[Lisa_seminaires] [Tea Talk] Yoshua Bengio (Mila) Fri 12 April 2019 10h30 Mila Auditorium

Rim Assouel rim.assouel at gmail.com
Ven 12 Avr 09:59:53 EDT 2019


Reminder that this happens in 30 minutes :)

Le lundi 8 avril 2019, Rim Assouel <rim.assouel at 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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