References

(will be more updated soon)

Courses, Textbooks, and Other Resources

Courses

• Deep Unsupervised Learning. UC Berkeley.
• Deep Generative Models. Stanford.
• Deep Generative Models. NYU.
• Differentiable Inference and Generative Models. Toronto.
• Probabilistic Machine Learning. Duke.
• Inference and Representation. NYU.
• Probabilistic Graphical Models. Stanford.

Textbooks

• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
• Murphy, K. (2012). Machine learning: a probabilistic perspective. MIT press.
• Koller, D., & Freedman, N. (2009). Probabilistic graphical models. MIT press.
• MacKay, D. J. (2003). Information theory, inference and learning algorithms. Cambridge university press.

Other Resources

• OpenAI Blog
• Karpathy et al. (2016). OpenAI Blog Post on Deep Generative Models.
• Jang E. (2018). Normalizing Flows Tutorial, Part 1, Part 2.
• Mohamed & Rezende. Tutorial on Deep Generative Models.
• Carter et al. (2016). Experiments in Handwriting with a Neural Network.

Publications

Classical Probabilistic Models

• Roweis, S., & Ghahramani, Z. (1999). A unifying review of linear Gaussian models. Neural computation.
• Sontag. (2010). Approximate Inference in Graphical Models using LP Relaxations. PhD Thesis, MIT 2010.
• Yedidia, Freeman, Weis (2001). Understanding Belief Propagation and its Generalizations.
• Blei, Kucukelbir, McAuliffe (2017). Variational Inference: a Review for Statisticians. JASA 2017.
• Blei (2014). Data Analysis with Latent Variable Models.

Energy-Based Models

• Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural computation.
• LeCun, Y., Chopra, S., Hadsell, R., Marc’Aurelio, R., & Huang, F. J. (2006). A tutorial on energy-based learning. Predicting Structured Data.
• Salakhutdinov, R., & Hinton, G. (2009). Deep boltzmann machines. In Artificial intelligence and statistics.
• Du, Y., & Mordatch, I. (2019). Implicit Generation and Generalization with Energy-Based Models. arXiv preprint arXiv:1903.08689.
• Nash, C., & Durkan, C. (2019). Autoregressive energy machines. arXiv preprint arXiv:1904.05626.

Generative Model Evaluation

• Theis, L., van den Oord, A., & Bethge, M. (2016). A note on the evaluation of generative models. In International Conference on Learning Representations.
• Wu, Y., Burda, Y., Salakhutdinov, R., & Grosse, R. (2017). On the Quantitative Analysis of Decoder-Based Generative Models. In International Conference on Learning Representations.
• Alemi, A. A., & Fischer, I. (2018). GILBO: One Metric to Measure Them All. In Advances in Neural Information Processing Systems.
• Nalisnick, E., Matsukawa, A., Teh, Y. W., Gorur, D., & Lakshminarayanan, B. (2019). Do Deep Generative Models Know What They Don't Know?. In International Conference on Learning Representations.

Implicit Models, Generative Adversarial Networks

• Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems.
• Alain, G., Bengio, Y., Yao, L., Yosinski, J., Thibodeau-Laufer, E., Zhang, S., & Vincent, P. (2016). GSNs: generative stochastic networks. Information and Inference: A Journal of the IMA.
• Radford, A., Metz, L., & Chintala, S. (2016). Unsupervised representation learning with deep convolutional generative adversarial networks. In International Conference on Learning Representations.
• Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016). Generative Adversarial Text to Image Synthesis. In International Conference on Machine Learning.
• Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved techniques for training gans. In Advances in neural information processing systems.
• Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016). Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in neural information processing systems.
• Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein gan. arXiv preprint arXiv:1701.07875.
• Mohamed, S., & Lakshminarayanan, B. (2017). Learning in implicit generative models. In International Conference on Learning Representations.
• Dumoulin, V., Belghazi, I., Poole, B., Mastropietro, O., Lamb, A., Arjovsky, M., & Courville, A. (2017). Adversarially learned inference. In International Conference on Learning Representations.
• Isola, P., Zhu, J. Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In Computer Vision and Pattern Recognition.
• Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., & Metaxas, D. N. (2017). Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In International Conference on Computer Vision.
• Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. C. (2017). Improved training of wasserstein gans. In Advances in Neural Information Processing Systems.
• Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017). Gans trained by a two time-scale update rule converge to a local nash equilibrium. In Advances in Neural Information Processing Systems.
• Miyato, T., Kataoka, T., Koyama, M., & Yoshida, Y. (2018). Spectral normalization for generative adversarial networks. In International Conference on Learning Representations.
• Brock, A., Donahue, J., & Simonyan, K. (2019). Large scale gan training for high fidelity natural image synthesis. In International Conference on Learning Representations.

