November 2014 - Lisa_seminaires - mailman.iro.umontreal.ca

Schedule of Talks before and after NIPS 2014
by Kyung Hyun Cho 15 Dec '14

15 Dec '14

Dear all, We will have a number of visitors at the lab before and after NIPS 2014. Many of the talks will be interesting to many of the lab members, and I would like to recommend all to be at all of the talks. Also, I'd like to warmly welcome anyone on this mailing list who's not at the LISA lab to attend the talks. Most of the talks will be held at AA3195 (Pavillon André-Aisenstadt, 2920 Chemin de la tour, Montréal). Please, check back frequently for the locations of the talks on http://lisa.iro.umontreal.ca/seminars_en.html. There may be a few more talks added in a next few days, so again, please check the schedule online frequently. Best, - Cho *= Schedule = * + *3 Dec 2014* (Wed) - 13.30 - 14.30 <A Multiplicative Model for Learning Distributed Text-Based Attribute Representations> by Ryan Kiros (University of Toronto) + *4 Dec 2014* (Thu) - 10.30 - 11.30 <What if computer can perceive the world like we do?> by Xinyi Chen, Peng (Eric) Ding (DeepGlint) - 13.30 - 14.30 <Learning Compact Yet Rich Models for Text> by Sarath Chandar (IBM India) + *5 Dec 2014* (Fri) - 10.30 - 11.30 <Joint Training of Deep Autoencoders> by Dr. Yingbo Zhou (University at Buffalo, The State University of New York) - 13.30 - 14.30 <How Watson Learns Superhuman Jeopardy! Strategies> by Dr. Gerald Tesauro (IBM T. J. Watson Research Center) + *15 Dec 2014* (Mon) - 13.30 - 14.30 <TBD> by Prof. Yung-Kyun Noh (KAIST) + *16 Dec 2014* (Tue) - 13.30 - 14.30 <General Stochastic Networks for Classification> by Matthias Zohrer (TU Graz)

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Talk 5 Dec Fri @13.30 AA3195 by Dr. Gerald Tesauro
by Kyung Hyun Cho 21 Nov '14

21 Nov '14

Dear all, This is an early reminder of the talk by Dr. Gerald Tesauro from IBM Research Center on *5 Dec *(Friday) starting from 13.30 at AA3195. He will tell us about the exciting story behind how Watson beat human competitors on Jeopardy!. Best, - Cho P.S. From now on, you can check the schedules of the future (and past) seminars at http://lisa.iro.umontreal.ca/seminars_en.html. The same page in French will appear soon. === - Speaker: Dr. Gerald Tesauro (IBM TJ Watson Research Center) - Date/Time: 13.30 - 14.30, 5 Dec - Place: AA3195 - Title: How Watson Learns Superhuman Jeopardy! Strategies - Abstract: Major advances in Question Answering technology were needed for Watson to play "Jeopardy!" at championship level -- the show requires rapid-fire answers to challenging natural language questions, broad general knowledge, high precision, and accurate confidence estimates. In addition, Jeopardy! features four types of decision making carrying great strategic importance: (1) selecting the next clue when in control of the board; (2) deciding whether to attempt to buzz in; (3) wagering on Daily Doubles; (4) wagering in Final Jeopardy. This talk describes how Watson makes the above decisions using innovative quantitative methods that, in principle, maximize Watson's overall winning chances. We first describe our development of faithful simulation models of human contestants and the Jeopardy! game environment. We then present specific learning/optimization methods used in each strategy algorithm: these methods span a range of popular AI research topics, including Bayesian inference, game theory, Dynamic Programming, Reinforcement Learning, and real-time "rollouts." Application of these methods yielded superhuman game strategies for Watson that significantly enhanced its overall competitive record. Joint work with David Gondek, Jon Lenchner, James Fan and John Prager. - Speaker Bio: Gerald Tesauro is a Research Staff Member at IBM's TJ Watson Research Center. He is best known for creating TD-Gammon, a self-teaching neural network that learned to play backgammon at human world championship level. He has also worked on theoretical and applied machine learning in a wide variety of other settings, including multi-agent learning, dimensionality reduction, computer virus recognition, computer chess (Deep Blue), intelligent e-commerce agents and autonomic computing. Dr. Tesauro serves on the NIPS Foundation Board of Directors, and was recently elected AAAI Fellow.

