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Jules Pondard

Alumni

Publications

Disentangling the independently controllable factors of variation by interacting with the world
Valentin Thomas
Philippe Beaudoin
Emmanuel Bengio
Hugo Larochelle
William Fedus
Joelle Pineau
Yoshua Bengio
Jules Pondard
Doina Precup
It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it rema… (voir plus)ins an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that some of the causal factors could be discovered if the learner is allowed to interact with its environment. The agent can experiment with different actions and observe their effects. More specifically, we hypothesize that some of these factors correspond to aspects of the environment which are independently controllable, i.e., that there exists a policy and a learnable feature for each such aspect of the environment, such that this policy can yield changes in that feature with minimal changes to other features that explain the statistical variations in the observed data. We propose a specific objective function to find such factors, and verify experimentally that it can indeed disentangle independently controllable aspects of the environment without any extrinsic reward signal.
2017-12-31
arXiv (prépublication)
doi.org
arxiv.org
Independently Controllable Factors
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. However, it rema… (voir plus)ins an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that some of the causal factors could be discovered if the learner is allowed to interact with its environment. The agent can experiment with different actions and observe their effects. More specifically, we hypothesize that some of these factors correspond to aspects of the environment which are independently controllable, i.e., that there exists a policy and a learnable feature for each such aspect of the environment, such that this policy can yield changes in that feature with minimal changes to other features that explain the statistical variations in the observed data. We propose a specific objective function to find such factors and verify experimentally that it can indeed disentangle independently controllable aspects of the environment without any extrinsic reward signal.
2016-12-31
arXiv (prépublication)
doi.org
arxiv.org