Lisa_seminaires Janvier 2019

lisa_seminaires@iro.umontreal.ca

3 participants
4 discussions

[Tea Talk] Isabela Albuquerque (INRS) Fri February 1 2019 10h30 Mila Auditorium
by Rim Assouel 01 Fév '19

01 Fév '19

This week we have Isabela Albuquerque from INRS giving a talk on Multi-objective training of Generative Adversarial Networks with multiple discriminators at 10h30 in room Mila Auditorium. Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? yes PS : Slides and recordings of previous talks are available at :https://sites.google.com/lisa.iro.umontreal.ca/tea-talk-recordings/home?authuser=0 <https://sites.google.com/lisa.iro.umontreal.ca/tea-talk-recordings/home?aut…> See you there! Pablo, Sai and Rim TITLE Multi-objective training of Generative Adversarial Networks with multiple discriminators ABSTRACT Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involving one generator against a single adversary. Such methods perform single-objective optimization on some simple consolidation of the losses, e.g. an average. In this work, we revisit the multiple-discriminator setting by framing the simultaneous minimization of losses provided by different models as a multi-objective optimization problem. Specifically, we evaluate the performance of multiple gradient descent and the hypervolume maximization algorithm on a number of different datasets. Moreover, we argue that the previously proposed methods and hypervolume maximization can all be seen as variations of multiple gradient descent in which the update direction can be computed efficiently. Our results indicate that hypervolume maximization presents a better compromise between sample quality and computational cost than previous methods. BIO Isabela Albuquerque is a PhD candidate at the Institut National de la Recherche Scientifique (INRS), Université du Québec. Her main research interests are machine learning and cognitive neuroscience. The primary goal of her current research is improving the generalization across different individuals of electroencephalogram-based mental workload models. Prior to joining INRS, she received a Bachelor's degree in Electrical Engineering from the Federal University of Pernambuco and a Master's degree in Computer Engineering from the University of Pernambuco, both in Recife, Brazil.

2 2

[Tea Talk] Hugo Larochelle (Mila + Brain) Fri January 25 2019 10h30 Mila Auditorium
by Rim Assouel 25 Jan '19

25 Jan '19

This week we have Hugo Larochelle from Mila + Brain giving a talk on (Part II) Few-Shot Learning with Meta-Learning: Progress Made and Challenges Ahead at 10h30 in room Mila Auditorium. Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? yes Feel like learning to learn again ? Hugo will teach you another way, this Friday, one shot at a time ;) See you there! Pablo, Sai and Rim Tea Talks website : https://sites.google.com/lisa.iro.umontreal.ca/tea-talk-recordings <https://sites.google.com/lisa.iro.umontreal.ca/tea-talk-recordings> TITLE (Part II) Few-Shot Learning with Meta-Learning: Progress Made and Challenges Ahead ABSTRACT A lot of the recent progress on many AI tasks was enable in part by the availability of large quantities of labeled data. Yet, humans are able to learn concepts from as little as a handful of examples. Meta-learning is a very promising framework for addressing the problem of generalizing from small amounts of data, known as few-shot learning. In meta-learning, our model is itself a learning algorithm: it takes as input a training set and outputs a classifier. For few-shot learning, it is (meta-)trained directly to produce classifiers with good generalization performance for problems with very little labeled data. In this talk, I'll present an overview of the recent research that has made exciting progress on this topic (including my own) and will discuss the challenges as well as research opportunities that remain. BIO Hugo Larochelle is Research Scientist at Google Brain and lead of the Montreal Google Brain team. He is also a member of Yoshua Bengio's Mila and an Adjunct Professor at the Université de Montréal. Previously, he was Associate Professor at the University of Sherbrooke. He also co-founded Whetlab, which was acquired in 2015 by Twitter, where he then worked as a Research Scientist in the Twitter Cortex group. From 2009 to 2011, he was also a member of the machine learning group at the University of Toronto, as a postdoctoral fellow under the supervision of Geoffrey Hinton. He obtained his Ph.D. at the Université de Montréal, under the supervision of Yoshua Bengio. He has the best hair in machine learning academia (not his words). Finally, he has a popular online course on deep learning and neural networks, freely accessible on YouTube.

2 1

[No Tea Talk][ICML deadline]
by Rim Assouel 14 Jan '19

14 Jan '19

There will be no Tea Talk this week because of the ICML deadline coming soon. Next talk will be on the 25th for Hugo Larochelle’s part 2 on Few-Shot Learning with Meta-Learning. See you next week, Pablo, Sai and Rim

1 0

[Tea Talk] [New Website]Maxime Chevalier-Boisvert (Mila) Fri January 11 2019 10h30 Mila Auditorium
by Rim Assouel 11 Jan '19

11 Jan '19

This week we have Maxime Chevalier-Boisvert from Mila giving a talk on BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning at 10h30 in room Mila Auditorium. Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? yes We are also pleased to share the new « website » where we will keep track of the talks slides/recordings : https://sites.google.com/lisa.iro.umontreal.ca/tea-talk-recordings/home?aut… <https://sites.google.com/lisa.iro.umontreal.ca/tea-talk-recordings/home?aut…> It’s accessible with your Mila google account :) See you there and Happy New Year! Pablo, Sai and Rim TITLE BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning ABSTRACT This talk will present BabyAI, a research project based at Mila with the long-term goal of creating agents which we can communicate with and teach new concepts using natural language. We will begin by discussing why language learning is a hard problem, one to which deep learning doesn't yet have a satisfying answer for. We will then introduce the BabyAI platform, created to study the sample efficiency of grounded language learning in the context of embodied agents and instruction following (see paper accepted at ICLR19, https://openreview.net/forum?id=rJeXCo0cYX). Lastly, we will discuss multiple promising research directions we have identified with the goal of improving the sample efficiency of grounded language learning. BIO Maxime has completed a PhD in computer science at the Université de Montréal in early 2016. She has since worked at Apple as part of the mobile GPU team, and then joined Mila as part of the research engineering staff. At Mila, she has been co-directing the BabyAI project with Dzmitry Bahdanau, and took part in creating the Duckietown Simulator. Her main research interests are robotics, embodied agents and reinforcement learning.

3 3

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

Lisa_seminaires Janvier 2019