[Lisa_seminaires] [mila-tous] Re: [Tea Talk] Isabela Albuquerque (INRS) Fri February 1 2019 10h30 Mila Auditorium

Pablo Fonseca palefo at gmail.com
Ven 1 Fév 10:29:28 EST 2019


The talk is going to be streamed at
 https://mila.bluejeans.com/4862024040/webrtc
<https://mila.bluejeans.com/4862024040/webrtc>



On Fri, Feb 1, 2019 at 9:57 AM Rim Assouel <rim.assouel at gmail.com> wrote:

> Reminder : this happens in 30 minutes!
>
> On Jan 28, 2019, at 12:36 PM, Rim Assouel <rim.assouel at gmail.com> wrote:
>
> This week we have * Isabela Albuquerque* from * INRS * giving a talk on *
> Multi-objective training of Generative Adversarial Networks with multiple
> discriminators* at * 10h30* in room * Mila Auditorium*.
>
> Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>?
> yes
>
> PS : Slides and recordings of previous talks are available at :
> https://sites.google.com/lisa.iro.umontreal.ca/tea-talk-recordings/home?authuser=0
>
> See you there!
> Pablo, Sai and Rim
>
> *TITLE* Multi-objective training of Generative Adversarial Networks with
> multiple discriminators
>
> *ABSTRACT*
> Recent literature has demonstrated promising results for training
> Generative Adversarial Networks by employing a set of discriminators, in
> contrast to the traditional game involving one generator against a single
> adversary. Such methods perform single-objective optimization on some
> simple consolidation of the losses, e.g. an average. In this work, we
> revisit the multiple-discriminator setting by framing the simultaneous
> minimization of losses provided by different models as a multi-objective
> optimization problem. Specifically, we evaluate the performance of multiple
> gradient descent and the hypervolume maximization algorithm on a number of
> different datasets. Moreover, we argue that the previously proposed methods
> and hypervolume maximization can all be seen as variations of multiple
> gradient descent in which the update direction can be computed efficiently.
> Our results indicate that hypervolume maximization presents a better
> compromise between sample quality and computational cost than previous
> methods.
>
> *BIO*
> Isabela Albuquerque is a PhD candidate at the Institut National de la
> Recherche Scientifique (INRS), Université du Québec. Her main research
> interests are machine learning and cognitive neuroscience. The primary goal
> of her current research is improving the generalization across different
> individuals of electroencephalogram-based mental workload models. Prior to
> joining INRS, she received a Bachelor's degree in Electrical Engineering
> from the Federal University of Pernambuco and a Master's degree in Computer
> Engineering from the University of Pernambuco, both in Recife, Brazil.
>
>
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