Iterated Models: Expressive Power, Learning, and Chain of Thought
Offered By: Simons Institute via YouTube
Course Description
Overview
Explore the computational power and learning dynamics of iterated sequence-to-sequence models in this 54-minute lecture by Nati Srerbo from Toyota Technological Institute at Chicago. Delve into the concept of transformers as a computational model, focusing on systems that apply the same function at each step to generate the next token. Examine the expressive capabilities of these models, even with simple base classes, and investigate the sample and computational complexity of learning processes. Gain insights into both end-to-end learning and approaches that leverage the entire "chain of thought" in model training and inference.
Syllabus
Iterated Models: Expressive Power, Learning, and Chain of Thought
Taught by
Simons Institute
Related Courses
Introduction to Artificial IntelligenceStanford University via Udacity Natural Language Processing
Columbia University via Coursera Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent