[Lisa_seminaires] [TeaTalk] Zhouhan Lin & Kundan Kumar, March 3, 13:30, AA6214

Dzmitry Bahdanau dimabgv at gmail.com
Jeu 2 Mar 09:44:58 EST 2017


Hi all,

An update: we will have two presentations, not just one! Our second speaker
will be *Kundan Kumar* who is currently a visiting student in MILA. Please
find more information below:

*Title 2: *Sample RNN: An Unconditional End-to-End Neural Audio Generation
Model

*Abstract: *In this paper, we propose a novel model for unconditional audio
generation based on generating one audio sample at a time. We show that our
model, which profits from combining memory-less modules, namely
autoregressive multilayer perceptrons, and stateful recurrent neural
networks in a hierarchical structure is able to capture underlying sources
of variations in the temporal sequences over very long time spans, on three
datasets of different nature. Human evaluation on the generated samples
indicate that our model is preferred over competing models. We also show
how each component of the model contributes to the exhibited performance.

Dima

On Wed, 1 Mar 2017 at 11:40 Dzmitry Bahdanau <dimabgv at gmail.com> wrote:

> Hi all,
>
> Our next speaker is *Zhouhan Lin*, a PhD student from MILA. The time and
> place are usual: March 3, at 1:30pm, room AA6214. Home to see many of you
> there!
>
> *Title: *A Structured Self-Attentive Sentence Embedding
>
> *Abstract: *This paper proposes a new model for extracting an
> interpretable sentence embedding by introducing self-attention. Instead of
> using a vector, we use a 2-D matrix to represent the embedding, with each
> row of the matrix attending on a different part of the sentence. We also
> propose a self-attention mechanism and a special regularization term for
> the model. As a side effect, the embedding comes with an easy way of
> visualizing what specific parts of the sentence are encoded into the
> embedding. We evaluate our model on 3 different tasks: author profiling,
> sentiment classification and textual entailment. Results show that our
> model yields a significant performance gain compared to other sentence
> embedding methods in all of the 3 tasks
>
> Dima
>
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