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An Optimal Control Approach to Deep Learning

An Optimal Control Approach to Deep Learning

CSC-Room 304, 2 p.m., June 25, 2018

Dr. Qianxiao Li, Research Scientist at the Institute of High Performance Computing, A*STAR, Singapore and an adjunct assistant professor in the department of mathematics, National University of Singapore

Abstract: In this talk, we discuss a new approach to study the algorithmic and theoretical aspects of deep learning. In particular, the optimization of deep neural networks is recast as an optimal control problem, which is a classical problem that originates from the calculus of variations. Based on this viewpoint, we investigate the development of novel algorithms, as well as theoretical insights on generalization bounds of neural networks.

Bio of the speaker: Qianxiao Li is a research Scientist at the Institute of High Performance Computing, A*STAR, Singapore and an adjunct assistant professor in the department of mathematics, National University of Singapore. He graduatedwith a BA in Mathematics from University of Cambridge in 2010, and a PhD in applied mathematics from Princeton University in 2016. His main research interests include the theory and algorithms for deep learning, stochastic optimization and applications ofmachine learning to the physical sciences.