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ORCIDTimothée Lesort
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2020 – today
- 2024
[j5]Mohammad-Javad Darvishi Bayazi
, Mohammad Sajjad Ghaemi, Timothée Lesort, Md Rifat Arefin, Jocelyn Faubert, Irina Rish:
Amplifying pathological detection in EEG signaling pathways through cross-dataset transfer learning. Comput. Biol. Medicine 169: 107893 (2024)- [j4]Adam Ibrahim, Benjamin Thérien, Kshitij Gupta, Mats L. Richter, Quentin Gregory Anthony, Eugene Belilovsky, Timothée Lesort, Irina Rish:
Simple and Scalable Strategies to Continually Pre-train Large Language Models. Trans. Mach. Learn. Res. 2024 (2024) - [c9]Evangelia Gogoulou, Timothée Lesort, Magnus Boman, Joakim Nivre:
Continual Learning Under Language Shift. TSD (1) 2024: 71-84 - [i25]Adam Ibrahim, Benjamin Thérien, Kshitij Gupta, Mats L. Richter, Quentin Anthony, Timothée Lesort, Eugene Belilovsky, Irina Rish:
Simple and Scalable Strategies to Continually Pre-train Large Language Models. CoRR abs/2403.08763 (2024) - 2023
- [c8]Timothée Lesort:
Spurious Features in Continual Learning. AAAI Bridge Program 2023: 59-62 - [c7]Timothée Lesort, Oleksiy Ostapenko, Pau Rodríguez, Diganta Misra, Md Rifat Arefin, Laurent Charlin, Irina Rish:
Challenging Common Assumptions about Catastrophic Forgetting and Knowledge Accumulation. CoLLAs 2023: 43-65 - [i24]Kshitij Gupta, Benjamin Thérien, Adam Ibrahim, Mats L. Richter, Quentin Anthony, Eugene Belilovsky, Irina Rish, Timothée Lesort:
Continual Pre-Training of Large Language Models: How to (re)warm your model? CoRR abs/2308.04014 (2023) - [i23]Mohammad-Javad Darvishi Bayazi, Mohammad Sajjad Ghaemi, Timothée Lesort, Md Rifat Arefin, Jocelyn Faubert, Irina Rish:
Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning. CoRR abs/2309.10910 (2023) - [i22]Evangelia Gogoulou, Timothée Lesort, Magnus Boman, Joakim Nivre:
A Study of Continual Learning Under Language Shift. CoRR abs/2311.01200 (2023) - 2022
- [c6]Oleksiy Ostapenko, Timothée Lesort, Pau Rodríguez, Md Rifat Arefin, Arthur Douillard, Irina Rish, Laurent Charlin:
Continual Learning with Foundation Models: An Empirical Study of Latent Replay. CoLLAs 2022: 60-91 - [c5]Alexander Gepperth, Timothée Lesort:
Tutorial - Continual Learning beyond classification. ESANN 2022 - [i21]Timothée Lesort:
Continual Feature Selection: Spurious Features in Continual Learning. CoRR abs/2203.01012 (2022) - [i20]Oleksiy Ostapenko, Timothée Lesort, Pau Rodríguez, Md Rifat Arefin, Arthur Douillard, Irina Rish, Laurent Charlin:
Foundational Models for Continual Learning: An Empirical Study of Latent Replay. CoRR abs/2205.00329 (2022) - [i19]Timothée Lesort, Oleksiy Ostapenko, Diganta Misra, Md Rifat Arefin, Pau Rodríguez, Laurent Charlin, Irina Rish:
Scaling the Number of Tasks in Continual Learning. CoRR abs/2207.04543 (2022) - [i18]Benedikt Bagus, Alexander Gepperth, Timothée Lesort:
Beyond Supervised Continual Learning: a Review. CoRR abs/2208.14307 (2022) - 2021
- [i17]Arthur Douillard, Timothée Lesort:
Continuum: Simple Management of Complex Continual Learning Scenarios. CoRR abs/2102.06253 (2021) - [i16]Timothée Lesort, Massimo Caccia, Irina Rish:
Understanding Continual Learning Settings with Data Distribution Drift Analysis. CoRR abs/2104.01678 (2021) - [i15]Timothée Lesort, Thomas George, Irina Rish:
Continual Learning in Deep Networks: an Analysis of the Last Layer. CoRR abs/2106.01834 (2021) - [i14]Fabrice Normandin, Florian Golemo, Oleksiy Ostapenko, Pau Rodríguez, Matthew D. Riemer, Julio Hurtado, Khimya Khetarpal, Dominic Zhao, Ryan Lindeborg, Timothée Lesort, Laurent Charlin, Irina Rish, Massimo Caccia:
Sequoia: A Software Framework to Unify Continual Learning Research. CoRR abs/2108.01005 (2021) - 2020
- [b1]Timothée Lesort:
