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}" }