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Hyperparameter Selection in Continual Learning
April 10, 2024, 4:42 a.m. | Thomas L. Lee, Sigrid Passano Hellan, Linus Ericsson, Elliot J. Crowley, Amos Storkey
cs.LG updates on arXiv.org arxiv.org
Abstract: In continual learning (CL) -- where a learner trains on a stream of data -- standard hyperparameter optimisation (HPO) cannot be applied, as a learner does not have access to all of the data at the same time. This has prompted the development of CL-specific HPO frameworks. The most popular way to tune hyperparameters in CL is to repeatedly train over the whole data stream with different hyperparameter settings. However, this end-of-training HPO is unrealistic …
abstract arxiv continual cs.lg data development frameworks hyperparameter optimisation popular standard stat.ml trains type
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