CS 295: Deep Generative Models
Day/Time: Tuesday and Thursday 11:00-12:20pm
Location: PCB 1200
Course Code: 34875
Generative models are an important class of machine learning models due to their ability to produce artificial data. Deep generative models build on recent advances in the fields of deep learning and approximate inference, and make it possible to create structured data that highly resemble the data on which they were trained, such as images, audio, text, or video. This course will introduce students to the probabilistic foundations of deep generative models with an emphasis on variational autoencoders (VAEs), generative adversarial networks (GANs), and the training paradigm of black box variational inference. Advanced topics that will be covered include normalizing flows, variational dropout, disentangled representations, deep sequential models, alternative variational bounds, and the information bottleneck. We will discuss diverse applications from the domains of computer vision, speech, NLP, and compression.
- An introductory machine learning course (CS 178, CS 273A, or equivalent).
- An introductory artificial intelligence course (CS 171, CS 271, or equivalent).
- Advanced courses on graphical models (CS 274B or CS 276), probabilistic learning (CS 274A), or neural networks (CS 274C) are a plus.
- Students should have basic knowledge of probability and calculus.
- Programming assignments will require a working familiarity with Python.
As part of the course, students will work on a research project in small groups. This project may deal with topics such as applications of deep generative models to a new domain or dataset, or improvements on the inference of deep generative models.
There is no book on the course topic. For deep learning, the textbook by Goodfellow, Bengio, and Courville is recommended (free online version
). Links to relevant research papers will be provided.
Generative Models and Variational Inference
Generative Adversarial Networks
Structured Generative Models
Discrete Latent Variables in Deep Models
Information Theoretic Perspectives
Deep Sequential Models
Video Prediction, Text Generation, and Forecasting
Structured VAEs and Image/Video Compression