[Lisa_seminaires] [TeaTalk] Rémi Leblond & Jean-Baptiste Alayrac, July 7, AA3195 (CHANGED), 13:45

Dzmitry Bahdanau dimabgv at gmail.com
Ven 7 Juil 13:22:12 EDT 2017


The room is changed to AA3195. Sorry for the inconvenience!

On Fri, Jul 7, 2017, 12:14 Dzmitry Bahdanau <dimabgv at gmail.com> wrote:

> 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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