Autoregressive Models

• Bengio, Y., & Bengio, S. (2000). Modeling high-dimensional discrete data with multi-layer neural networks. In Advances in Neural Information Processing Systems.
• Larochelle, H., & Murray, I. (2011). The neural autoregressive distribution estimator. In International Conference on Artificial Intelligence and Statistics.
• Uria, B., Murray, I., & Larochelle, H. (2013). RNADE: The real-valued neural autoregressive density-estimator. In Advances in Neural Information Processing Systems.
• Graves, A. (2013). Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850.
• Uria, B., Murray, I., & Larochelle, H. (2014). A deep and tractable density estimator. In International Conference on Machine Learning.
• Germain, M., Gregor, K., Murray, I., & Larochelle, H. (2015). Made: Masked autoencoder for distribution estimation. In International Conference on Machine Learning.
• Van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., ... & Kavukcuoglu, K. (2016). Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499.
• Van den Oord, A., Kalchbrenner, N., & Kavukcuoglu, K. (2016). Pixel Recurrent Neural Networks. In International Conference on Machine Learning.
• Van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., & Graves, A. (2016). Conditional image generation with pixelcnn decoders. In Advances in Neural Information Processing Systems.
• Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners.

Variational Inference, Explicit Latent Variable Models

• Ranganath, R., Gerrish, S., & Blei, D. (2014). Black box variational inference. In Artificial Intelligence and Statistics.
• Kingma, D. P., & Welling, M. (2014). Auto-encoding variational bayes. In International Conference on Learning Representations.
• Rezende, D. J., Mohamed, S., & Wierstra, D. (2014). Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In International Conference on Machine Learning.
• Gregor, K., Danihelka, I., Mnih, A., Blundell, C., & Wierstra, D. (2014). Deep AutoRegressive Networks. In International Conference on Machine Learning.
• Mnih, A., & Gregor, K. (2014). Neural Variational Inference and Learning in Belief Networks. In International Conference on Machine Learning.
• Gregor, K., Danihelka, I., Graves, A., Rezende, D., & Wierstra, D. (2015). DRAW: A Recurrent Neural Network For Image Generation. In International Conference on Machine Learning.
• Doersch, C. (2016). Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908.
• Burda, Y., Grosse, R., & Salakhutdinov, R. (2016). Importance weighted autoencoders. In International Conference on Learning Representations.
• Ranganath, R., Tran, D., & Blei, D. (2016). Hierarchical variational models. In International Conference on Machine Learning.
• Makhzani, A., Shlens, J., Jaitly, N., & Goodfellow, I. (2016). Adversarial Autoencoders. In International Conference on Machine Learning.
• Gregor, K., Besse, F., Rezende, D. J., Danihelka, I., & Wierstra, D. (2016). Towards conceptual compression. In Advances In Neural Information Processing Systems.
• Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., ... & Lerchner, A. (2017). beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. In International Conference on Learning Representations.
• Srivastava & Sutton (2017). Autoencoding Variational Inference for Topic Models. In International Conference on Learning Representations.
• Jang, E., Gu, S., & Poole, B. (2017). Categorical reparameterization with gumbel-softmax. In International Conference on Learning Representations.
• Mescheder, L., Nowozin, S., & Geiger, A. (2017). Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks. In International Conference on Machine Learning.
• Maddison, C. J., Lawson, J., Tucker, G., Heess, N., Norouzi, M., Mnih, A., ... & Teh, Y. (2017). Filtering variational objectives. In Advances in Neural Information Processing Systems.
• Alemi, A., Poole, B., Fischer, I., Dillon, J., Saurous, R. A., & Murphy, K. (2018). Fixing a Broken ELBO. In International Conference on Machine Learning.
• Chen, T. Q., Li, X., Grosse, R. B., & Duvenaud, D. K. (2018). Isolating sources of disentanglement in variational autoencoders. In Advances in Neural Information Processing Systems.

