[Lisa_seminaires] séminaire de Yoshua Bengio dans 2 semaines à McGill

Yoshua Bengio bengioy at iro.UMontreal.CA
Ven 2 Nov 16:15:24 EDT 2007


Bonjour,

Je donne un séminaire pour une audience non-spécialisée (informatique et génie électrique) dans deux 
semaines à McGill, sur la question de l'intelligence artificielle via l'apprentissage machine et des 
problèmes difficiles d'optimisation qui sont impliqués là-dedans.

L'annonce est là: http://www.cse.mcgill.ca/Fall07/YB.pdf

Friday Nov 16 at 14:30 in McConnell Engineering Bldg Room 103

Learning Deep Architectures for AI

Theoretical results in complexity theory of circuits and in non-parametric
statistics strongly suggest that in order to learn the kind of
complicated functions that can represent high-level abstractions  (e.g. in
vision, language, and other AI-level tasks), one may need "deep
architectures", which are composed of multiple levels of non-linear
operations, such as in neural nets with many hidden layers. Searching  the
parameter space of deep architectures appears to be a fundamentally  difficult optimization task,
so approximate numerical optimization schemes are called for.
Learning algorithms such as those for Deep Belief Networks have  recently been proposed
which make a dent in this difficult optimization task, beating the  state-of-the-art
in certain areas. This talk discusses the motivations and principles  regarding
learning algorithms for deep architectures and in particular for  those based
on unsupervised learning such as Deep Belief Networks, using as building
blocks single-layer models such as Restricted Boltzmann Machines.


-- 
Yoshua Bengio


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