[Lisa_seminaires] [Fwd: Predoc oral de James Bergstra, jeudi 21 aout]
Dumitru Erhan
erhandum at iro.umontreal.ca
Lun 18 Aou 17:57:16 EDT 2008
---------------------------- Message original ----------------------------
Objet: Predoc oral de James Bergstra, jeudi 21 aout
De: "Pierre L'Ecuyer" <lecuyer at iro.umontreal.ca>
Date: Ven 15 août 2008 15:40
À: "seminaires" <seminaires at iro.umontreal.ca>
"Pierre L'Ecuyer" <lecuyer at iro.umontreal.ca>
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Bonjour.
Vous êtes tous cordialement invités au predoc oral de
*James Bergstra*,
etudiant au DIRO sous la direction de Yoshua Bengio.
Date: jeudi 21 aout
Heure: 16h30
Salle: 3195, Pav. Aisenstadt
Titre:
Object Recognition and Multiscale prediction with Recurrent Neural Networks
Résumé / Abstract:
Object recognition rates in humans are much higher when subjects are allowed
to look around an image and reflect a little, but shallow feedforward
models are
unable to exhibit this sort of behaviour. Deep models with multiple
layers of
nonlinear processing may be said to reflect a little, but my doctoral work
will
look at strategies for training recurrent neural networks with this capacity.
Deep neural networks are much like recurrent networks: an unfolded recurrent
architecture is a deep network with tied weights, and any deep network can be
transformed into a recurrent architecture by adding hidden units to the
recurrent state. I will to translate recent techniques for learning deep
neural
networks into the domain of recurrent networks. My thesis proposal will
outline
new recurrent models, new training strategies, and a new way to perform
static
classification with temporal models. My doctoral work so far has centered on
the use of physiologically-motivated cell models in artificial neural
networks,
and on techniques for training recurrent networks to learn over short and
medium-term timescales.
--
Pierre L'Ecuyer, Professeur Titulaire
Chaire du Canada en Simulation et Optimisation Stochastique
CIRRELT, GERAD, and DIRO, Université de Montréal, Canada
http://www.iro.umontreal.ca/~lecuyer
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