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

xavier bouthillier xavier.bouthillier at gmail.com
Ven 7 Sep 14:57:31 EDT 2018


Here is the link of the recording: https://bluejeans.com/s/Gwjyq/

On Wed, Sep 5, 2018 at 12:21 PM Michael Noukhovitch <mnoukhov at gmail.com>
wrote:

> *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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