Systems (NeurIPS)},
year={2017},
note={methodology},
howpublished="\url{https://arxiv.org/abs/1705.07057}"
}
@article{papamakarios2019normalizing,
title={Normalizing flows for probabilistic modeling and inference},
author={Papamakarios, George and Nalisnick, Eric and Rezende, Danilo Jimenez and Mohamed, Shakir and Lakshminarayanan, Balaji},
journal={arXiv preprint arXiv:1912.02762},
year={2019},
note={survey},
howpublished="\url{https://arxiv.org/abs/1912.02762}"
}
@article{phan2019composable,
author={Phan, Du and Pradhan, Neeraj and Jankowiak, Martin},
title={Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro},
journal={arXiv preprint arXiv:1912.11554},
year={2019},
note={library},
howpublished="\url{https://num.pyro.ai/en/stable/}"
}
@inproceedings{rezende2015variational,
title={Variational inference with normalizing flows},
author={Rezende, Danilo and Mohamed, Shakir},
booktitle={International Conference on Machine Learning (ICML)},
year={2015},
note={methodology},
howpublished="\url{https://arxiv.org/abs/1505.05770}"
}
@inproceedings{yang2019pointflow,
title={Pointflow: 3d point cloud generation with continuous normalizing flows},
author={Yang, Guandao and Huang, Xun and Hao, Zekun and Liu, Ming-Yu and Belongie, Serge and Hariharan, Bharath},
booktitle={IEEE/CVF International Conference on Computer Vision},
year={2019},
note={applications},
howpublished="\url{https://arxiv.org/abs/1906.12320}"
}
@inproceedings{webb2017faithful,
title={Faithful inversion of generative models for effective amortized inference},
author={Webb, Stefan and Golinski, Adam and Zinkov, Robert and Siddharth, N and Rainforth, Tom and Teh, Yee Whye and Wood, Frank},
booktitle={31th Conference on Neural Information Processing Systems (NeurIPS)},
year={2018},
note={other},
howpublished="\url{https://arxiv.org/abs/1712.00287}"
}