Artificial Intelligence Seminar

— 1:00pm

Location:
In Person and Virtual - ET - ASA Conference Room, Gates Hillman 6115 and Zoom

Speaker:
XIANGXIANG XU , Postdoctoral Associate, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technologyu
https://xiangxiangxu.mit.edu/

A Geometric Perspective of Feature Learning

In this talk, we present a geometric framework for learning and processing information with deep neural networks. We introduce feature geometry, which unifies statistical dependence and feature representations in a function space. We formulate each learning problem as solving the optimal feature representation of the associated dependence component. We will illustrate how this perspective connects distinct learning problems and provides more adaptable solutions, from classification/estimation to feature selection/extraction. We also demonstrate its applications in complicated learning scenarios, including dealing with constraints and incomplete data, incorporating side information, and learning the dependence structures of sequential data. 

— 

Xiangxiang Xu received the B.Eng. and Ph.D. degrees in electronic engineering from Tsinghua University, Beijing, China, in 2014 and 2020, respectively. He is a postdoctoral associate in the Department of EECS at MIT. His research focuses on information theory and statistical learning, with applications in understanding and developing learning algorithms. 

The AI Seminar is sponsored in part by SambaNova Systems

In Person and Zoom Participation.  See announcement.

Event Website:
http://www.cs.cmu.edu/~aiseminar/

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