Timothée Lesort

According to our database, Timothée Lesort authored at least 34 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Amplifying pathological detection in EEG signaling pathways through cross-dataset transfer learning.

Mohammad-Javad Darvishi Bayazi, Mohammad Sajjad Ghaemi, Timothée Lesort, Md Rifat Arefin, Jocelyn Faubert, Irina Rish

Comput. Biol. Medicine, February, 2024

Simple and Scalable Strategies to Continually Pre-train Large Language Models.

Adam Ibrahim, Benjamin Thérien, Kshitij Gupta, Mats L. Richter, Quentin Gregory Anthony, Eugene Belilovsky, Timothée Lesort, Irina Rish

Trans. Mach. Learn. Res., 2024

Continual Learning Under Language Shift.

Evangelia Gogoulou, Timothée Lesort, Magnus Boman, Joakim Nivre

Proceedings of the Text, Speech, and Dialogue - 27th International Conference, 2024

2023

A Study of Continual Learning Under Language Shift.

Evangelia Gogoulou, Timothée Lesort, Magnus Boman, Joakim Nivre

CoRR, 2023

Continual Pre-Training of Large Language Models: How to (re)warm your model?

Kshitij Gupta, Benjamin Thérien, Adam Ibrahim, Mats L. Richter, Quentin Anthony, Eugene Belilovsky, Irina Rish, Timothée Lesort

CoRR, 2023

Challenging Common Assumptions about Catastrophic Forgetting and Knowledge Accumulation.

Timothée Lesort, Oleksiy Ostapenko, Pau Rodríguez, Diganta Misra, Md Rifat Arefin, Laurent Charlin, Irina Rish

Proceedings of the Conference on Lifelong Learning Agents, 2023

Spurious Features in Continual Learning.

Timothée Lesort

Proceedings of the AAAI Bridge Program on Continual Causality, 2023

2022

Beyond Supervised Continual Learning: a Review.

Benedikt Bagus, Alexander Gepperth, Timothée Lesort

CoRR, 2022

Scaling the Number of Tasks in Continual Learning.

Timothée Lesort, Oleksiy Ostapenko, Diganta Misra, Md Rifat Arefin, Pau Rodríguez, Laurent Charlin, Irina Rish

CoRR, 2022

Foundational Models for Continual Learning: An Empirical Study of Latent Replay.

Oleksiy Ostapenko, Timothée Lesort, Pau Rodríguez, Md Rifat Arefin, Arthur Douillard, Irina Rish, Laurent Charlin

CoRR, 2022

Continual Feature Selection: Spurious Features in Continual Learning.

Timothée Lesort

CoRR, 2022

Tutorial - Continual Learning beyond classification.

Alexander Gepperth, Timothée Lesort

Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

Continual Learning with Foundation Models: An Empirical Study of Latent Replay.

Oleksiy Ostapenko, Timothée Lesort, Pau Rodríguez, Md Rifat Arefin, Arthur Douillard, Irina Rish, Laurent Charlin

Proceedings of the Conference on Lifelong Learning Agents, 2022

2021

Sequoia: A Software Framework to Unify Continual Learning Research.

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

CoRR, 2021

Continual Learning in Deep Networks: an Analysis of the Last Layer.

Timothée Lesort, Thomas George, Irina Rish

CoRR, 2021

Understanding Continual Learning Settings with Data Distribution Drift Analysis.

Timothée Lesort, Massimo Caccia, Irina Rish

CoRR, 2021

Continuum: Simple Management of Complex Continual Learning Scenarios.

Arthur Douillard, Timothée Lesort

CoRR, 2021

2020

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).

Timothée Lesort

PhD thesis, 2020

Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges.

Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian, Davide Maltoni, David Filliat, Natalia Díaz Rodríguez

Inf. Fusion, 2020

Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes.

Timothée Lesort

CoRR, 2020

2019

Exploring to learn visual saliency: The RL-IAC approach.

Céline Craye, Timothée Lesort, David Filliat, Jean-François Goudou

Robotics Auton. Syst., 2019

Regularization Shortcomings for Continual Learning.

Timothée Lesort, Andrei Stoian, David Filliat

CoRR, 2019

DisCoRL: Continual Reinforcement Learning via Policy Distillation.

René Traoré, Hugo Caselles-Dupré, Timothée Lesort, Te Sun, Guanghang Cai, Natalia Díaz Rodríguez, David Filliat

CoRR, 2019

Continual Learning for Robotics.

Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian, Davide Maltoni, David Filliat, Natalia Díaz Rodríguez

CoRR, 2019

Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer.

René Traoré, Hugo Caselles-Dupré, Timothée Lesort, Te Sun, Natalia Díaz Rodríguez, David Filliat

CoRR, 2019

Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics.

Antonin Raffin, Ashley Hill, Kalifou René Traoré, Timothée Lesort, Natalia Díaz Rodríguez, David Filliat

CoRR, 2019

Deep unsupervised state representation learning with robotic priors: a robustness analysis.

Timothée Lesort, Mathieu Seurin, Xinrui Li, Natalia Díaz Rodríguez, David Filliat

Proceedings of the International Joint Conference on Neural Networks, 2019

Generative Models from the perspective of Continual Learning.

Timothée Lesort, Hugo Caselles-Dupré, Michaël Garcia Ortiz, Andrei Stoian, David Filliat

Proceedings of the International Joint Conference on Neural Networks, 2019

Training Discriminative Models to Evaluate Generative Ones.

Timothée Lesort, Andrei Stoian, Jean-François Goudou, David Filliat

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019: Image Processing, 2019

Marginal Replay vs Conditional Replay for Continual Learning.

Timothée Lesort, Alexander Gepperth, Andrei Stoian, David Filliat

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019: Deep Learning, 2019

2018

State representation learning for control: An overview.

Timothée Lesort, Natalia Díaz Rodríguez, Jean-François Goudou, David Filliat

Neural Networks, 2018

S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning.

Antonin Raffin, Ashley Hill, René Traoré, Timothée Lesort, Natalia Díaz Rodríguez, David Filliat

CoRR, 2018

Training Discriminative Models to Evaluate Generative Ones.

Timothée Lesort, Jean-François Goudou, David Filliat

CoRR, 2018

2017

Unsupervised state representation learning with robotic priors: a robustness benchmark.

Timothée Lesort, Mathieu Seurin, Xinrui Li, Natalia Díaz Rodríguez, David Filliat

CoRR, 2017