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

Dzmitry Bahdanau dimabgv at gmail.com
Ven 3 Mar 09:09:33 EST 2017


Hi all,

This is just a reminder, that we have a tea-talk today, at 13:30.

Dima

On Thu, 2 Mar 2017 at 09:44 Dzmitry Bahdanau <dimabgv at gmail.com> wrote:

> 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
>
>
-------------- section suivante --------------
Une pièce jointe HTML a été nettoyée...
URL: http://webmail.iro.umontreal.ca/pipermail/lisa_seminaires/attachments/20170303/e91fc80e/attachment.html 


Plus d'informations sur la liste de diffusion Lisa_seminaires