| State representation learning for control: An overview T Lesort, N Díaz-Rodríguez, JF Goudou, D Filliat Neural Networks 108, 379-392, 2018 | 94 | 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 | 28 | 2019 |
| 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 | 19* | 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 | 18* | 2018 |
| 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 | 16 | 2020 |
| 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 | 15 | 2019 |
| Continual learning for robotics T Lesort, V Lomonaco, A Stoian, D Maltoni, D Filliat, N Díaz-Rodríguez arXiv preprint arXiv:1907.00182, 2019 | 11 | 2019 |
| 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 | 10 | 2019 |
| Training Discriminative Models to Evaluate Generative Ones T Lesort, JF Goudou, D Filliat arXiv preprint arXiv:1806.10840, 2018 | 10* | 2018 |
| 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 | 6* | 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 | 6 | 2019 |
| 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, 2019 | 5 | 2019 |
| Regularization shortcomings for continual learning T Lesort, A Stoian, D Filliat arXiv preprint arXiv:1912.03049, 2019 | 3 | 2019 |
| Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes T Lesort arXiv preprint arXiv:2007.00487, 2020 | | 2020 |
| 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 |
| Generative Models from the perspective of Continual Learning M Garcia Ortiz, T Lesort, H Caselles-Dupré, JF Goudou, D Filliat | | 2018 |
| Unsupervised Deep Learning of State Representation Using Robotic Priors T LESORT, D FILLIAT | | 2016 |
David FilliatProfessor, ENSTA ParisAdresse e-mail validée de ensta-paris.fr
