Learning with Feature Geometry

Release Time:2022-12-07Number of visits:3516

Speaker:       Lizhong ZhengMassachusetts Institute of Technology

Time:            9:30-10:30am  Dec.09.2022

Host:            Youlong Wu

Location:        SIST 1A 200



In this talk, we view statistical learning as choosing feature functions that carry useful information and developing the corresponding metrics and operations for the design of the process to find such features. In particular, we show that deep neural networks is one particular method of achieving this goal. Based on that observation, we propose more flexible ways to connect neural networks for more complex learning tasks, for example with multi-modal data and coordination with remote terminals. This talk offers an overview of the method of information geometry used to describe feature function spaces and is designed to propose some new research directions in incorporating external knowledge in the use of deep neural networks.



Lizhong Zheng received the B.S and M.S. degrees, in 1994 and 1997 respectively, from the Department of Electronic Engineering, Tsinghua University, China, and the Ph.D. degree, in 2002, from the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley. Since 2002, he has been working at MIT, where he is currently a professor of Electrical Engineering. His research interests include information theory, statistical inference, communications, and networks theory. He received Eli Jury award from UC Berkeley in 2002, IEEE Information Theory Society Paper Award in 2003, and NSF CAREER award in 2004, and the AFOSR Young Investigator Award in 2007. He served as an associate editor for IEEE Transactions on Information Theory, and the general co-chair for the IEEE International Symposium on Information Theory in 2012. He is an IEEE fellow.