Apprentissage continu : S'attaquer à l'oubli foudroyant des réseaux de neurones profonds grâce aux méthodes à rejeu de données. (Continual Learning : Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes). Institut Polytechnique de Paris, France, 2020 - [j3]Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian, Davide Maltoni, David Filliat, Natalia Díaz Rodríguez:
Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges. Inf. Fusion 58: 52-68 (2020) - [i13]Timothée Lesort:
Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes. CoRR abs/2007.00487 (2020)
2010 – 2019
- 2019
- [j2]Céline Craye, Timothée Lesort, David Filliat, Jean-François Goudou:
Exploring to learn visual saliency: The RL-IAC approach. Robotics Auton. Syst. 112: 244-259 (2019) - [c4]Timothée Lesort, Alexander Gepperth, Andrei Stoian, David Filliat:
Marginal Replay vs Conditional Replay for Continual Learning. ICANN (2) 2019: 466-480 - [c3]Timothée Lesort, Andrei Stoian, Jean-François Goudou, David Filliat:
Training Discriminative Models to Evaluate Generative Ones. ICANN (3) 2019: 604-619 - [c2]Timothée Lesort, Hugo Caselles-Dupré, Michaël Garcia Ortiz, Andrei Stoian, David Filliat:
Generative Models from the perspective of Continual Learning. IJCNN 2019: 1-8 - [c1]Timothée Lesort, Mathieu Seurin, Xinrui Li, Natalia Díaz Rodríguez, David Filliat:
Deep unsupervised state representation learning with robotic priors: a robustness analysis. IJCNN 2019: 1-8 - [i12]Antonin Raffin, Ashley Hill, Kalifou René Traoré, Timothée Lesort, Natalia Díaz Rodríguez, David Filliat:
Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics. CoRR abs/1901.08651 (2019) - [i11]René Traoré, Hugo Caselles-Dupré, Timothée Lesort, Te Sun, Natalia Díaz Rodríguez, David Filliat:
Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer. CoRR abs/1906.04452 (2019) - [i10]Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian, Davide Maltoni, David Filliat, Natalia Díaz Rodríguez:
Continual Learning for Robotics. CoRR abs/1907.00182 (2019) - [i9]René Traoré, Hugo Caselles-Dupré, Timothée Lesort, Te Sun, Guanghang Cai, Natalia Díaz Rodríguez, David Filliat:
DisCoRL: Continual Reinforcement Learning via Policy Distillation. CoRR abs/1907.05855 (2019) - [i8]Timothée Lesort, Andrei Stoian, David Filliat:
Regularization Shortcomings for Continual Learning. CoRR abs/1912.03049 (2019) - 2018
- [j1]Timothée Lesort, Natalia Díaz Rodríguez, Jean-François Goudou, David Filliat:
State representation learning for control: An overview. Neural Networks 108: 379-392 (2018) - [i7]Timothée Lesort, Natalia Díaz Rodríguez, Jean-François Goudou, David Filliat:
State Representation Learning for Control: An Overview. CoRR abs/1802.04181 (2018) - [i6]Céline Craye, Timothée Lesort, David Filliat, Jean-Francois Goudou:
Exploring to learn visual saliency: The RL-IAC approach. CoRR abs/1804.00435 (2018) - [i5]Timothée Lesort, Jean-François Goudou, David Filliat:
Training Discriminative Models to Evaluate Generative Ones. CoRR abs/1806.10840 (2018) - [i4]Antonin Raffin, Ashley Hill, René Traoré, Timothée Lesort, Natalia Díaz Rodríguez, David Filliat:
S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning. CoRR abs/1809.09369 (2018) - [i3]Timothée Lesort, Alexander Gepperth, Andrei Stoian, David Filliat:
Marginal Replay vs Conditional Replay for Continual Learning. CoRR abs/1810.12069 (2018) - [i2]Timothée Lesort, Hugo Caselles-Dupré, Michaël Garcia Ortiz, Andrei Stoian, David Filliat:
Generative Models from the perspective of Continual Learning. CoRR abs/1812.09111 (2018) - 2017
- [i1]Timothée Lesort, Mathieu Seurin, Xinrui Li, Natalia Díaz Rodríguez, David Filliat:
Unsupervised state representation learning with robotic priors: a robustness benchmark. CoRR abs/1709.05185 (2017)
Coauthor Index
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