Lisa_seminaires Avril 2017

lisa_seminaires@iro.umontreal.ca

6 participants
5 discussions

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No tea-talk today
by Dzmitry Bahdanau 28 Avr '17

28 Avr '17

Hi all, There is no tea-talk today - blame ICLR for that :) Dima

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[TeaTalk] Michael James, April 21, 13:45, AA5340
by Dzmitry Bahdanau 21 Avr '17

21 Avr '17

Hi all, Our next speaker is Michael James from Cerebras Systems. The talk will take place at *AA5340, April 21, 13:45*. Hope to see many of you there! *Title:* Continuous Propagation and Computer Architectures Specialized for Deep Learning *Abstract: *I will be discussing a novel computing platform designed for training deep neural models, and highlight Continuous Propagation, which is one of the new learning algorithms that it makes possible. Continuous Propagation is a training technique that allows layer-parallelism: training for deep models is parallelized along the depth of the network. The technique is motivated by the continuous differential formulation of learning and has relations to biologically plausible learning. Cerebras Systems is building a next generation system, purpose designed for sparse linear algebra and large stochastic optimization problems. *Bio: *I am a founder and Chief Software Architect at Cerebras Systems, a venture-backed stealth mode startup located in Los Altos, California. Prior to founding Cerebras Systems, I was lead SW Architect at SeaMicro (acquired by AMD for $357 million). I was responsible for the distributed system software that among other things, did placement and work allocation for the more than 16,000 cores in a SeaMicro solution. Post acquisition, I was an AMD Fellow and worked as a hardware and software architect. At AMD, I designed a novel distributed high-availability compute architecture base on cellular automata. Career highlights include: Platform Architect, Kno Inc, where I designed gesture recognition algorithms. Chief Architect, MultiLing Corp, where I designed machine translation software using fuzzy logic. I have designed operating systems, compilers, real-time feedback control systems, signal processing, and recognition systems. I hold patents in computer architecture and distributed algorithms. My alma mater is University of California at Berkeley where I studied Molecular Neurobiology, Mathematics, and Computer Science. Dima

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[Tea-Talk] No tea-talk on April 14
by Dzmitry Bahdanau 11 Avr '17

11 Avr '17

Hi all, This is a reminder that there will be no tea-talk on April 14, since the university will be closed for the Easter break. Enjoy the holidays! Dima

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Fwd: Marc Bellemare talk Wed Apr 12, 10:30am, MC103
by Yoshua Bengio 10 Avr '17

10 Avr '17

---------- Forwarded message ---------- From: Doina Precup <dprecup(a)cs.mcgill.ca> Date: 2017-04-10 15:32 GMT-04:00 Subject: Marc Bellemare talk Wed Apr 12, 10:30am, MC103 To: labrl(a)cs.mcgill.ca Cc: "Marc G. Bellemare" <marcgb(a)gmail.com>, Gen Fried < genevieve.fried(a)mail.mcgill.ca>, Yoshua Bengio <yoshua.umontreal(a)gmail.com> Hi everyone, Marc Bellemare, who has made great contributions to the theory and practice of reinforcement learning (including the ALE environment that we all use & love) will be visiting us Wed. The talk is at 10:30am, MC103. If you want to meet with Marc, please send email to Gen (cc’d), he will be around for the day. Title: The role of density models in reinforcement learning Abstract: Much of the theoretical foundations of reinforcement learning assume, or derive from, a tabular representation. In practical applications, however, the tabular representation is usually impractical and undesirable. The translation to practice therefore typically involves a regression step: a projection of the value function onto a tractable function class, for example a deep network. In performing this regression, we often lose many of the appealing properties of tabular representations, including measures of value uncertainty or the ability to learn from a few examples. In this talk I will argue that a particular kind of probabilistic generative models, density models, allow us to recover the benefits of the tabular representation, without sacrificing generalization. I will first revisit the Compress and Control approach, which uses density models (or in fact any sequential data compression algorithm) to model the value function. As an example, I will demonstrate an agent that learns to play Pong in 13 games. I will subsequently present our recent work on pseudo-counts, showing how to induce intrinsically motivated behaviour from a simple density model, and how this behaviour leads to state-of-the-art exploration in one of the hardest Atari 2600 games, Montezuma's Revenge. Short bio Marc G. Bellemare received his MSc from McGill University and Ph.D. from the University of Alberta, where he investigated the concept of domain-independent agents and led the design of the highly-successful Arcade Learning Environment. His research interests include reinforcement learning, online learning, information theory, lifelong learning, and randomized algorithms. He is currently a Senior Research Scientist at DeepMind. http://www.marcgbellemare.info/static/index.html Best, Doina

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Amir Moravej, April 7, 13:45, Z209
by Junyoung Chung 07 Avr '17

07 Avr '17

Hi all, the next tea-talk will be given by Amir Moravej from BotlerAI. The room will be Z209, and the talk starts at 13:45. Title: AI-Powered Immigration Chatbot About: Immigration Law refers to government policies which regulate the right of entry, and residency right in each country. Immigration Law absorbs features from other closely related areas of law, including Administrative Law, Constitutional Law, Criminal Law, Tax Law etc. The Immigration Process refers to a set oa steps an individual should take in order to obtain a legal status under Immigration Law. Botler is a Chatbot which automates Immigration Process. From a system perspective, the immigration process can be modeled as a two steps process: Eligibility Assessment, and Application Packaging. Botler models Eligibility Assessment as a rule-based system initiative dialogue using a finite-state dialogue manager which is modeled as a graph of action rules. Application Packaging is a three step process: document authenticity, document relevance, and packaging. Document authenticity is modeled using a CNN for image object recognition. Document relevance is modeled using an OCR for content extraction, and a text processing module to determine content relevancy. Packaging is a two step process, filling out required forms, and creating a map of required documents to the actual documents provided by the user. Botlers’ dataset contains the largest Canadian immigration forum, tens of thousands of conversations in the form of email threads between human legal agents and users, more than five millions of Canadian immigration case eligibility assessment results, and thousands of Immigration Law related documents. Bio: Amir graduated as a software engineer in 2009, and came to Montreal to continue his graduate studies at Concordia. He finished his Master's degree in Quality Systems Engineering in 2012 and started working in startups ever since. He contributed to the development of several projects, including a content personalization platform for Relevant AI, a QA system for Toti VT, a relevancy engine for nTerop, a data visualization platform for Retinad VR, and the full stack of Squado app. He founded Botler AI in 2016 after he faced several challenges with him immigration process. Best, --Junyoung

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Lisa_seminaires Avril 2017