Electrical Engineering & Center for Statistics and Machine Learning Seminar
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In this talk, we offer an entirely “white box’’ interpretation of deep (convolutional) networks from the perspective of data compression. In particular, we show how modern deep architectures, linear (convolution) operators and nonlinear activations, and parameters of each layer can be derived from the principle of rate reduction (and invariance). All layers, operators, and parameters of the network are explicitly constructed via forward propagation, instead of learned via back propagation. All components of such a network have precise optimization, geometric, and statistical meaning. There are also several nice surprises from this principled approach that shed new light on fundamental relationships between forward (optimization) and backward (variation) propagation, between invariance and sparsity, and between deep networks and Fourier transform.
Yi Ma is a Professor in residence at the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He received his Bachelor’s degree from Tsinghua University in 1995 and MS and PhD degrees from UC Berkeley in 2000. His research interests are in computer vision, high-dimensional data analysis, and intelligent systems. He has been on the faculty of UIUC ECE from 2000 to 2011, the manager of the Visual Computing group of Microsoft Research Asia from 2009 to 2014, and the Dean of the School of Information Science and Technology from 2014 to 2017. He has published over 160 papers and three textbooks in computer vision, statistical learning, and data science. He received NSF Career award in 2004 and ONR Young Investigator award in 2005. He also received the David Marr prize in computer vision in 1999 and has served as Program Chair and General Chair of ICCV 2013 and 2015, respectively. He is a Fellow of IEEE, SIAM, and ACM.
This seminar is supported by CSML and EE Korhammer Lecture Series Fund