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Timothée LESORT

Postdoctoral Researcher

I am a Postdoctoral Researcher in the Department of Computer Science and Operations Research (DIRO) at the University of Montreal, and a member of Mila – Quebec Artificial Intelligence Institute under the supervision of Prof. Irina Rish. I study the capacity of artificial deep neural networks to detect drifts in their input data distribution and how they learn, memorize, and forget in sequences of tasks. I am also interested in generative models, state representation learning, and continual learning. I obtained my Ph.D. in Computer Science from Institut Polytechnique de Paris (France) under the supervision of David Filliat. My Ph.D. was focused on replay processes for continual learning. Previously, I obtained my engineering diploma in electronics and robotics from CPE Lyon (France).

I previously worked on state representation learning (SLR). It was mostly about unsupervised learning of latent representation for control applications.

Google Scholar Page: Scholar

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Selected Articles

State representation learning for control: An overview (2018)
T Lesort, N Díaz-Rodríguez, JF Goudou, D Filliat, Neural Networks 108, 379-392 pdf

An overview on strategies to extract latent representation from data in particular for reinforcement learning and control purposes.

Generative Models from the perspective of Continual Learning (2018)
T Lesort, H Caselles-Dupré, M Garcia-Ortiz, A Stoian, D Filliat, IJCNN 2019 arXiv

An empirical study on how various type of generative models may help to learn continually on incremental scenarios.

DisCoRL: Continual Reinforcement Learning via Policy Distillation (2019)
R Traoré, H Caselles-Dupré, T Lesort, T Sun, N Diaz-Rodriguez, D Filliat, NeurIPS 2019 Workshop on “Reinforcement Learning” arXiv

An approach to continual reinforcement learning based on distillation and rehearsal.

Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges (2019)
T Lesort, V Lomonaco, A Stoian, D Maltoni, D Filliat, N Díaz-Rodríguez, Information Fusion 58, 52-68 arXiv

An overview on continual learning, existing approaches, evaluation metrics/benchmarks with a proposition of framework for continual approaches and description of how robotics environments need continual learning and may exploit it.

All my publications here or on my Scholar.

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