[Lisa_seminaires] [DIRO Talk] Devon Hjelm (MILA) Fri Mar 2 10:30AM AA1360

Michael Noukhovitch mnoukhov at gmail.com
Lun 26 Fév 14:03:36 EST 2018


For our last special DIRO talk, we have our very own *Devon Hjelm* giving a
talk on *Friday March 2* at *10:30AM* in room *AA1360*.

This talk has generated a lot of interest, so come take a GANder and judge
for yourself if it's the real deal!
Michael

*TITLE *Research in Generative Adversarial Learning

*KEYWORDS*  Unsupervised learning, generative models, representation
learning, adversarial learning, mutual information

*ABSTRACT*
Since their inception, generative adversarial networks (GANs) have emerged
as a state-of-the-art approach for generating high-dimensional continuous
data. As the field has grown, so has its applications, with the underlying
principles of GANs having been extended into numerous problems in
unsupervised learning.

This talk will cover the basic principles of GANs, as well as briefly
summarize our three recent works that cover different types of unsupervised
problems using these principles:

1) Discrete generation in GANs (Boundary seeking GANs, ICLR 2018): Training
GANs with discrete data (e.g., natural language as realized as a sequence
of character or word tokens) is normally not possible due to being unable
to backprop. We introduce a principled approach for training GANs with
discrete data that draws from likelihood ratio estimation, importance
sampling, and policy gradients.

2) Learning richer representations in bidirectional adversarial models
(GibbsNet, NIPS 2017). Undirected graphical models (e.g., RBMs, DBMs) can
provide richer representations than those available from directed graphical
models (e.g., VAEs). We draw from ideas in undirected graphical models to
formulate an adversarial model that learns a representation that is richer
than competing adversarial models.

3) Neural mutual information estimation (MINE, in review): Mutual
information is notoriously difficult to compute, especially in the
high-dimensional continuous setting. We introduce a general-purpose neural
estimator for mutual information which is scalable, flexible, and
completely trainable via back-prop.


*BIO*
R Devon Hjelm earned his PhD at the University of New Mexico under the
supervision of Vince Calhoun at the Mind Research Network, a research
institute dedicated to neuro-diagnostic discovery. Prior to this, he
acquired a Master’s degree in Physics (with a focus on Quantum Information)
and Linguistics. He joined the Montréal Institute for Learning Algorithms
(MILA) at the University of Montréal in January 2017 as an IVADO
“distinguished researcher” postdoctoral fellow under Yoshua Bengio. There,
his research focus became adversarial learning (GANs), notably applying
ideas from GANs to solving a broader class of unsupervised learning
problems. Ultimately, he is primarily interested in research on training an
agent that is able to reason about the natural world from evidence and
communicate its understanding to humans.

*PHOTO*
*(real sample, not generated)*

*[image: CD2F92BF-5584-473C-BE37-5D616C37B293.png]*
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