Alan Yuille: Deep Networks and Beyond
Deep Networks are very successful for many visual task but their performance still fall far short of human visual abilities. Humans can learn from a few examples, with very weak supervision, can adapt to unknown factors like occlusion, can generalize from objects we know to objects which we do not. This talk will describe some state of the art work on deep networks but also discuss some of their limitations.
Alan Yuille received his B.A. in mathematics from the University of Cambridge in 1976, and completed his Ph.D. in theoretical physics at Cambridge in 1980. He then held a postdoctoral position with the Physics Department, University of Texas at Austin, and the Institute for Theoretical Physics, Santa Barbara. He then became a research scientists at the Artificial Intelligence Laboratory at MIT (1982-1986) and followed this with a faculty position in the Division of Applied Sciences at Harvard (1986-1995), rising to the position of associate professor. From 1995-2002 he worked as a senior scientist at the Smith-Kettlewell Eye Research Institute in San Francisco. From 2002-2016 he was a full professor in the Department of Statistics at UCLA with joint appointments in Psychology, Computer Science, and Psychiatry. In 2016 he became a Bloomberg Distinguished Professor in Cognitive Science and Computer Science at Johns Hopkins University. He has won a Marr prize, a Helmholtz prize, and is a Fellow of IEEE.