[Lisa_seminaires] [Tea Talk] Geoff Gordon (MSR Montreal) Fri September 7 2018 10:30 AA3195

Michael Noukhovitch mnoukhov at gmail.com
Mer 5 Sep 12:21:10 EDT 2018


*Correction*, the talk will be streamed:
https://mila.bluejeans.com/809027115/webrtc

On Tue, Sep 4, 2018 at 5:55 PM Michael Noukhovitch <mnoukhov at gmail.com>
wrote:

> This week we are super lucky to have *Geoff Gordon*, Research Director of *
> MSR Montreal * giving a talk on *Fri September 7 2018* at *10:30* in room
> *AA3195*
>
> Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>?
> *No*
>
> Geoff will be available to meet in the afternoon! Sign up here
> <https://calendar.google.com/calendar/selfsched?sstoken=UVBVTVF0X25Nd09LfGRlZmF1bHR8ZWQ5MmNlYWMxODI4OWVkNmUzNGU3OTE4ZDExMGI0YTk>
>
> Knowing that this talk is likely awesome and that you probably come to
> talks given they're awesome, you're probably going to the talk! Good choice!
> 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.
>
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