Nouvel objet : [mila-tous] Re: [Tea Talk] Yoshua Bengio (Mila) Fri 12 April 2019 10h30 Mila Auditorium

24 Avr 2019


      Yes, you can find the recording on the Mila youtube channel or the tea
talks website!
Cheers,
The Tea Talk Team
Le mercredi 24 avril 2019, Rémi LP remi.lp.17@gmail.com a écrit :
...
Was this talk recorded ?
On Apr 12, 2019, at 09:59, Rim Assouel rim.assouel@gmail.com wrote:
Reminder that this happens in 30 minutes :)
Le lundi 8 avril 2019, Rim Assouel rim.assouel@gmail.com a écrit :
...
This week we have * Yoshua Bengio* from * Mila * giving a talk on *
Meta-transfer learning for factorizing representations and knowledge for AI*
at * 10h30* in room * Mila Auditorium*.
Will this talk be streamed
https://mila.bluejeans.com/4862024040/webrtc? yes
See you there!
The Tea Talk Team
*TITLE* Meta-transfer learning for factorizing representations and
knowledge for AI
*ABSTRACT*
Whereas machine learning theory has focused on generalization to examples
from the same distribution as the training data, better understanding of
the transfer scenarios where the observed distribution changes often in the
lifetime of the learning agent is important, both for robust deployment and
to achieve a more powerful form of generalization which humans seem able to
enjoy and which seem necessary for learning agents. Whereas most machine
learning algorithms and architectures can be traced back to assumptions
about the training distributions, we also need to explore assumptions about
how the observed distribution changes. We propose that sparsity of change
in distribution, when knowledge is represented appropriately, is a good
assumption for this purpose, and we claim that if that assumption is
verified and knowledge represented appropriately, it leads to fast
adaptation to changes in distribution, and thus that the speed of
adaptation to changes in distribution can be used as a meta-objective which
can drive the discovery of knowledge representation compatible with that
assumption. We illustrate these ideas in causal discovery: is some variable
a direct cause of another? and how to map raw data to a representation
space where different dimensions correspond to causal variables for which a
clear causal relationship exists? We propose a large research program in
which this non-stationarity assumption and meta-transfer objective is
combined with other closely related assumptions about the world embodied in
a world model, such as the consciousness prior (the causal graph is
captured by a sparse factor graph) and the assumption that the causal
variables are often those agents can act upon (the independently
controllable factors prior), both of which should be useful for agents
which plan, imagine and try to find explanations for what they observe.
*BIO*
Yoshua Bengio is Full Professor in the computer science and operations
research department at U. Montreal, scientific director of Mila and of
IVADO, Turing Award 2018 recipient, Canada Research Chair in Statistical
Learning Algorithms, as well as a Canada AI CIFAR Chair. He pioneered deep
learning and has been getting the most citations per day in 2018 among all
computer scientists, worldwide. He is officer of the Order of Canada,
member of the Royal Society of Canada, was awarded the Marie-Victorin Prize
and the Radio-Canada Scientist of the year in 2017, and he is a member of
the NeurIPS board and co-founder and general chair for the ICLR conference,
as well as program director of the CIFAR program on Learning in Machines
and Brains. His goal is to contribute to uncover the principles giving rise
to intelligence through learning, as well as favour the development of AI
for the benefit of all.
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Re: [Lisa_seminaires] [Tea Talk] Yoshua Bengio (Mila) Fri 12 April 2019 10h30 Mila Auditorium