April 2019 - Lisa_seminaires - mailman.iro.umontreal.ca

[Tea Talk] ICLR Lightning talks (Mila) Fri 3 May 2019 10h30 Mila Auditorium
by Rim Assouel 30 Apr '19

30 Apr '19

This week we have ICLR Lightning talks at 10h30 in the Mila Auditorium. Each signed-up presenter will give a short 5-10 min presentation of his/her accepted work at ICLR ! Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? yes See you there! The Tea Talk Team

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Re: [Lisa_seminaires] [Tea Talk] Yoshua Bengio (Mila) Fri 12 April 2019 10h30 Mila Auditorium
by Rim Assouel 26 Apr '19

26 Apr '19

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(a)gmail.com> a écrit : > Was this talk recorded ? > > On Apr 12, 2019, at 09:59, Rim Assouel <rim.assouel(a)gmail.com> wrote: > > Reminder that this happens in 30 minutes :) > > Le lundi 8 avril 2019, Rim Assouel <rim.assouel(a)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. >> > > -- > You received this message because you are subscribed to the Google Groups > "MILA Tous" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to mila-tous+unsubscribe(a)mila.quebec. > > >

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No Tea Talk this week
by Rim Assouel 22 Apr '19

22 Apr '19

Hi everyone, There is no tea talk this week so that you can enjoy TechAide conference :) Cheers, The Tea Talk Team

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Re: [Lisa_seminaires] [mila-tous] Re: [teatalk-orgs] [Tea Talk] Dhruva Raman (Cambridge) Fri 19 April 2019 10h30 Mila Auditorium
by Pablo Fonseca 19 Apr '19

19 Apr '19

And for those joinig remotely, the streaming link is https://mila.bluejeans.com/4862024040/webrtc Best, Pablo Fonseca On Fri, Apr 19, 2019 at 9:25 AM saikrishna gottipati < saikrishnagv1996(a)gmail.com> wrote: > Gentle reminder, this talk is today at 10.30am > > Cheers > The tea talk team > > On Mon, 15 Apr 2019, 10:08 saikrishna gottipati, < > saikrishnagv1996(a)gmail.com> wrote: > >> This week we have *Dhruva Raman* from *Univ of Cambridge *giving a talk >> on *Fundamental Bounds on Learning Performance in Neural Circuits *at >> *10h30* on *19th April* in *Mila Auditorium*. >> >> If you are interested in* meeting Dhruva*, please book a slot in this google >> sheet >> <https://docs.google.com/spreadsheets/d/13k6e1rCDM1tD__MvxS-UDHMS9L6XsWcTvqs…> >> >> See you there! >> The Tea Talk Team >> >> *TITLE* Fundamental Bounds on Learning Performance in Neural Circuits >> >> *ABSTRACT* >> Biological neural circuits learn in spite of imperfect information on >> task performance and noisy biological components. How can these problems be >> mitigated? We use optimization theory to show how adding apparently >> redundant neurons and connections to a network can improve learning >> performance in the face of imperfect learning rules and corrupted error >> signals. The theory shows how large neural circuits can exploit additional >> connectivity to achieve faster and more precise learning. However, there is >> a limit to the benefit of adding connections. Biologically, synapses >> (connections strengths) are intrinsically unreliable. We show that >> excessive network size eventually outcompetes the benefits to learning >> performance. Consequently, there is an optimal size of network for a given >> task, which we can calculate in specific cases. >> >> *BIO* >> Dhruva did his undergraduate (MMath) at the University of Warwick >> (2008-2012), spent a year at the systems biology doctoral training centre >> at the University of Oxford (2012-2013), and did his PhD in the Control >> Group at the University of Oxford under the supervision of Antonis >> Papachristodoulou (2013-2016). Since 2017 he has been a postdoc in the >> control group at the University of Cambridge under the supervision of >> Timothy O’Leary. >> >> >> > -- > You received this message because you are subscribed to the Google Groups > "MILA Tous" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to mila-tous+unsubscribe(a)mila.quebec. >

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[teatalk-orgs] [Tea Talk] Dhruva Raman (Cambridge) Fri 19 April 2019 10h30 Mila Auditorium
by saikrishna gottipati 15 Apr '19

