[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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