Academics / Courses / DescriptionsCOMP_SCI 496: Generative Deep Models
VIEW ALL COURSE TIMES AND SESSIONS
Prerequisites
CS PhD students or Permission of InstructorDescription
Deep learning is a branch of machine learning based on algorithms that try to model high-level abstract representations of data by using multiple processing layers with complex structures. One of the most exciting areas of research in deep learning is that of generative models. Today’s generative models create text documents, write songs, make paintings and videos, and generate speech. This course is dedicated to understanding the inner workings of the technologies that underlie these advances. Students will learn about key methodologies, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformer-based language models. This is an advanced course that presumes a good working understanding of traditional supervised neural network technology and techniques (e.g. convolutional networks, LSTMs, loss functions, regularization, gradient descent).
REQUIRED TEXTBOOK: Advanced research papers in the field.
REFERENCE TEXTBOOKS: (not required purchases) The focus will be on research papers published in the field.
COURSE COORDINATOR: Prof. Bryan Pardo
COURSE GOALS: The goal of this course is to familiarize graduate students (and advanced undergraduates) with the current state-of-the-art in machine generation of speech, music, still images and video using Deep Learning architectures. Students will read recently published papers in the field and become well informed on at least one sub-field within this area.
DETAILED COURSE TOPICS:
What follows is an example syllabus. As topics of current interest in the field shift, course content will vary to reflect research trends.
Week 1: Autoencoders
Week 2: Variational Autoencoders (VAEs)
Week 3: Conditional VAEs and Wasserstein VAEs
Week 4: Generative Adversarial Networks (GANs)
Week 5: Conditional GANs
Week 6: Transformers & Language Modeling
Week 7: Recent Transformer Architectures (BERT, GPT-3)
Week 8: Creative Applications of Generative Models
Week 9: Societal Impacts (Deep Fakes, Adversarial Examples)
Week 10: Student Project Presentations
ASSIGNMENTS:
Presentation on topic (30%)
Research paper synopses (30%)
Final project (30%)
Class participation (10%)
COURSE OBJECTIVES: When a student completes this course, s/he should:
- have a general understanding of the current state-of-the art in generative models.
- be able to distill large amounts of research into coherent summaries.
- be able to think critically about work in the field.