[Lisa_teatalk] Soutenance de thèse de Caglar Gulcehre

Michael Noukhovitch mnoukhov at gmail.com
Fri Jun 15 12:40:12 EDT 2018


*Reminder*: this is in 20 minutes!

On Fri, Jun 8, 2018 at 4:27 PM Michael Noukhovitch <mnoukhov at gmail.com>
wrote:

> Forwarding the announcement of Caglar's thesis defense next week
>
> *1pm*
>
> *Friday June 15 *
> *AA1360*
>
> ---------- Forwarded message ---------
> From: Pierre McKenzie <mckenzie at iro.umontreal.ca>
> Date: Fri, Jun 8, 2018 at 3:02 PM
> Subject: Soutenance de thèse de Caglar Gulcehre
> To: <seminaires at iro.umontreal.ca>
>
>
> SOUTENANCE DE THESE DE DOCTORAT
>
> Département d'informatique et de recherche opérationnelle
> Université de Montréal
>
> CANDIDAT: Caglar Gulcehre
>
> TITRE:    Learning and Time: on Using Memory and Curricula for Natural
> Language Understanding
>
> DATE:     vendredi 15 juin 2018
> HEURE:    13:00
> ENDROIT:  Local 1360
>            Pavillon André-Aisenstadt
>            Université de Montréal
>
> RÉSUMÉ:
>
> In this thesis, I present some of the steps that we took towards
> advancing natural language understanding and learning long-term
> dependencies. The goal of those advancements is to keep us on the path
> to develop better artificial intelligence algorithms by using deep
> learning based architectures. Deep-learning architectures have a
> profound effect on various language understanding applications such as
> summarization, machine translation, language modeling and image caption
> generation. I will be summarizing five different papers that we have
> written during my Ph.D
>
> In our first article, we propose a novel method to utilize the abundant
> amount of available monolingual data for training neural machine
> translation models. We have accomplished this goal by training a long
> short-term memory (LSTM) language model on a large monolingual corpus
> first and then fusing the outputs or the hidden representations of the
> LSTM language model with the decoder of the neural machine translation
> model which is trained end to end using an attention mechanism.
>
> In our second paper, we propose an approach to address the problem of
> rare words in general for natural language processing tasks. Our
> approach augments the encoder-decoder architecture with attention model
> by replacing the softmax layer with our proposed pointer-softmax layer
> that defines pointers to the source sentences when the decoder predicts.
>
> In our third paper, we propose two new approaches to learn alignments in
> a sequence to sequence model. Our model addresses the difficulty of
> learning alignments between the source and the target context that
> arises when the source context is very long.
>
> In "Dynamic Neural Turing Machine with Soft and Hard Addressing
> Schemes," we propose a new approach for augmenting neural networks with
> an explicit memory mechanism. Our model achieves promising results on
> question answering and algorithmic tasks.
>
> Finally I will conclude with our "Noisy Activation Functions" paper, in
> which we propose a novel activation function that makes the activations
> are stochastic by injecting the noise to them.
>
> JURY:
>
> Président-rapporteur: Pierre McKenzie (DIRO)
> Directeur de recherche: Yoshua Bengio (DIRO)
> Membre du jury: Simon Julien-Lacoste (DIRO)
> Examinateur externe: Christopher Manning (Stanford)
>
> Bienvenue à tous. Welcome to all. la présentation sera en anglais.
>
>
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