Speaker: Mikhail Belkin, Dept. of CSE
Time: Wed 11/20, 4pm-5pm
Location: Dreese Lab 480
Title: It’s time to think again
Abstract: Deep learning has drastically changed the practice in many applied areas of AI. But an equally profound and not yet fully recognized impact of deep learning is in forcing us to rethink many deeply held beliefs and theoretical assumptions. I will discuss some of these ideas, such as over-fitting and capacity control, and show why they fail to adequately describe modern machine learning. I will point to the types of analyses we need to understand and develop modern practice.
Bio: Mikhail Belkin is a Professor in the departments of Computer Science and Engineering and Statistics at the Ohio State University. He received a PhD in mathematics from the University of Chicago in 2003. His research focuses on understanding structure in data, the principles of recovering such structures, and their computational, mathematical and statistical properties. His notable work includes algorithms such as Laplacian Eigenmaps and Manifold Regularization, which use ideas of classical differential geometry for analyzing non-linear high-dimensional data. He is the recipient of a NSF Career Award, and has served on editorial boards of the Journal of Machine Learning Research and IEEE PAMI.