Lecture schedule

(Note: this schedule is subject to change)

Lecture 1 (4/2/2019): Introduction & Course Overview

• Presented by Yisong & Joe
• Administrivia
• Introduction to Probabilistic Modeling & Deep Learning
• Slides Part 1
• Slides Part 2

Lecture 2 (4/4/2019): Classical Latent Variable Models

• PCA, Gaussian Mixture Models, EM Algorithm, Linear Factor Models, Variational Inference
• Presented by Jeremy & Joe
• Slides and notes on the EM algorithm.

Lecture 3 (4/9/2019): Explicit Latent Variable Models w/ Variational Inference

• Lectured by Joey Hong & Rohan Choudhury
• Mentored by Joe
• Slides, Tutorial
• Dayan, P., Hinton, G. E., Neal, R. M., & Zemel, R. S. (1995). The Helmholtz Machine.
• Kingma, D. P., & Welling, M. (2014). Auto-encoding variational bayes.
• See also:
  • Hinton, G. E., Dayan, P., Frey, B. J., & Neal, R. M. (1995). The "wake-sleep" algorithm for unsupervised neural networks.
  • Rezende, D. J., Mohamed, S., & Wierstra, D. (2014). Stochastic Backpropagation and Approximate Inference in Deep Generative Models.
  • Doersch, C. (2016). Tutorial on variational autoencoders.

Lecture 4 (4/11/2019): Auto-regressive Models

• Lectured by Vinayak Kumar, Mayank Pandey, Alycia Lee, Albert Tseng, Eric Han & Emma Qian
• Mentored by Jeremy
• Slides
• Graves, A. (2013). Generating sequences with recurrent neural networks.
• Van den Oord, A., Kalchbrenner, N., & Kavukcuoglu, K. (2016). Pixel Recurrent Neural Networks.
• See also:
  • Bengio, Y., & Bengio, S. (2000). Modeling high-dimensional discrete data with multi-layer neural networks.
  • Van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., ... & Kavukcuoglu, K. (2016). Wavenet: A generative model for raw audio.

Lecture 5 (4/16/2019): Generative Adversarial Networks

• Lectured by Daniel Guth, Siddharth Prasad, Jieni Li, Jing Ding & Sunny Can
• Slides & Demo
• Mentored by Yisong
• Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets.
• Goodfellow, I, (2016) NIPS 2016 Tutorial: Generative Adversarial Networks
• Brock, A., Donahue, J., & Simonyan, K. (2019). Large scale gan training for high fidelity natural image synthesis.
• See also:
  • Radford, A., Metz, L., & Chintala, S. (2016). Unsupervised representation learning with deep convolutional generative adversarial networks.

Lecture 6 (4/18/2019): Flow-based Models & Normalizing Flows

• Lectured by Kevin Yu, Jesse Cai, Akshay Vegesna, Alex Lettenberger & Rafael Fueyo-Gomez
• Slides & Demo 1, Demo 2
• Mentored by Joe
• Dinh, L., Sohl-Dickstein, J., & Bengio, S. (2017). Density estimation using real nvp.
• Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., & Welling, M. (2016). Improved variational inference with inverse autoregressive flow.
• See also:
  • Dinh, L., Krueger, D., & Bengio, Y. (2015). NICE: Non-linear independent components estimation.
  • Rezende, D., & Mohamed, S. (2015). Variational Inference with Normalizing Flows.
  • Papamakarios, G., Pavlakou, T., & Murray, I. (2017). Masked autoregressive flow for density estimation.

Lecture 7 (4/23/2019): Discrete Latent Variable Models

• Lectured by Yoojin (Terry) Chung, Brandon Quach, Daniel Kyme, Alveera Khan & Ruoyun Zheng
• Slides
• Mentored by Jeremy
• Mnih, A., & Gregor, K. (2014). Neural Variational Inference and Learning in Belief Networks.
• Jang, E., Gu, S., & Poole, B. (2017). Categorical reparameterization with gumbel-softmax.
• See also:
  • Dayan, P., Hinton, G. E., Neal, R. M., & Zemel, R. S. (1995). The Helmholtz Machine.
  • Gregor, K., Danihelka, I., Mnih, A., Blundell, C., & Wierstra, D. (2014). Deep AutoRegressive Networks.
  • Maddison, C. J., Mnih, A., & Teh, Y. W. (2016). The concrete distribution: A continuous relaxation of discrete random variables.
  • Tucker, G., Mnih, A., Maddison, C. J., Lawson, J., & Sohl-Dickstein, J. (2017). Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models.

Lecture 8 (4/25/2019): Sequential Latent Variable Models

• Lectured by Erich Liang, Sarina Liu, Christine Yu, Kevin Yu & Kushal Tirumala
• Slides
• Mentored by Eric
• Chung, J., Kastner, K., Dinh, L., Goel, K., Courville, A. C., & Bengio, Y. (2015). A recurrent latent variable model for sequential data.
• Yingzhen Li, Stephan Mandt (2018). Disentangled Sequential Autoencoder.
• See also:
  • Fraccaro, M., Sønderby, S. K., Paquet, U., & Winther, O. (2016). Sequential neural models with stochastic layers.
  • Goyal, A. G. A. P., Sordoni, A., Côté, M. A., Ke, N. R., & Bengio, Y. (2017). Z-forcing: Training stochastic recurrent networks.

Lecture 9 (4/30/2019): Energy-Based Models

• Lectured by Alex Janosi, Chad Thut, Jacob Snyder, Richard Feder-Staehle
• Mentored by Yisong
• Slides
• Salakhutdinov, R., & Hinton, G. (2009). Deep boltzmann machines.
• Du, Y., & Mordatch, I. (2019). Implicit Generation and Generalization with Energy-Based Models.
• See also:
  • LeCun, Y., Chopra, S., Hadsell, R., Marc’Aurelio, R., & Huang, F. J. (2006). A tutorial on energy-based learning.
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Chapters 16, 18, and 20.

