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stat.ML2019★ 57 cited
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…
stat.ML2019
Functional Regularisation for Continual Learning with Gaussian Processes
Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews +2
We introduce a framework for Continual Learning (CL) based on Bayesian inference over the function space rather than the parameters of a deep neural network. This method, referred…