Using Human Behavior and Brain Activity to Guide Machine Learning
Dr. Walter J. Scheirer, University of Notre Dame
Abstract: For many problems in computer vision, human learners are considerably better than machines. Human possess highly accurate internal recognition and learning mechanisms that are not yet understood, and they frequently have access to more extensive training data through a lifetime of unbiased experience with the visual world. In this talk, an advanced online psychometric testing platform will be described that makes new kinds of annotation data available for learning. Subsequently, a new technique for harnessing these new kinds of information - "perceptual annotations" - for support vector machines will be introduced. A key intuition for this approach is that while it may remain infeasible todramatically increase the amount of data and high-quality labels available for the training of a given system, measuring the exemplar-by-exemplar difficulty and pattern of errors of human annotations can provide important information for regularizing the solution of the system at hand. The talk will go on to describe a related methodology that makes use of fMRI recordings of the human brain as a guide for machine learning algorithms. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.
Biography: Walter J. Scheirer, Ph.D. is an Assistant Professor in the Department of Computer Science and Engineering at the University of Notre Dame. Previously, he was a postdoctoral fellow at Harvard University, with affiliation in the School of Engineering and Applied Science, Dept. of Molecular and Cellular Biology and Center for Brain Science, and the director of research & development at Securics, Inc., an early stage company producing innovative computer vision-based solutions. He received his Ph.D. from the University of Colorado and his M.S. and B.A. degrees from Lehigh University. Dr. Scheirer has extensive experience in the areas of computer vision, machine learning and image processing. His overreaching research interest is the fundamental problem of recognition, including the representations and algorithms supporting solutions to it.