Lecture 10 (5/2/2019): Neural Autoregressive Networks

• Lectured by Ankush Hommerich-Dutt, Jagath Vytheeswaran, Anirudh Rangaswamy, Aidan Swope & Brendan Hollaway
• Slides
• Mentored by Joe
• Larochelle, H., & Murray, I. (2011). The neural autoregressive distribution estimator.
• Uria, B., Murray, I., & Larochelle, H. (2014). A deep and tractable density estimator.
• See also:
  • Uria, B., Murray, I., & Larochelle, H. (2013). RNADE: The real-valued neural autoregressive density-estimator.
  • Germain, M., Gregor, K., Murray, I., & Larochelle, H. (2015). Made: Masked autoencoder for distribution estimation.

Lecture 11 (5/7/2019): Deep Structured Prediction w/ Message Passing

• Lectured by Alex Guerra, Clare Hao & Umesh Padia
• Slides
• Mentored by Hoang
• Chen, Schwing, Yuille, Urtasun. (2015). Learning Deep Structured Models. In International Conference on Machine Learning.
• Additional Reading:
  • Course Notes on Message Passing
  • Yedidia et al., Understanding Belief Propagation and its Generalizations
  • Messaoud et al., ECCV 2018. Structural Consistency and Controllability for Diverse Colorization
  • Graber et al., NeurIPS 2018. Deep Structured Prediction with Nonlinear Output Transformations

Lecture 12 (5/9/2019): Adversarial Autoencoders

• Lectured by Karena Cai, Matthew Levine, Jennifer Sun & Xiaoqiao Chen
• Slides
• Mentored by Jialin
• Mescheder, L., Nowozin, S., & Geiger, A. (2017). Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks.
• See also:
  • Makhzani, A., Shlens, J., Jaitly, N., & Goodfellow, I. (2016). Adversarial Autoencoders.

Lecture 13 (5/14/2019): Language Models, Transformers

• Lectured by Megan Durney, Sophia Coplin, Tristan Ang & Surya Mathialagan
• Slides
• Mentored by Jeremy
• Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners.
• See also:
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need.
  • Google AI Blog Post
  • Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training.

Lecture 14 (5/16/2019): Generative Model Evaluation

• Lectured by Nick Haliday, Tim Krasnoperov, Pai Buabthong, Zhewei Chen & Hongsen Qin
• Slides
• Mentored by Jeremy
• Theis, L., van den Oord, A., & Bethge, M. (2016). A note on the evaluation of generative models.
• Alemi, A. A., & Fischer, I. (2018). GILBO: One Metric to Measure Them All.
• See also:
  • Wu, Y., Burda, Y., Salakhutdinov, R., & Grosse, R. (2017). On the Quantitative Analysis of Decoder-Based Generative Models.

Lecture 15 (5/21/2019): Disentangled Representation Learning

• Lectured by Neehar Kondapaneni, Guruprasad Raghavan, Francesca Baldini, Charles Guan & Kadina Johnston
• Mentored by Yisong
• Slides & InfoGAN Demo
• Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016). Infogan: Interpretable representation learning by information maximizing generative adversarial nets.
• 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.
• See also:
  • Chen, T. Q., Li, X., Grosse, R. B., & Duvenaud, D. K. (2018). Isolating sources of disentanglement in variational autoencoders.

Lecture 16 (5/23/2018): Conditional Generation

• Lectured by Alexander Cui, Anne Zhou, Matthew Wu, Victor Chen & Josh Chen
• Mentored by Yisong
• Slides
• Isola, P., Zhu, J. Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks.
• 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.
• See also:
  • Van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., & Graves, A. (2016). Conditional image generation with pixelcnn decoders.
  • Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks.

Lecture 17 (5/28/2019): W-GANs + Spectral Normalization

• Lectured by Yujia Huang, Tongxin Li, Hao Liu, Sihui (Sophie) Dai & Tanvi Gupta
• Mentored by Jeremy
• Slides
• Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein gan.
• Miyato, T., Kataoka, T., Koyama, M., & Yoshida, Y. (2018). Spectral normalization for generative adversarial networks.

Lecture 18 (5/30/2019): Generative Models & Reinforcement Learning

• Lectured by Luke Juusola, Robin Henry, Michaelangelo Caporale, Anthony Bao & Ethan Pronovost
• Mentored by Joe
• Slides
• Ha, D., & Schmidhuber, J. (2018). Recurrent world models facilitate policy evolution.
• Chua, K., Calandra, R., McAllister, R., & Levine, S. (2018). Deep reinforcement learning in a handful of trials using probabilistic dynamics models.
• See also:
  • Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., & Davidson, J. (2018). Learning Latent Dynamics for Planning from Pixels.

Lecture 19 (6/4/2019): Causal Models

• Lectured by Danny Sawyer, Avinoam Bar Zion, David Mittelstein, Karthik Nair & Ted Yu
• Mentored by Yisong
• Slides
• Louizos, C., Shalit, U., Mooij, J. M., Sontag, D., Zemel, R., & Welling, M. (2017). Causal Effect Inference with Deep Latent-Variable Models.
• See also:
  • Introduction to the foundations of causal discovery
  • Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness
  • Visual Causal Feature Learning
  • Unsupervised Discovery of El Nino˜ Using Causal Feature Learning on Microlevel Climate Data

Lecture 20 (6/6/2019): Poster Session