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Stanford University CS236: Deep Generative Models
Detailed Syllabus
Current quarter's videos are available through Panopto.
Course notes are published here.
| Week | Date | Lecture Topics | Coursework | Sections |
|---|---|---|---|---|
| 1 | Sep 27 | Introduction (Slides) | Probability and Linear Algebra | |
| 2 | Oct 02 & Oct 04 | Background (Slides) + Autoregressive Models (Slides) | HW 1 Released (Oct 02) | PyTorch |
| 3 | Oct 09 & Oct 11 | Maximum Likelihood Learning (Slides) + VAEs (Slides) | CNNs, RNNs, Transformers | |
| 4 | Oct 16 & Oct 18 | VAEs (Slides) + Normalizing Flows (Slides) | HW 1 due, HW 2 released (Oct 16) | |
| 5 | Oct 23 & Oct 25 | Normalizing Flows (Slides) + GANs (Slides) | ||
| Project Proposal: Due Wednesday, October 25, 2023 | ||||
| 6 | Oct 30 & Nov 01 | GANs (Slides) + Energy Based Models (Slides) | HW 2 due (Oct 30) | |
| 7 | Nov 06 & Nov 08 | Energy Based Models (Slides) + Score Based Models (Slides) | HW 3 released (Nov 06) | |
| Midterm: Day: Nov 10 - Time: 6-9pm - Location: CEMEX (Last names A-L), HEWLETT200 (Last names M-Z) | ||||
| 8 | Nov 13 & Nov 15 | Energy Based Models (Slides) + Evaluation of Generative Models (Slides) | ||
| Project Progress Report: Due Wednesday, November 15, 2023 | ||||
| 9 | Nov 20 & Nov 22 | Thanksgiving Break | ||
| 10 | Nov 27 & Nov 29 | Score Based Diffusion Models (Slides) + Discrete Latent Variable Models (Slides) | HW 3 due (Nov 27) | |
| 11 | Dec 04 & Dec 06 | Diffusion Models for Discrete Data (Slides) | ||
| Poster Presentation: Wednesday, December 6, 2023 from 3:00 pm - 6:00 pm | ||||
| 12 | Dec 11 & Dec 13 | Finals Week | ||
| Final Project Report: Due Monday, December 11, 2023 | ||||
Additional Reading: Surveys and Tutorials
- Generative Modeling by Estimating Gradients of the Data Distribution Yang Song. Blog post on score-based generative models, May 2021.
- How to Train Your Energy-Based Models. Yang Song and Diederik P. Kingma. February 2021.
- Tutorial on Deep Generative Models. Aditya Grover and Stefano Ermon. International Joint Conference on Artificial Intelligence, July 2018.
- Tutorial on Generative Adversarial Networks. Computer Vision and Pattern Recognition, June 2018.
- Tutorial on Deep Generative Models. Shakir Mohamed and Danilo Rezende. Uncertainty in Artificial Intelligence, July 2017.
- Tutorial on Generative Adversarial Networks. Ian Goodfellow. Neural Information Processing Systems, December 2016.
- Learning deep generative models. Ruslan Salakhutdinov. Annual Review of Statistics and Its Application, April 2015.