, Towards a Definition of Disentangled Representations, 2018.
Darla: Improving zero-shot transfer in reinforcement learning, Proceedings of the 34th International Conference on Machine Learning, vol.70, pp.1480-1490, 2017. ,
Adam: A method for stochastic optimization, 3rd International Conference for Learning Representations, 2014. ,
On the Fairness of Disentangled Representations, NeurIPS, vol.2019, 2019. ,
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations, International Conference on Machine Learning, pp.4114-4124, 2019. ,
Automatic differentiation in pytorch, 2017. ,
Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00650905
Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics, ICLR 2019 Workshop on Structure & Priors in Reinforcement Learning (SPiRL), 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02285831
Steps Towards a Theory of Visual Information: Active Perception, Signal-to-Symbol Conversion and the Interplay Between Sensing and Control, 2011. ,
, Doina Precup, and Yoshua Bengio. 2017. Independently Controllable Features, 2017.
Are Disentangled Representations Helpful for Abstract Visual Reasoning?, NeurIPS, vol.2019, 2019. ,