79 citations · 255 across the 15 of their papers we have counts for
5 papers · 1 filter
Continual Learning via Local Module Composition
Oleksiy Ostapenko, Pau Rodriguez, Massimo Caccia +1
Modularity is a compelling solution to continual learning (CL), the problem of modeling sequences of related tasks. Learning and then composing modules to solve different tasks pro…
Learning where to learn: Gradient sparsity in meta and continual learning
Johannes von Oswald, Dominic Zhao, Seijin Kobayashi +4
Finding neural network weights that generalize well from small datasets is difficult. A promising approach is to learn a weight initialization such that a small number of weight ch…
Sequoia: A Software Framework to Unify Continual Learning Research
Fabrice Normandin, Florian Golemo, Oleksiy Ostapenko +10
The field of Continual Learning (CL) seeks to develop algorithms that accumulate knowledge and skills over time through interaction with non-stationary environments. In practice, a…
Understanding Continual Learning Settings with Data Distribution Drift Analysis
Timothée Lesort, Massimo Caccia, Irina Rish
Classical machine learning algorithms often assume that the data are drawn i.i.d. from a stationary probability distribution. Recently, continual learning emerged as a rapidly grow…
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
Pau Rodriguez, Massimo Caccia, Alexandre Lacoste +4
Explainability for machine learning models has gained considerable attention within the research community given the importance of deploying more reliable machine-learning systems.…