Timothee LESORT
Timothee LESORT
Mila - Quebec AI Institute
Verified email at ensta-paris.fr - Homepage
Title
Cited by
Cited by
Year
State representation learning for control: An overview
T Lesort, N Díaz-Rodríguez, JF Goudou, D Filliat
Neural Networks 108, 379-392, 2018
1652018
Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges
T Lesort, V Lomonaco, A Stoian, D Maltoni, D Filliat, N Díaz-Rodríguez
Information Fusion 58, 52-68, 2020
852020
Generative models from the perspective of continual learning
T Lesort, H Caselles-Dupré, M Garcia-Ortiz, A Stoian, D Filliat
2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019
632019
Deep unsupervised state representation learning with robotic priors: a robustness analysis
T Lesort, M Seurin, X Li, N Díaz-Rodríguez, D Filliat
2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019
33*2019
Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics
A Raffin, A Hill, KR Traoré, T Lesort, N Díaz-Rodríguez, D Filliat
International Conference on Learning Representations (ICLR) 2019, Structure …, 2019
242019
DisCoRL: Continual Reinforcement Learning via Policy Distillation
R Traoré, H Caselles-Dupré, T Lesort, T Sun, G Cai, N Díaz-Rodríguez, ...
International Conference on Neural Information Processing Systems (NeurIPS …, 2019
23*2019
S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning
DF Antonin Raffin, Ashley Hill, René Traoré, Timothée Lesort, Natalia Díaz ...
International Conference on Neural Information Processing Systems (NeurIPS …, 2018
23*2018
Marginal replay vs conditional replay for continual learning
T Lesort, A Gepperth, A Stoian, D Filliat
International Conference on Artificial Neural Networks, 466-480, 2019
212019
Continual learning for robotics
T Lesort, V Lomonaco, A Stoian, D Maltoni, D Filliat, N Dıaz-Rodrıguez
arXiv preprint arXiv:1907.00182, 1-34, 2019
192019
Regularization shortcomings for continual learning
T Lesort, A Stoian, D Filliat
arXiv preprint arXiv:1912.03049, 2019
122019
Training Discriminative Models to Evaluate Generative Ones
T Lesort, JF Goudou, D Filliat
arXiv preprint arXiv:1806.10840, 2018
11*2018
Exploring to learn visual saliency: The RL-IAC approach
C Craye, T Lesort, D Filliat, JF Goudou
Robotics and Autonomous Systems 112, 244-259, 2019
62019
Continual reinforcement learning deployed in real-life using policy distillation and sim2real transfer
RT Kalifou, H Caselles-Dupré, T Lesort, T Sun, N Diaz-Rodriguez, D Filliat
ICML Workshop on Multi-Task and Lifelong Learning 4, 2019
62019
Understanding Continual Learning Settings with Data Distribution Drift Analysis
T Lesort, M Caccia, I Rish
arXiv preprint arXiv:2104.01678, 2021
32021
Continuum: Simple management of complex continual learning scenarios
A Douillard, T Lesort
arXiv preprint arXiv:2102.06253, 2021
22021
Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes
T Lesort
arXiv preprint arXiv:2007.00487, 2020
22020
Continual Learning in Deep Networks: an Analysis of the Last Layer
T Lesort, T George, I Rish
arXiv preprint arXiv:2106.01834, 2021
2021
Apprentissage continu: S'attaquer à l'oubli foudroyant des réseaux de neurones profonds grâce aux méthodes à rejeu de données
T Lesort
Institut Polytechnique de Paris, 2020
2020
Continuum: Data Loaders for Continual Learning
TL Arthur Douillard
https://github.com/Continvvm/continuum, 2020
2020
Unsupervised Deep Learning of State Representation Using Robotic Priors
T LESORT, D FILLIAT
2016
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Articles 1–20