Detailed Syllabus


WeekDateLecture TopicsCourseworkAdditional Readings
1 Jan 21 & 23 Introduction and Background
(slides 1, slides 2)
2 Jan 28 & 30 Autoregressive Models
(slides 3, slides 4) 		HW 1 released van den Oord et al. (2016a, 2016b) Kalchbrenner et al. (2016) Vaswani et al. (2017)
3 Feb 4 & 6 Variational Autoencoders
(slides 5, slides 6) 		Kingma et al. (2014) Gregor et al. (2015) Burda et al. (2016) Maddison et al. (2017)
4 Feb 11 & 13 Normalizing Flow Models
(slides 7, slides 8) 		HW1 due (02/13), HW 2 released Kingma and Dhariwal (2018) Chen et al. (2018) Chen et al. (2019) Kumar et al. (2019)
5 Feb 18 & 20 Generative Adversarial Networks
(slides 9, slides 10) 		Dumoulin et al. (2016) Arjofsky et al. (2017) Zhu et al. (2017)
Project Proposal: Due Thursday, February 20, 2020.
6 Feb 27 Energy-Based Models
(slides 11) 		HW 2 due (02/27)
7 Mar 3 & 4 Combining Generative Model Variants
Evaluating Generative Models
(slides 12, slides 13)
8 Mar 10 & 12 Discreteness in Generative Modeling
(slides 14)
March 12: Postponed due to move to videoconferencing. 		HW 3 released
9 Mar 17 & 19 Student Presentations
3/17: Boyi Li and Junwen Bai: Uncertainty in DGMs
3/19: Yixin Shen and Youya Xia: Generative Models in RL
10 Mar 24 & 26 Student Presentations
03/24: Evgenii Nikishin and Yicheng Bai: Noise Contrastive Estimation
03/26: Utkarsh Mall and Hubert Lin 		HW 3 due (03/26)
11 Mar 31 & Apr 2 Spring Break
12 Apr 7 & 9 Student Presentations
04/07: Jack Wang and Joseph Kim
04/09: Dan Adler and Gengmo Qi
Project Progress Report: Due April 9, 2020.
13 Apr 14 & 16 Student Presentations
04/14: Yong Huang and Yordanos Goshu: Combining GANs and variational inference
04/16: Kai Zhang, and Rui Qian
14 Apr 21 & 23 Student Presentations
04/21: Guandao Yang and Wenqi Xian: Normalizing Flows
04/23: Shachi Deshpande, Alex Wang and Arman Mielke
15 Apr 28 & 30 04/28: Joseph Kim and Zekun Hao: Wasserstein GANs
04/30: Guest Lecture
16 May 5 Student Presentations
05/06: Kane Tian and Aaron Lou
17 May 9-16 Exam Week (no lectures)
Final Project Reports: Due May 14, 2020.

Additional Reading: Surveys and Tutorials


  1. Tutorial on Deep Generative Models. Aditya Grover and Stefano Ermon. International Joint Conference on Artificial Intelligence, July 2018.
  2. Tutorial on Generative Adversarial Networks. Computer Vision and Pattern Recognition, June 2018.
  3. Tutorial on Deep Generative Models. Shakir Mohamed and Danilo Rezende. Uncertainty in Artificial Intelligence, July 2017.
  4. Tutorial on Generative Adversarial Networks. Ian Goodfellow. Neural Information Processing Systems, December 2016.
  5. Learning deep generative models. Ruslan Salakhutdinov. Annual Review of Statistics and Its Application, Apr 2015.