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Talk 21 Nov Fri @15.00 AA3195 by Dr. John Hershey
by Kyung Hyun Cho 21 Nov '14

21 Nov '14

Dear all, Dr. John Hershey (Mistubishi Electric Research Labs, US) will tell us about the connection between model-free approaches (e.g. deep neural networks) and model-based approaches (e.g. probabilistic graphical models) and how we can utilize this connection. His talk will start from 15.00 *today* (21 Nov) at the usual place AA3195. Hope to see many of you there! - Cho === - Speaker: Dr. John Hershey (Mistubishi Electric Research Labs) - Date/Time: 15.00 - 16.00, 21 Nov - Place: AA3195 - Title: Deep Unfolding: Infusing Deep Architectures with Generative Model Inference - Abstract: Model-based methods and deep neural networks have both been tremendously successful paradigms in machine learning. In model-based methods, problem domain knowledge can be built into the constraints of the model, typically at the expense of difficulties during inference. In contrast, deterministic deep neural networks are constructed in such a way that inference is straightforward, but their architectures are rather generic and it can be unclear how to incorporate problem domain knowledge. This work aims to obtain the advantages of both approaches. To do so, we start with a model-based approach and unfold the iterations of its inference method to form a layer-wise structure. We then decouple the model parameters across layers to increase the network's learning capacity. This results in novel neural-network-like architectures that incorporate our model-based constraints, but can be trained discriminatively to perform fast and accurate inference. We show how this framework can be applied to a non-negative matrix factorization model to obtain a new kind of non-negative deep neural network, that can be trained using a multiplicative backpropagation-style update algorithm. We present speech enhancement experiments showing that our approach is competitive with conventional neural networks despite using far fewer parameters. - Bio: John Hershey is a researcher at Mistubishi Electric Research Labs (MERL), in Cambridge, MA since 2010. Prior to that, John spent 5 years as a researcher at IBM's Watson Research Center in New York, in the Speech Algorithms and Engines group, and one year as visiting researcher in the speech group at Microsoft Research, in Redmond, WA. His obtained his Ph.D. at the University of California, San Diego. He is currently working on machine learning for signal enhancement and separation, speech recognition, language processing, and adaptive user interfaces.

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Fwd: [Lisa_labo] Talk 21 Nov Fri @15.00 AA3195 by Dr. John Hershey
by Frédéric Bastien 21 Nov '14

21 Nov '14

I forward this as there is an email issue. Fred ---------- Forwarded message ---------- From: Kyung Hyun Cho <cho.k.hyun(a)gmail.com> Date: Fri, Nov 21, 2014 at 11:38 AM Subject: Re: [Lisa_labo] Talk 21 Nov Fri @15.00 AA3195 by Dr. John Hershey To: lisa_labo Labo <lisa_labo(a)iro.umontreal.ca>, " lisa_teatalk(a)iro.umontreal.ca" <lisa_teatalk(a)iro.umontreal.ca>, lisa_seminaires(a)iro.umontreal.ca Cc: John Hershey <hershey(a)merl.com> Dear all, It's a reminder that we have a talk by Dr. John Hershey *today* at 15.00. Hope to see many of you there! - K On Sun, Nov 16, 2014 at 6:00 PM, Kyung Hyun Cho <cho.k.hyun(a)gmail.com> wrote: > Dear all, > > Dr. John Hershey (Mistubishi Electric Research Labs, US) will tell us > about the connection between model-free approaches (e.g. deep neural > networks) and model-based approaches (e.g. probabilistic graphical models) > and how we can utilize this connection. His talk will start from 15.00 next > Friday (21 Nov) at the usual place AA3195. > > Hope to see many of you there! > - Cho > > === > - Speaker: Dr. John Hershey (Mistubishi Electric Research Labs) > - Date/Time: 15.00 - 16.00, 21 Nov > - Place: AA3195 > - Title: Deep Unfolding: Infusing Deep Architectures with Generative > Model Inference > - Abstract: > > Model-based methods and deep neural networks have both been tremendously > successful paradigms in machine learning. In model-based methods, problem > domain knowledge can be built into the constraints of the model, typically > at the expense of difficulties during inference. In contrast, deterministic > deep neural networks are constructed in such a way that inference is > straightforward, but their architectures are rather generic and it can be > unclear how to incorporate problem domain knowledge. This work aims to > obtain the advantages of both approaches. To do so, we start with a > model-based approach and unfold the iterations of its inference method to > form a layer-wise structure. We then decouple the model parameters across > layers to increase the network's learning capacity. This results in novel > neural-network-like architectures that incorporate our model-based > constraints, but can be trained discriminatively to perform fast and > accurate inference. We show how this framework can be applied to a > non-negative matrix factorization model to obtain a new kind of > non-negative deep neural network, that can be trained using a > multiplicative backpropagation-style update algorithm. We present speech > enhancement experiments showing that our approach is competitive with > conventional neural networks despite using far fewer parameters. > > - Bio: > John Hershey is a researcher at Mistubishi Electric Research Labs (MERL), > in Cambridge, MA since 2010. Prior to that, John spent 5 years as a > researcher at IBM's Watson Research Center in New York, in the Speech > Algorithms and Engines group, and one year as visiting researcher in the > speech group at Microsoft Research, in Redmond, WA. His obtained his > Ph.D. at the University of California, San Diego. He is currently working > on machine learning for signal enhancement and separation, speech > recognition, language processing, and adaptive user interfaces. > > > > > _______________________________________________ Lisa_labo mailing list Lisa_labo(a)iro.umontreal.ca https://webmail.iro.umontreal.ca/mailman/listinfo/lisa_labo

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