The Machine Learning Center at Georgia Tech presents a seminar by Hugo Larochelle from Google. The event will be held in the Marcus Nanotechnology Building, Rooms 1116-1118, from 12:15-1:15 p.m. and is open to the public.
For scheduling information, please contact Dhruv Batra at firstname.lastname@example.org
A lot of the recent progress on many AI tasks enabled 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 input as 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.
Hugo Larochelle is a 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 an Associate Professor at the University of Sherbrooke.
Larochelle 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. Finally, he has a popular online course on deep learning and neural networks, freely accessible on YouTube.