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Timothee Lesort
Timothee Lesort
Mila - Quebec AI Institute
Adresse e-mail validée de ensta-paris.fr - Page d'accueil
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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
3602020
State representation learning for control: An overview
T Lesort, N Díaz-Rodríguez, JF Goudou, D Filliat
Neural Networks 108, 379-392, 2018
3172018
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
1552019
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
86*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
572019
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
54*2019
Understanding Continual Learning Settings with Data Distribution Drift Analysis
T Lesort, M Caccia, I Rish
International Conference of Machine Learning 2021 (ICML) Workshop on Theory …, 2021
452021
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
37*2018
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
362019
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
352019
Regularization shortcomings for continual learning
T Lesort, A Stoian, D Filliat
arXiv preprint arXiv:1912.03049, 2019
322019
Continuum: Simple management of complex continual learning scenarios
A Douillard, T Lesort
arXiv preprint arXiv:2102.06253, 2021
29*2021
Foundational Models for Continual Learning: An Empirical Study of Latent Replay
O Ostapenko, T Lesort, P Rodríguez, MR Arefin, A Douillard, I Rish, ...
CoLLas 2022, Oral, 2022
26*2022
Continual learning: Tackling catastrophic forgetting in deep neural networks with replay processes
T Lesort
arXiv preprint arXiv:2007.00487, 2020
25*2020
Training Discriminative Models to Evaluate Generative Ones
T Lesort, JF Goudou, D Filliat
International Conference on Artificial Neural Networks, 604--619, 2018
202018
Sequoia: A Software Framework to Unify Continual Learning Research
F Normandin, F Golemo, O Ostapenko, P Rodriguez, MD Riemer, ...
arXiv preprint arXiv:2108.01005, 2021
18*2021
Continual Learning in Deep Networks: an Analysis of the Last Layer
T Lesort, T George, I Rish
International Conference of Machine Learning 2021 (ICML) Workshop on Theory …, 2021
162021
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
15*2019
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
102019
Continual feature selection: Spurious features in continual learning
T Lesort
arXiv preprint arXiv:2203.01012, 2022
92022
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