305 citations · 534 across the 23 of their papers we have counts for
4 papers · 1 filter
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…
Continual Learning in Deep Networks: an Analysis of the Last Layer
Timothée Lesort, Thomas George, Irina Rish
We study how different output layer parameterizations of a deep neural network affects learning and forgetting in continual learning settings. The following three effects can cause…
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…
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…