40959 Deep Generative Models

Course Description

Deep Generative Models

Course Information

Required Texts

  1. [BSH] Bishop, Christopher M. and Hugh Bishop, Deep Learning: Foundations and Concepts, Springer, 2024.

  2. [MUR] Murphy, Kevin P, Probabilistic Machine Learning: Advanced Topics, The MIT Press, 2023.

  3. [TOM] Tomczak, Jakub M., Deep Generative Modeling, Springer, 2022.

Grading Policy

  1. 20%: Mid-term exam (1403/01/25).

  2. 20%: Final exam (1403/03/26).

  3. 35%: Homeworks.

  4. 15%: Quiz.

  5. 10%: Paper & Explore a theoretical or empirical question and present it. Deadline for choosing paper: 1403/01/25.

  6. 5%: Class Activity

Lecture Schedule


Lecture Lecture Date Topics Related Readings and Links Homeworks & Assignments Quizes
1 1402-11-14Introduction: what is deep generative models? Chapter 20 of MUR
Chapter 1 of TOM
2
3
4
5
6
1402-11-17
1402-11-21
1402-11-23
1402-11-28
1402-11-30
Structured density Chapter 10 of MUR
Chapter 11 of BSH
7
8
1402-12-05
1402-12-07
Disentangled Representation Learning Papers given in the slides
9 1402-12-12Autoregressive Generative Models Chapter 22 of MUR
Chapter 2 of TOM
10
11
12
13
14
1402-12-14
1402-12-19
1402-12-21
1402-12-26
1403-01-18
Varational Autoencoder Chapter 21 of MUR
Chapter 4 of TOM
Papers given in the slides



Quiz 1
-
15 1403-01-20 Generative Adverserial networksChapter 26 of MUR
Chapter 7 of TOM
Papers given in the slides
16 1403-01-25 Mid-term exam Presentation topic selection deadline
17
18
1403-01-27
1403-02-01
Generative Adverserial networks Chapter 26 of MUR
Chapter 7 of TOM
Papers given in the slides
19
20
1403-02-03
1403-02-08
Flow-based models Chapter 22 of MUR
Chapter 3 of TOM
Papers given in the slides
21
22
23
1403-02-10
1403-02-17
1403-02-22
Energy-based models Chapter 24 of MUR
Chapter 3 of TOM
Papers given in the slides
24
25
26
1403-02-24
1403-02-29
1403-02-31
Diffusion Models Chapter 25 of MUR
Section 4,5,3 of TOM
Papers given in the slides
27 1403-03-05Evaluating Generative Model Section 20.4 1 of MUR
Papers given in the slides
28 1403-02-07Ethics of Generative AI
1403-03-26 Final exam At 15:30