Rockwood Memorial Lecture
Center for Biological and Computational Learning
Computer Science and Artificial Intelligence Laboratory
McGovern Institute for Brain Research
Massachusetts Institute of Technology
4:00PM, Thursday, April 5, 2007
Leichtag Family Biomedical Research Building, University of California, San Diego
Title: "What Should Computer Vision Learn from Neuroscience?"
The problem of learning is one of the main gateways to making intelligent machines and to understanding how the brain works. In this talk I will give a brief overview of recent efforts in my group developing machines that learn for applications such as visual recognition and speech synthesis. I will then review hierarchical feedforward quantitative models of the ventral stream which, heavily constrained by physiology and biophysics, are surprisingly successful in explaining several physiological data and psychophysical results in scene categorization. I will then focus on the limitations of such models for object recognition, pointing to questions about the computational role of attention and about recognition tasks beyond scene classification."
Host: Terry Sejnowski