Lisa_seminaires Novembre 2016

lisa_seminaires@iro.umontreal.ca

5 participants
4 discussions

Fwd: Séminaire machine learning par Prof Melko
by Yoshua Bengio 30 Nov '16

30 Nov '16

Interesting upcoming talk, Dec. 12, on machine learning for astronomy. ---------- Message transféré ---------- De : Witczak-Krempa William <w.witczak-krempa(a)umontreal.ca> Date : 30 novembre 2016 à 13:49 Objet : Séminaire machine learning par Prof Melko À : "bengioy(a)iro.umontreal.ca" <bengioy(a)iro.umontreal.ca>, Bengio Yoshua < yoshua.bengio(a)umontreal.ca> Cc : Lewis Laurent J <laurent.lewis(a)umontreal.ca> Bonjour Yoshua, CC Laurent Lewis, Il y aura un séminaire donné par Roger Melko (U. of Waterloo) intitulé "Machine Learning the Many-Body Problem” le 12 décembre à 11:30am: http://www.calendrier.umontreal.ca/detail/717846-machine-learning-the-many- body-problem-roger-melko-u-of-waterloo Je me demandais si serais possible de diffuser l’information au près des gens en informatique qui seraient potentiellement intéressés? Merci! Cordialement, William —— Professeur Adjoint | Assistant Professor Canada Research Chair Université de Montréal Pavillion Roger-Gaudry, 2900 boul. Édouard-Montpetit Bureau | office: A-405 T: 514-343-6669 W: http://physique.umontreal.ca/wwk

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[Talk] Prof. Wulfram Gerstner on "Synaptic Plasticity to Learning Rules: an approach of theoretical neuroscience"
by Junyoung Chung 23 Nov '16

23 Nov '16

Hi everyone, on next Tuesday, we will have a guest tutorial on neuroscience by professor Wulfram Gerstner who is visiting our lab for two weeks. Looking forward to see many of you there! Who: Wulfram Gerstner When: Nov. 22 (Tuesday) 3PM-5PM Where: AA6214 Title: Synaptic Plasticity to Learning Rules: an approach of theoretical neuroscience Abstract: In this tutorial I will show what type of constraints experimental data set for biologically plausible learning rules and why inside these constraints we (as theoretical neuroscientists) have a lot of freedom to think about learning rules that are functionally relevant in learning paradigms. Taking examples from unsupervised learning, reinforcement learning, and surprise modulated learning I will point out potential links (and differences) to machine learning approaches. Bio: Wulfram Gerstner is Director of the Laboratory of Computational Neuroscience LCN at the EPFL. He studied physics at the universities of Tubingen and Munich and received a PhD from the Technical University of Munich. His research in computational neuroscience concentrates on models of spiking neurons and spike-timing dependent plasticity, on the problem of neuronal coding in single neurons and populations, as well as on the role of spatial representation for navigation of rat-like autonomous agents. He currently has a joint appointment at the School of Life Sciences and the School of Computer and Communications Sciences at the EPFL. He teaches courses for Physicists, Computer Scientists, Mathematicians, and Life Scientists. Best, --Junyoung

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Dialogue-based learning talk by Jason Weston
by Tegan Maharaj 17 Nov '16

17 Nov '16

Hi everyone, The tea talk this week will be Jason Weston's talk about dialogue. Who: Jason Weston When: Nov. 17 (Today) 3:00PM Where: PAA 1207 Title: Dialog-based Language Learning Abstract: We study how to learn to use language via dialog-based language learning, where supervision is given naturally and implicitly in the response of the dialog partner during the conversation. Experiments are in two domains: the bAbI dataset of (Weston et al., 2015) and the larger movieQA dataset from (Dodge et al., 2015). We evaluate a set of baseline learning strategies on these tasks, and show that a novel model incorporating predictive lookahead is a promising approach for learning from a teacher's response. In particular, a surprising result is that it can learn to answer questions correctly without any reward-based supervision at all. This is joint work with Jiwei Li, Alex Miller, Sumit Chopra and Marc'Aurelio Ranzato Hope to see you there! Cheers, Tegan

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Fwd: [Stats] McGill Statistics Seminar Series - THIS FRIDAY
by Yoshua Bengio 16 Nov '16

16 Nov '16

---------- Forwarded message ---------- From: Yi Yang <yi.yang6(a)mcgill.ca> Date: 2016-11-15 11:14 GMT-05:00 Subject: [Stats] McGill Statistics Seminar Series - THIS FRIDAY To: stats.students(a)math.mcgill.ca, stats(a)math.mcgill.ca, EBOH Secretary < secretary.eboh(a)mcgill.ca>, "Jackie Castreje, Ms." <jackie.castreje(a)mcgill.ca>, Yoshua Bengio <yoshua.umontreal(a)gmail.com> *McGill Statistics Seminar Series* Friday, *November 18* 2016, 15:30 – 16:30 Room 1205, Burnside Hall, 805 Sherbrooke West ______________________________________________________ * Progress in Theoretical Understanding of Deep Learning* Yoshua Bengio, PhD Professor, University of Montreal Deep learning has arisen around 2006 as a renewal of neural networks research allowing such models to have more layers. Theoretical investigations have shown that functions obtained as deep compositions of simpler functions (which includes both deep and recurrent nets) can express highly varying functions (with many ups and downs and different input regions that can be distinguished) much more efficiently (with fewer parameters) than otherwise, under a prior which seems to work well for artificial intelligence tasks. Empirical work in a variety of applications has demonstrated that, when well trained, such deep architectures can be highly successful, remarkably breaking through previous state-of-the-art in many areas, including speech recognition, object recognition, language models, machine translation and transfer learning. Although neural networks have long been considered lacking in theory and much remains to be done, theoretical advances have been made and will be discussed, to support distributed representations, depth of representation, the non-convexity of the training objective, and the probabilistic interpretation of learning algorithms (especially of the auto-encoder type, which were lacking one). The talk will focus on the intuitions behind these theoretical results. Organized by the McGill Statistics Group. Seminar website: http://www.math.mcgill.ca/node/5358 -- Yi Yang Assistant Professor of Statistics Department of Mathematics and Statistics McGill University http://www.math.mcgill.ca/yyang/

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Lisa_seminaires Novembre 2016