CS 295: Deep Generative Models

Instructor: Prof. Stephan Mandt
Teaching Assistant: Aodong Li
Fall 2021
Day/Time: Monday and Wednesday 11:00-12:20pm
Location: Subject to change, check Canvas website for updates
Course Code: 34770

Attention: the main course page with all relevant and updated information is on Canvas (accessible with your UCI credentials).

Course Summary

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Generative models are an important class of machine learning models that aim to learn the data distribution. Deep generative models build on recent advances in the fields of deep learning, and make it possible to sample data that highly resemble the structure of the data on which these models were trained. Recent success stories of deep generative models include Google’s WaveNet which set a new state of the art for voice synthesis, Transformer Networks for highly accurate machine translation, CycleGAN for weakly-supervised style transfer between images or videos, neural compression algorithms that outperform their classical counterparts, and deep generative models for molecular design. This course will introduce students to the probabilistic foundations of deep generative models with an emphasis on variational autoencoders (VAEs), generative adversarial networks (GANs), autoregressive models, and normalizing flows. Advanced topics that will be covered include black-box variational inference, variational dropout, disentangled representations, deep sequential models, alternative variational bounds, and information theoretical perspectives on VAEs. We will discuss diverse applications from the domains of computer vision, speech, NLP, and data compression.

Prerequisites

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At minimum:

• An introductory machine learning course (CS 178, CS 273A, or equivalent) is absolutely required.
• 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 be very familiar with probability, calculus, and linear algebra (e.g., moments of a random variable, Eigendecomposition, Monte Carlo integral, etc.).
• Programming assignments will require a working familiarity with Python.

Course Project

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.

Suggested Reading

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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.

Tentative Content

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Introduction

Generative Models and Variational Inference

Autoregressive Models

Variational Autoencoders

Normalizing Flows

Generative Adversarial Networks

Structured Generative Models

Discrete Latent Variables in Deep Models

Information Theoretic Perspectives

Deep Sequential Models

Structured VAEs and Image/Video Compression

Academic Integrity

All students are expected to be familiar with the policy below. Failure to adhere to this policy can result in a student receiving a failing grade in the class.

Academic integrity is taken seriously. For homework problems or programming assignments you are allowed to discuss the problems or assignments verbally with other class members, but under no circumstances can you look at or copy anyone else's written solutions or code relating to homework problems or programming assignments. All problem solutions and code submitted must be material you have personally written during this quarter, except for (a) material that you clearly indicate and reference as coming from another source, or (b) code provided to you by the TA/reader or instructor.

It is the responsibility of each student to be familiar with UCI's Academic Integrity Policies and UCI's definitions and examples of academic misconduct.