A Geometric Understanding of Deep Learning
Thursday, October 27, 2022
Deep learning (DL) has achieved great successes, but understanding of DL remains primitive. In this talk, we try to answer some fundamental questions about DL through a geometric perspective: what does a DL system really learn ? How does the system learn? Does it really learn or just memorize the training data sets? How to improve the learning process?
Speaker/s
Dr. David Xianfeng Gu got his Bachelor degree in Computer Science from Tsinghua University; master degree from Harvard University, supervised by a Fields medalist, Prof. David Mumford; and PhD from Harvard University, supervised by another Fields medalist, Prof. Shing-Tung Yau. Dr. Gu currently is a SUNY Empire Innovation Professor at the Computer Science Department in the State University of New York at Stony Brook. David is also affiliated with the Applied Mathematics Department at the SUNY Stony Brook, Yau Mathematical Science Center at Tsinghua University and the Center of Mathematical Sciences and Applications at Harvard University. David is also the director of the 3D scanning laboratory of SUNY Stony Brook, the chief scientist of Beijing Advanced Innovation Center for Imaging Technology.
- AI Talks ,
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