Distinguished Lecture: The quest to open the black box of Deep Learning

Abstract: Deep learning has rapidly come to dominate AI and machine learning in
the past decade. These successes have come despite deep learning largely
being a "black box." Now a small sub-discipline has grown up trying to
derive better understanding of the underlying mathematical properties. 
Via a tour d'horizon of recent theoretical analyses of deep learning in
some concrete settings,  we illustrate how the black box view can miss
out on (or even be wrong about) special phenomena going on during
training. These phenomena are also not captured by the training
objective. We argue that understanding such phenomena via mathematical
understanding will be crucial for enabling the full range of future
applications.

Bio: Sanjeev Arora is Charles C. Fitzmorris Professor of Computer
Science at Princeton University. He has received Packard Fellowship
(1997), Simons Investigator Award (2012), Gödel Prize (2001 and 2010),
ACM Prize in Computing  (2012), and the Fulkerson Prize (2012). He is a
Member of NAS and a Fellow of the ACM and the AAAS.