| 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 | 633 | 2020 |
| State representation learning for control: An overview T Lesort, N Díaz-Rodríguez, JF Goudou, D Filliat Neural Networks 108, 379-392, 2018 | 418 | 2018 |
| 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 | 201 | 2019 |
| 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 | 77* | 2022 |
| Continual Pre-Training of Large Language Models: How to (re) warm your model? K Gupta, B Thérien, A Ibrahim, ML Richter, Q Anthony, E Belilovsky, I Rish, ... | 76 | 2023 |
| 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 | 75 | 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 | 68 | 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 | 64 | 2021 |
| Simple and scalable strategies to continually pre-train large language models A Ibrahim, B Thérien, K Gupta, ML Richter, Q Anthony, T Lesort, ... arXiv preprint arXiv:2403.08763, 2024 | 61 | 2024 |
| 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 | 58* | 2019 |
| Continual reinforcement learning deployed in real-life using policy distillation and sim2real transfer R Traoré, H Caselles-Dupré, T Lesort, T Sun, N Díaz-Rodríguez, D Filliat arXiv preprint arXiv:1906.04452, 2019 | 58 | 2019 |
| Regularization shortcomings for continual learning T Lesort, A Stoian, D Filliat arXiv preprint arXiv:1912.03049, 2019 | 52 | 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 | 40* | 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 | 39 | 2019 |
| Continuum: Simple management of complex continual learning scenarios A Douillard, T Lesort arXiv preprint arXiv:2102.06253, 2021 | 35* | 2021 |
| Continual learning: Tackling catastrophic forgetting in deep neural networks with replay processes T Lesort arXiv preprint arXiv:2007.00487, 2020 | 34* | 2020 |
| 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 | 27* | 2021 |
| Training Discriminative Models to Evaluate Generative Ones T Lesort, JF Goudou, D Filliat International Conference on Artificial Neural Networks, 604--619, 2018 | 23 | 2018 |
| Continual feature selection: Spurious features in continual learning T Lesort arXiv preprint arXiv:2203.01012, 2022 | 21* | 2022 |
| 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 | 21 | 2021 |
David FilliatProfessor, ENSTA ParisVerified email at ensta-paris.fr
