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

Dzmitry Bahdanau dimabgv at gmail.com
Mer 5 Juil 12:06:36 EDT 2017


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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