Amortized Inference / Recognition Models

• Dayan, P., Hinton, G. E., Neal, R. M., & Zemel, R. S. (1995). The Helmholtz Machine. Neural computation.
• Hinton, G. E., Dayan, P., Frey, B. J., & Neal, R. M. (1995). The" wake-sleep" algorithm for unsupervised neural networks. Science.
• Kingma, D. P., & Welling, M. (2014). Auto-encoding variational bayes. In International Conference on Learning Representations.
• Rezende, D. J., Mohamed, S., & Wierstra, D. (2014). Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In International Conference on Machine Learning.
• Gregor, K., Danihelka, I., Mnih, A., Blundell, C., & Wierstra, D. (2014). Deep AutoRegressive Networks. In International Conference on Machine Learning.
• Sønderby, C. K., Raiko, T., Maaløe, L., Sønderby, S. K., & Winther, O. (2016). Ladder variational autoencoders. In Advances in Neural Information Processing Systems.
• Marino, J., Yue, Y., & Mandt, S. (2018). Iterative Amortized Inference. In International Conference on Machine Learning.
• Marino, J., Cvitkovic, M., Yue, Y. (2018). A General Framework for Amortizing Variational Filtering. In NeurIPS 2018.

Sequential Latent Variable Models

• Bayer, J., & Osendorfer, C. (2014). Learning stochastic recurrent networks. arXiv preprint arXiv:1411.7610.
• Chung, J., Kastner, K., Dinh, L., Goel, K., Courville, A. C., & Bengio, Y. (2015). A recurrent latent variable model for sequential data. In Advances in neural information processing systems.
• Fraccaro, M., Sønderby, S. K., Paquet, U., & Winther, O. (2016). Sequential neural models with stochastic layers. In Advances in neural information processing systems.
• Gemici, M., Hung, C. C., Santoro, A., Wayne, G., Mohamed, S., Rezende, D. J., ... & Lillicrap, T. (2017). Generative temporal models with memory. arXiv preprint arXiv:1702.04649.
• Karl, M., Soelch, M., Bayer, J., & van der Smagt, P. (2017). Deep variational bayes filters: Unsupervised learning of state space models from raw data. In International Conference on Learning Representations.
• Krishnan, R. G., Shalit, U., & Sontag, D. (2017). Structured inference networks for nonlinear state space models. In AAAI Conference on Artificial Intelligence.
• Goyal, A. G. A. P., Sordoni, A., Côté, M. A., Ke, N. R., & Bengio, Y. (2017). Z-forcing: Training stochastic recurrent networks. In Advances in neural information processing systems.
• Fraccaro, M., Kamronn, S., Paquet, U., & Winther, O. (2017). A disentangled recognition and nonlinear dynamics model for unsupervised learning. In Advances in Neural Information Processing Systems.
• Denton, E., & Fergus, R. (2018). Stochastic Video Generation with a Learned Prior. In International Conference on Machine Learning.
• Fraccaro, M., Rezende, D., Zwols, Y., Pritzel, A., Eslami, S. A., & Viola, F. (2018). Generative Temporal Models with Spatial Memory for Partially Observed Environments. In International Conference on Machine Learning.
• Gregor, K., & Besse, F. (2019). Temporal difference variational auto-encoder. In International Conference on Learning Representations.
• Zhan, E., Zheng, S., Yue, Y., Sha, L., Lucey, P. (2019) Generating Multi-Agent Trajectories using Programmatic Weak Supervision. In ICLR 2019.

Normalizing Flows

• Rezende, D., & Mohamed, S. (2015). Variational Inference with Normalizing Flows. In International Conference on Machine Learning.
• Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., & Welling, M. (2016). Improved variational inference with inverse autoregressive flow. In Advances in neural information processing systems.

