[콜로퀴엄] Applications of Optimal Transport in Machine Learning (24/6/14)
1. 일시 : 2024년 06월 14일(금) 오후 4시~5시 2. 장소 : 아산이학관 526호(권택연 세미나실) 3. 연사 : 권도현 교수님 (서울시립대학교) 4. 제목 : Applications of Optimal Transport in Machine Learning 5. 초록 : Over the past few decades, optimal transport theory has gained increasing interest across multiple fields, including partial differential equations, probability, and machine learning. In this talk, we explore the diverse applications of optimal transport theory within various machine learning problems, with a specific focus on generative models and dictionary learning. Our discussion begins by examining gradient flows in the space of probability measures equipped with the distance arising from the Monge-Kantorovich optimal transport problem. We then analyze a score-based generative model based on the Fokker-Planck equations that underlie both the forward and reverse processes of the model.