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

Dzmitry Bahdanau dimabgv at gmail.com
Lun 23 Jan 12:14:51 EST 2017


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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