[Lisa_seminaires] [TeaTalk] Talks by Çağlar Gülçehre and Vincent Dumoulin

Dzmitry Bahdanau dimabgv at gmail.com
Jeu 26 Jan 17:01:21 EST 2017


Hi all,

This is a kind reminder about tomorrow's presentation.

Best,
Dima

On Mon, 23 Jan 2017 at 12:14 Dzmitry Bahdanau <dimabgv at gmail.com> wrote:

> Hi all,
>
> Next Friday (27.01) at 1:30pm, room 6214, we will have not just one, but
> two tea-talk presentations by Çağlar and Vincent, both MILA students.
> Please come in great numbers!
>
> Speaker 1: Çağlar Gülçehre
> Title: TARDIS: an RNN with Wormhole Connections
> Abstract: In this talk, I will discuss a new memory augmented model which
> we have been working on with Sarath and Yoshua since last summer. Basically
> we revisit the idea of memory augmented models and investigate some of
> their aspects theoretically and propose a new model. The presentation will
> be about an ongoing work(although the arXiv paper will be out
> soon). Basically, we revisit the idea of memory augmented models and
> investigate some of their aspects theoretically and propose a new model. In
> this paper, we propose a novel memory augmented neural network model called
> TARDIS (Temporal Automatic Relation Discovery in Sequences). Our model
> (TARDIS) has an RNN controller which modifies an external memory component
> where it can store a selective set of embeddings of its own previous hidden
> states and revisit them as and when needed. In terms of the memory
> structure, our proposed model is similar to Dynamic Neural Turing Machines
> ~(D-NTM), but both read and write operations are simpler and more
> efficient. TARDIS uses discrete addressing for read/write operations which
> helps to substantially reduce the vanishing gradient problem with very long
> sequences. We evaluate our models on different long-term dependency tasks
> and report competitive results in all of them.
>
> Speaker 2: Vincent Dumoulin
> Title: A Learned Representation for Artistic Style
> Abstract: In this work (done while interning at Google Brain and
> submitted to ICLR2017) we investigate the construction of a single,
> scalable deep network that can parsimoniously capture the artistic style of
> a diversity of paintings. We demonstrate that such a network generalizes
> across a diversity of artistic styles by reducing a painting to a point in
> an embedding space. Importantly, this model permits a user to explore new
> painting styles by arbitrarily combining the styles learned from individual
> paintings.
>
> Best,
> Dima
>
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