---------- Forwarded message ----------
From: Centre de recherches mathematiques <crm@crm.umontreal.ca>
Date: 2018-04-10 15:18 GMT-04:00
Subject: Conférence de Yoshua Bengio - lundi 16 avril - Programme du 50e anniversaire du CRM - Les mathématique de l'apprentissage machine
To: Activités CRM <activites@crm.umontreal.ca>


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Programme du 50e anniversaire du CRM
Les mathématique de l'apprentissage machine

14 avril - 11 mai 2018 / April 14 - May 11, 2018

50th anniversary program
Mathematics of machine learning

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Conférence inaugurale / Opening keynote lecture

lundi 16 avril / Monday, April 16
11:30 - 12:30
Université de Montréal, Pavillon André-Aisenstadt, salle / room 1360


Yoshua Bengio (Université de Montréal)

"Deep Learning for AI"

There has been rather impressive progress recently with brain-inspired
statistical learning algorithms based on the idea of learning multiple
levels of representation, also known as neural networks or deep
learning. They shine in artificial intelligence tasks involving
perception and generation of sensory data like images or sounds and to
some extent in understanding and generating natural language. We have
proposed new generative models which lead to training frameworks very
different from the traditional maximum likelihood framework, and
borrowing from game theory. Theoretical understanding of the success
of deep learning is work in progress but relies on representation
aspects as well as optimization aspects, which interact. At the heart
is the ability of these learning mechanisms to capitalize on the
compositional nature of the underlying data distributions, meaning
that some functions can be represented exponentially more efficiently
with deep distributed networks compared to approaches like standard
non-parametric methods which lack both depth and distributed
representations. On the optimization side, we now have evidence that
local minima (due to the highly non-convex nature of the training
objective) may not be as much of a problem as thought a few years ago,
and that training with variants of stochastic gradient descent
actually helps to quickly find better-generalizing solutions. Finally,
new interesting questions and answers are arising regarding learning
theory for deep networks, why even very large networks do not
necessarily overfit and how the representation-forming structure of
these networks may give rise to better error bounds which do not
absolutely depend on the iid data hypothesis.

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http://www.crm.math.ca/crm50/

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