Reminder: this is in 10 minutes!
On Mon, Feb 26, 2018 at 2:03 PM Michael Noukhovitch mnoukhov@gmail.com wrote:
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:
- 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.
- 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.
- 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]*
