[Lisa_seminaires] [Tea Talk] Hugo Larochelle (Mila + Brain) Fri January 25 2019 10h30 Mila Auditorium

Rim Assouel rim.assouel at gmail.com
Lun 21 Jan 11:09:49 EST 2019


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.
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