Search Results for author: Philippe Beaudoin

Found 9 papers, 1 papers with code

Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments

no code implementations ICLR 2021 Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Charles Blundell, Sergey Levine, Yoshua Bengio, Michael Curtis Mozer

To use a video game as an illustration, two enemies of the same type will share schemata but will have separate object files to encode their distinct state (e. g., health, position).

Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers

no code implementations15 Oct 2020 Alex Lamb, Anirudh Goyal, Agnieszka Słowik, Michael Mozer, Philippe Beaudoin, Yoshua Bengio

Feed-forward neural networks consist of a sequence of layers, in which each layer performs some processing on the information from the previous layer.

Domain Generalization

Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems

no code implementations29 Jun 2020 Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Sergey Levine, Charles Blundell, Yoshua Bengio, Michael Mozer

To use a video game as an illustration, two enemies of the same type will share schemata but will have separate object files to encode their distinct state (e. g., health, position).

CLOSURE: Assessing Systematic Generalization of CLEVR Models

3 code implementations12 Dec 2019 Dzmitry Bahdanau, Harm de Vries, Timothy J. O'Donnell, Shikhar Murty, Philippe Beaudoin, Yoshua Bengio, Aaron Courville

In this work, we study how systematic the generalization of such models is, that is to which extent they are capable of handling novel combinations of known linguistic constructs.

Few-Shot Learning Systematic Generalization +1

Independently Controllable Factors

no code implementations3 Aug 2017 Valentin Thomas, Jules Pondard, Emmanuel Bengio, Marc Sarfati, Philippe Beaudoin, Marie-Jean Meurs, Joelle Pineau, Doina Precup, Yoshua Bengio

It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation.

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