[Lisa_seminaires] Reminder 30: [Tea Talk] Devon Hjelm (MSR Montreal + Mila) 10:30am Jean Coutu S1-111

Michael Noukhovitch mnoukhov at gmail.com
Ven 12 Oct 09:59:10 EDT 2018


Reminder: talk in 30 minutes!
Note the different room (in Jean Coutu)

On Tue, Oct 9, 2018, 17:12 Michael Noukhovitch <mnoukhov at gmail.com> wrote:

> 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 streamed <https://mila.bluejeans.com/809027115/webrtc>?
> No
> 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.
>
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