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. 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. link

  • Dinh, Laurent, Jascha Sohl-Dickstein, and Samy Bengio. 2016. “Density Estimation Using Real NVP.” arXiv [cs.LG]. arXiv. link

  • Kingma, Diederik P., and Prafulla Dhariwal. 2018. “Glow: Generative Flow with Invertible 1x1 Convolutions.” arXiv [stat.ML]. arXiv. 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. link

  • Zhang, Linfeng, E. Weinan, and Lei Wang. 2018. “Monge-Ampère Flow for Generative Modeling.” arXiv [cs.LG]. arXiv. 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

Learning Discrete Latent Structure