Papers
Here is a list of suggested papers, loosely organized by topic.
Compositional Generative Models
Lake, Brenden M., Ruslan Salakhutdinov, and Joshua B. Tenenbaum. 2015. “Human-Level Concept Learning through Probabilistic Program Induction.” Science 350 (6266): 1332–38. link
George, D., W. Lehrach, K. Kansky, M. Lázaro-Gredilla, C. Laan, B. Marthi, X. Lou, et al. 2017. “A Generative Vision Model That Trains with High Data Efficiency and Breaks Text-Based CAPTCHAs.” Science, October, eaag2612. link
Lake, Brenden M., Ruslan R. Salakhutdinov, and Josh Tenenbaum. 2013. “One-Shot Learning by Inverting a Compositional Causal Process.” In Advances in Neural Information Processing Systems 26, edited by C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, 2526–34. Curran Associates, Inc. link
Jin, Ya, and Stuart Geman. 2006. “Context and Hierarchy in a Probabilistic Image Model.” In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2, 2145–52. CVPR ’06. Washington, DC, USA: IEEE Computer Society. link
Chua, Jeroen, and Pedro F. Felzenszwalb. 2016. “Scene Grammars, Factor Graphs, and Belief Propagation.” arXiv [cs.CV]. arXiv. http://arxiv.org/abs/1606.01307. link
Kortylewski, Adam, Clemens Blumer, Aleksander Wieczorek, Mario Wieser, Sonali Parbhoo, Andreas Morel-Forster, Volker Roth, and Thomas Vetter. 2017. “Greedy Structure Learning of Hierarchical Compositional Models.” arXiv [cs.CV]. arXiv. link
Deep Generative Models
Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. “Generative Adversarial Nets.” In Advances in Neural Information Processing Systems 27, edited by Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, 2672–80. Curran Associates, Inc. link
Kingma, Diederik P., and Max Welling. 2013. “Auto-Encoding Variational Bayes.” arXiv [stat.ML]. link
Dinh, Laurent, David Krueger, and Yoshua Bengio. 2014. “NICE: Non-Linear Independent Components Estimation.” arXiv [cs.LG]. arXiv. http://arxiv.org/abs/1410.8516. link
Dinh, Laurent, Jascha Sohl-Dickstein, and Samy Bengio. 2016. “Density Estimation Using Real NVP.” arXiv [cs.LG]. arXiv. http://arxiv.org/abs/1605.08803. link
Kingma, Diederik P., and Prafulla Dhariwal. 2018. “Glow: Generative Flow with Invertible 1x1 Convolutions.” arXiv [stat.ML]. arXiv. http://arxiv.org/abs/1807.03039. link
Chen, Ricky T. Q., Yulia Rubanova, Jesse Bettencourt, and David Duvenaud. 2018. “Neural Ordinary Differential Equations.” arXiv [cs.LG]. arXiv. link
Grathwohl, Will, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud. 2018. “FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models.” arXiv [cs.LG]. arXiv. http://arxiv.org/abs/1810.01367. link
Zhang, Linfeng, E. Weinan, and Lei Wang. 2018. “Monge-Ampère Flow for Generative Modeling.” arXiv [cs.LG]. arXiv. http://arxiv.org/abs/1809.10188. link
Lei, Na, Kehua Su, Li Cui, Shing-Tung Yau, and David Xianfeng Gu. 2017. “A Geometric View of Optimal Transportation and Generative Model.” arXiv [cs.LG]. arXiv. link
Genevay, Aude, Gabriel Peyré, and Marco Cuturi. 2017. “GAN and VAE from an Optimal Transport Point of View.” arXiv [stat.ML]. arXiv. link
Daskalakis, Constantinos, Andrew Ilyas, Vasilis Syrgkanis, and Haoyang Zeng. 2017. “Training GANs with Optimism.” arXiv [cs.LG]. arXiv. link
Probabilistic Programming
Wingate, David, Andreas Stuhlmueller, and Noah Goodman. 2011. “Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation.” In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, edited by Geoffrey Gordon, David Dunson, and Miroslav Dudík, 15:770–78. Proceedings of Machine Learning Research. Fort Lauderdale, FL, USA: PMLR. link
Ackerman, Nathanael L., Cameron E. Freer, and Daniel M. Roy. 2010. “On the Computability of Conditional Probability.” arXiv [math.LO]. arXiv. link
Ackerman, N. L., C. E. Freer, and D. M. Roy. 2011. “Noncomputable Conditional Distributions.” In 2011 IEEE 26th Annual Symposium on Logic in Computer Science, 107–16. link