Invertible Latent Variable Models (Flow-Based Models)

• Dinh, L., Krueger, D., & Bengio, Y. (2015). NICE: Non-linear independent components estimation. In International Conference on Learning Representations.
• Dinh, L., Sohl-Dickstein, J., & Bengio, S. (2017). Density estimation using real nvp. In International Conference on Learning Representations.
• Papamakarios, G., Pavlakou, T., & Murray, I. (2017). Masked autoregressive flow for density estimation. In Advances in Neural Information Processing Systems.
• Oord, A., Li, Y., Babuschkin, I., Simonyan, K., Vinyals, O., Kavukcuoglu, K., ... & Casagrande, N. (2018). Parallel WaveNet: Fast High-Fidelity Speech Synthesis. In International Conference on Machine Learning.
• Huang, C. W., Krueger, D., Lacoste, A., & Courville, A. (2018). Neural Autoregressive Flows. In International Conference on Machine Learning.
• Kingma, D. P., & Dhariwal, P. (2018). Glow: Generative flow with invertible 1x1 convolutions. In Advances in Neural Information Processing Systems.
• Chen, T. Q., Rubanova, Y., Bettencourt, J., & Duvenaud, D. K. (2018). Neural ordinary differential equations. In Advances in Neural Information Processing Systems.
• Kumar, M., Babaeizadeh, M., Erhan, D., Finn, C., Levine, S., Dinh, L., & Kingma, D. (2019). VideoFlow: A Flow-Based Generative Model for Video. arXiv preprint arXiv:1903.01434.
• Grathwohl, W., Chen, R. T., Betterncourt, J., Sutskever, I., & Duvenaud, D. (2019). Ffjord: Free-form continuous dynamics for scalable reversible generative models. In International Conference on Learning Representations.

Deep Generative Models & Reinforcement Learning

• Jaderberg, M., Mnih, V., Czarnecki, W. M., Schaul, T., Leibo, J. Z., Silver, D., & Kavukcuoglu, K. (2016). Reinforcement learning with unsupervised auxiliary tasks. In International Conference of Learning Representations.
• Racanière, S., Weber, T., Reichert, D., Buesing, L., Guez, A., Rezende, D. J., ... & Pascanu, R. (2017). Imagination-augmented agents for deep reinforcement learning. In Advances in neural information processing systems.
• Wayne, G., Hung, C. C., Amos, D., Mirza, M., Ahuja, A., Grabska-Barwinska, A., ... & Gemici, M. (2018). Unsupervised predictive memory in a goal-directed agent. arXiv preprint arXiv:1803.10760.
• Nagabandi, A., Kahn, G., Fearing, R. S., & Levine, S. (2018). Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning. In International Conference on Robotics and Automation.
• Ha, D., & Schmidhuber, J. (2018). Recurrent world models facilitate policy evolution. In Advances in Neural Information Processing Systems.
• Chua, K., Calandra, R., McAllister, R., & Levine, S. (2018). Deep reinforcement learning in a handful of trials using probabilistic dynamics models. In Advances in Neural Information Processing Systems.
• Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., & Davidson, J. (2018). Learning Latent Dynamics for Planning from Pixels. arXiv preprint arXiv:1811.04551.

Causal Inference

• Louizos, C., Shalit, U., Mooij, J. M., Sontag, D., Zemel, R., & Welling, M. (2017). Causal Effect Inference with Deep Latent-Variable Models. In Advances in Neural Information Processing Systems.

Deep Structured Prediction

• Chen, Schwing, Yuille, Urtasun. (2015). Learning Deep Structured Models. In International Conference on Machine Learning.
• Graber, Meshi, Schwing. (2018). Deep Structured Prediction with Nonlinear Output Transformations. In Advances in Neural Information Processing Systems.
• Belanger & McCallum. (2016). Structured Prediction Energy Networks. In International Conference on Machine Learning.

Structured Inference

• Krishnan, Shalit, Sontag. (2017). Structured Inference Networks for Nonlinear State Space Models. AAAI 2017.
• Johnson, Duvenaud, Wiltschko, Datta, Adams. (2016). Composing graphical models with neural networks for structured representations and fast inference. In Advances in Neural Information Processing Systems.

Applications

• Saeedi, Hoffman, DiVerdi, Ghandeharioun, Johnson, Adams. (2018). Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. AISTATS 2018.