643 citations · 787 across the 7 of their papers we have counts for
3 papers · 1 filter
Continual Unsupervised Representation Learning
Dushyant Rao, Francesco Visin, Andrei A. Rusu +3
Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially…
Meta-Learning with Warped Gradient Descent
Sebastian Flennerhag, Andrei A. Rusu, Razvan Pascanu +3
Learning an efficient update rule from data that promotes rapid learning of new tasks from the same distribution remains an open problem in meta-learning. Typically, previous works…
Task Agnostic Continual Learning via Meta Learning
Xu He, Jakub Sygnowski, Alexandre Galashov +3
While neural networks are powerful function approximators, they suffer from catastrophic forgetting when the data distribution is not stationary. One particular formalism that stud…