Michael
TITLE Neural Networks and Bayes Rule
KEYWORDS Deep Graphical Models, Reasoning with NNs
ABSTRACT Relational or structured reasoning is an important current research challenge. The classical approach to this challenge is a templated graphical model: highly expressive, with well-founded semantics, but (at least naively) difficult to scale up, and difficult to combine with the most effective supervised learning methods. More recently, researchers have designed many different deep network architectures for structured reasoning problems, with almost the flip set of advantages and disadvantages. Can we get the best of both worlds? That is, can we design deep nets that look more like graphical models, or graphical models that look more like deep nets, so that we get a framework that is both practical and "semantic"? This talk will take a look at some progress toward such a hybrid framework.
BIO
Dr. Gordon is the Research Director of Microsoft Research Montreal. He is on leave as a Professor in the Department of Machine Learning at Carnegie Mellon University, where he has also served as Interim Department Head and as Associate Department Head for Education. His research interests include artificial intelligence, statistical machine learning, game theory, multi-robot systems, and planning in probabilistic, adversarial, and general-sum domains. His previous appointments include Visiting Professor at the Stanford Computer Science Department and Principal Scientist at Burning Glass Technologies in San Diego. Dr. Gordon received his B.A. in Computer Science from Cornell University in 1991, and his Ph.D. in Computer Science from Carnegie Mellon University in 1999.