Non-Euclidean Generative Modeling

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.