35 citations · 95 across the 5 of their papers we have counts for
12 papers · 1 filter
Continual Feature Selection: Spurious Features in Continual Learning
Timothée Lesort
Continual Learning (CL) is the research field addressing learning without forgetting when the data distribution is not static. This paper studies spurious features' influence on co…
Continuum: Simple Management of Complex Continual Learning Scenarios
Arthur Douillard, Timothée Lesort
Continual learning is a machine learning sub-field specialized in settings with non-iid data. Hence, the training data distribution is not static and drifts through time. Those dri…
Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes
Timothée Lesort
Humans learn all their life long. They accumulate knowledge from a sequence of learning experiences and remember the essential concepts without forgetting what they have learned pr…
Regularization Shortcomings for Continual Learning
Timothée Lesort, Andrei Stoian, David Filliat
In most machine learning algorithms, training data is assumed to be independent and identically distributed (iid). When it is not the case, the algorithm's performances are challen…
DisCoRL: Continual Reinforcement Learning via Policy Distillation
René Traoré, Hugo Caselles-Dupré, Timothée Lesort +4
In multi-task reinforcement learning there are two main challenges: at training time, the ability to learn different policies with a single model; at test time, inferring which of…
Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges
Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian +3
Continual learning (CL) is a particular machine learning paradigm where the data distribution and learning objective changes through time, or where all the training data and object…