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

Rim Assouel rim.assouel at gmail.com
Mer 24 Avr 17:38:18 EDT 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 at gmail.com> a écrit :

> Was this talk recorded ?
>
> On Apr 12, 2019, at 09:59, Rim Assouel <rim.assouel at gmail.com> wrote:
>
> 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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