Non-Euclidean Generative Modeling
Offered By: Fields Institute via YouTube
Course Description
Overview
Explore non-Euclidean generative modeling in this 31-minute conference talk by Molei Tao from Georgia Institute of Technology, presented at the Fourth Symposium on Machine Learning and Dynamical Systems. Delve into advanced concepts that bridge the gap between machine learning and dynamical systems, focusing on innovative approaches to generative modeling in non-Euclidean spaces. Gain insights into cutting-edge research and potential applications in fields such as computer vision, robotics, and data analysis.
Syllabus
Non-Euclidean Generative Modeling
Taught by
Fields Institute
Related Courses
Introduction to Dynamical Systems and ChaosSanta Fe Institute via Complexity Explorer Algorithmic Information Dynamics: From Networks to Cells
Santa Fe Institute via Complexity Explorer Introduction to Differential Equations
Santa Fe Institute via Complexity Explorer Introduction to Dynamical Systems and Chaos
Santa Fe Institute via Complexity Explorer Linear Differential Equations
Boston University via edX