15 Apr '19

This week we have *Dhruva Raman* from *Univ of Cambridge *giving a talk on *Fundamental Bounds on Learning Performance in Neural Circuits *at *10h30* on *19th April* in *Mila Auditorium*. If you are interested in* meeting Dhruva*, please book a slot in this google sheet <https://docs.google.com/spreadsheets/d/13k6e1rCDM1tD__MvxS-UDHMS9L6XsWcTvqs…> See you there! The Tea Talk Team *TITLE* Fundamental Bounds on Learning Performance in Neural Circuits *ABSTRACT* Biological neural circuits learn in spite of imperfect information on task performance and noisy biological components. How can these problems be mitigated? We use optimization theory to show how adding apparently redundant neurons and connections to a network can improve learning performance in the face of imperfect learning rules and corrupted error signals. The theory shows how large neural circuits can exploit additional connectivity to achieve faster and more precise learning. However, there is a limit to the benefit of adding connections. Biologically, synapses (connections strengths) are intrinsically unreliable. We show that excessive network size eventually outcompetes the benefits to learning performance. Consequently, there is an optimal size of network for a given task, which we can calculate in specific cases. *BIO* Dhruva did his undergraduate (MMath) at the University of Warwick (2008-2012), spent a year at the systems biology doctoral training centre at the University of Oxford (2012-2013), and did his PhD in the Control Group at the University of Oxford under the supervision of Antonis Papachristodoulou (2013-2016). Since 2017 he has been a postdoc in the control group at the University of Cambridge under the supervision of Timothy O’Leary.

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[Tea Talk] Yoshua Bengio (Mila) Fri 12 April 2019 10h30 Mila Auditorium
by Rim Assouel 12 Apr '19

12 Apr '19

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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[Tea Talk] Anirudh Goyal (Mila) Fri 5 April 2019 10h30 Mila Auditorium
by Rim Assouel 05 Apr '19

05 Apr '19

This week we have Anirudh Goyal from Mila giving a talk on Inductive Biases, Invariances and Generalization at 10h30 in room Mila Auditorium. Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? yes See you there! The Tea Talk Team TITLE Inductive Biases, Invariances and Generalization ABSTRACT The biggest challenge right now in RL specifically, AI more generally, is to devise methods for learning complex behaviours that have meaningful generalization. In supervised learning (vision, speech, NLP), it seems that deep models can achieve very complex generalization, but this is not really the case in RL. In this talk, I'm going to first argue that this gap in generalization has much to do with current practices in evaluating generalization in multitask, transfer and meta-learning setups, and then I'm going to talk about an elegant solution for learning complex inductive biases that can be used to exploit the structure of the task, thus enabling the learning agent to incorporate prior knowledge into the learning system to exploit reusable structure in task space. BIO Anirudh Goyal is a first year PhD student advised by Prof. Yoshua Bengio. His current research interests center around understanding the interplay b/w three words: Invariances, Inductive Biases and Generalization.

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[Coffee-Talk] April Van Houtte (Mila) Tuesday 1 April 2020 6h30 Mila Auditorium
by Rim Assouel 01 Apr '19

01 Apr '19

Next year we will have an additional Coffee talk given by April Van Houtte from Mila giving a talk on Generalization of the S-o-C in Deep Learning at 6h30 in room Mila Auditorium. Will this talk be streamed ? yes See you there! Pablo, Sai and Rim for The Coffee Talk Team TITLE Biases in Grad students: Analysis from a Cafeine Theoretic perspective ABSTRACT The biggest challenge right now in grad students specifically, AI grad students, is to devise methods for learning complex behaviours that include attending classes at 8.30am. In this talk, I'm going to first argue that the discrepancy between tea and coffee has much to do with current practices in evaluating generalization in multitask, transfer and meta-learning setups, and then I'm going to talk about an elegant solution for learning complex productive working strategies that can be used to exploit the structure of the task, thus enabling the grad student to incorporate prior knowledge into the learning system to exploit reusable structure in task space. BIO April Van Houtte is a senior member of Mila and he works on keeping grad students caffeinated. He pioneered the S-o-C (sip of coffee) operator as a new neural modulation block.He recently received enduring award from ACM (Association of Coffee Makers) which is considered the noble prize in hot beverages.

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