Ohad Shamir - COLLOQUIUM LECTURE
Tuesday, 5.11.2019, 14:30
Advisor: Weizmann Institute
Artificial neural networks are nowadays routinely trained to solve challenging learning
tasks, but our theoretical understanding of this phenomenon remains quite limited. One
increasingly popular approach, which is aligned with practice, is to study how making the
network sufficiently large (a.k.a. ''over-parameterized'') makes the associated training
problem easier. In this talk, I'll describe some of the possibilities and challenges in
understanding neural networks using this approach.
Based on joint works with Itay Safran and Gilad Yehudai.
Ohad Shamir is a faculty member at the Department of Computer Science
and Applied Mathematics at the Weizmann Institute. He received his PhD
in 2010 at the Hebrew University, and between 2010-2013 and 2017-2018
was a researcher at Microsoft Research in Boston. His research focuses
on theoretical machine learning, in areas such as theory of deep
learning, learning with information and communication constraints, and
topics at the intersection of machine learning and optimization. He
received several awards, and served as program co-chair of COLT as well
as a member of its steering committee.
Refreshments will be served from 14:15
Lecture starts at 14:30