This week we have our very own
Devon Hjelm from
MSR Montreal x Mila giving a talk on
Friday October 12 2018 at
10:30 in room
Jean Coutu S1-111
Will this talk be recorded? Yes
Maximize your own information and come learn at this talk!
Michael
TITLE Learning representations with Deep InfoMax
KEYWORDS representation learning, unsupervised learning, adversarial learning
ABSTRACT
In this work, we perform unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality of the input to the objective can greatly influence a representation’s suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and competes with fully-supervised learning on several classification tasks. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation-learning objectives for specific end-goals (
https://arxiv.org/abs/1808.06670)
BIO
Devon Hjelm is a researcher at Microsoft Research Montreal and an Adjunct Professor at MILA. He did his postdoc at MILA where he focused on adversarial learning and generative models. His current research focuses on using mutual information estimation objectives in representation learning for applications in computer vision, natural language, and RL.