[Lisa_seminaires] [TeaTalk] Rémi Leblond & Jean-Baptiste Alayrac, July 7, AA6214, 13:45

Dzmitry Bahdanau dimabgv at gmail.com
Ven 7 Juil 12:14:45 EDT 2017


Kind reminder - it's today!

On Wed, Jul 5, 2017, 12:06 Dzmitry Bahdanau <dimabgv at gmail.com> wrote:

> Hi all,
>
> For those of wondering if there will be a tea-talk this Friday I have good
> news - the answer is yes! Our next speakers will be Rémi Leblond and
> Jean-Baptiste Alayrac, both PhD students at INRIA, working with Simon
> Lacoste-Julien. Please come to AA6214 on July 7 at 13:45!
>
> Please find more information below:
>
> 1) Searnn, Rémi Leblond
> *Title:* SeaRNN: Training RNNs with Global-Local Losses
>
> *Abstract:*
> We propose SEARNN, a novel training algorithm for recurrent neural
> networks (RNNs) inspired by the “learning to search” (L2S) approach to
> structured prediction. RNNs have been widely successful in structured
> prediction applications such as machine translation or parsing, and are
> commonly trained using maximum likelihood estimation (MLE). Unfortunately,
> this training loss is not always an appropriate surrogate for the test
> error: by only maximizing the ground truth probability, it fails to exploit
> the wealth of information offered by structured losses. Further, it
> introduces discrepancies between training and predicting (such as exposure
> bias) that may hurt test performance. Instead, SEARNN leverages test-alike
> search space exploration to introduce global-local losses that are closer
> to the test error. We demonstrate improved performance over MLE on three
> different tasks: OCR, spelling correction and text chunking. Finally, we
> propose a subsampling strategy to enable SEARNN to scale to large
> vocabulary sizes.
>
> *Bio:*
> Rémi is a second year Ph.D. student at INRIA in the SIERRA team under the
> supervision of Simon Lacoste-Julien <http://www.di.ens.fr/~slacoste/>.
> He's interested in very large scale optimization for new complex machine
> learning models. His current focus is on parallel and distributed
> algorithms and structured prediction using neural networks.
>
> After graduating from Ecole Polytechnique <https://www.polytechnique.edu/>
>  and Corps des Mines (French MPA), he worked in the Big Data field for
> three years, as a Data Scientist at SpotRight <http://spotright.com/> in
> the US (2011-2012) and as a Data Engineer at the Ministry of Defense
> (2013-2015).
>
>
> 2) Joint discovery of objects and manipulation actions, JB Alayrac
>
> *Title:* Joint discovery of objects and manipulation actions
>
> *Abstract:*
> Many human activities involve object manipulations aiming to modify the
> object state. Examples of common state changes include full/empty bottle,
> open/closed door, and attached/detached car wheel. In this work, we seek to
> automatically discover the states of objects and the associated
> manipulation actions. Given a set of videos for a particular task, we
> propose a joint model that learns to identify object states and to localize
> state-modifying actions. Our model is formulated as a discriminative
> clustering cost with constraints. We assume a consistent temporal order for
> the changes in object states and manipulation actions, and introduce new
> optimization techniques to learn model parameters without additional
> supervision. We demonstrate successful discovery of seven manipulation
> actions and corresponding object states on a new dataset of videos
> depicting real-life object manipulations. We show that our joint
> formulation results in an improvement of object state discovery by action
> recognition and vice versa.
>
> *Bio:*
> Jean-Baptiste Alayrac received the MS degree in computer science in Ecole
> Normale Superieure (ENS), in Paris in 2014. He is currently working toward
> the PhD degree in the research teams WILLOW and SIERRA at INRIA Paris under
> the supervision of Josef Sivic, Ivan Laptev and Simon Lacoste-Julien. His
> research focuses on structured prediction from vision and natural
> language.
>
> Dima
>
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