[Lisa_seminaires] [TeaTalk] Karan Grewal, September 8, 11:00, AA6214 (TIME CHANGED!)

Dzmitry Bahdanau dimabgv at gmail.com
Ven 8 Sep 10:07:11 EDT 2017


A kind reminder that we have a talk today, in less than an hour (at 11:00).

Dima

On Thu, 7 Sep 2017 at 15:57 Dzmitry Bahdanau <dimabgv at gmail.com> wrote:

> Hi all,
>
> Our tea-talk tomorrow has been moved to 11am, same room (AA6214). Hope to
> you see there!
>
> Dima
>
> On Mon, 4 Sep 2017 at 21:02 Dzmitry Bahdanau <dimabgv at gmail.com> wrote:
>
>> Hi all,
>>
>> Our next speaker is Karan Grewal, who is currently an intern at MILA. The
>> talk will take place at *AA6214, at 13:45, September 8*. Hope to see
>> many of you there!
>>
>> *Title: *Variance Regularizing Adversarial Learning
>>
>> *Abstract:* Generative Adversarial Networks have become a breakthrough
>> towards generating synthetic data in recent years, however many problems
>> may arise during training which result in the generator being unable to
>> learn the desired data distribution/manifold. Most notably, the
>> discriminator often overpowers the generator, which leads to a poor
>> training signal and vanishing gradients. We study the effects of the
>> discriminator's unnormalized output distribution on the generator's ability
>> to learn. To combat this problem, we propose Variance Regularizing
>> Adversarial Learning -- regularizing the discriminator's unnormalized
>> output distribution to fit a mixture of gaussians, yielding higher variance
>> and hence a lipschitz-like discriminator function. To do this, we provide
>> two methods: (1) using the KL-divergence as a penalty in the
>> discriminator's loss and (2) playing meta-adversarial games to force the
>> discriminator to fit a MoG. We show that our models are robust in the
>> presence of high training ratios and compare them to other variants of GANs.
>>
>> *Bio:* Karan Grewal is an intern at MILA and a senior undergraduate
>> student at the University of Toronto. He is working on improving and
>> stabilizing generative models with Devon Hjelm and Yoshua Bengio.
>> Previously, he worked on applying machine learning to social contexts.
>>
>> Dima
>>
>
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