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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Reminder that this happens in 30 minutes :)
Le lundi 1 avril 2019, Rim Assouel rim.assouel@gmail.com a écrit :
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.
The streaming link is https://mila.bluejeans.com/4862024040/webrtc https://www.google.com/url?q=https%3A%2F%2Fmila.bluejeans.com%2F4862024040%2Fwebrtc&sa=D&usd=2&usg=AFQjCNGqbTWFulqOxxIoTuNpaxpDzb3XCw
On Fri, Apr 5, 2019 at 9:59 AM Rim Assouel rim.assouel@gmail.com wrote:
Reminder that this happens in 30 minutes :)
Le lundi 1 avril 2019, Rim Assouel rim.assouel@gmail.com a écrit :
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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