CS-E407509 - Deep Generative Modelling
Introduction
For each of the assigned chapters, you are required to prepare a couple of notes summarizing the chapter. There should be three sections covering
- Main points of the chapter
- Questions/unclear points
- Further comments/thoughts
There should be roughly three bullet points for each of them, without that being a hard constraint. For example, if you do not have any questions because everything is clear, then just focus more on the other two. The main focus of this task is not to spend too much time on this, or to submit overly long documents, but primarily for you to have a possibility to structure your thoughts while reading the chapter. We will then rely on these points to guide the discussion each week.
Content
Session 1
- Chapter 10: Variational Inference
Read 10.1, 10.2.1, 10.2.3-5, 10.3.1, 10.3.2-3, 10.3.7-8, 10.5.1 Skim 10.2.2, 10.2.6, 10.3.4-6, 10.4, 10.5.2, 10.7 Skip 10.2.2, 10.2.7-8, 10.6
Session 2
- Chapter 11: Markov Chain Inference
- Chapter 12: Markov Chain Monte Carlo Inference
Session 3
- Chapter 17: Bayesian Neural Networks
Session 4
- Chapter 18: Gaussian Processes
Session 5
- Chapter 22: Variational Autoencoders
Session 6
- Chapter 23: Auto-regressive models
Session 7
- Chapter 24: Normalizing flows
Session 8
- Chapter 25: Energy-based models
Session 9
- Chapter 26: Generative Adversarial Networks