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Posterior Meta-Replay for Continual Learning
[article]
2021
arXiv
pre-print
Learning a sequence of tasks without access to i.i.d. observations is a widely studied form of continual learning (CL) that remains challenging. In principle, Bayesian learning directly applies to this setting, since recursive and one-off Bayesian updates yield the same result. In practice, however, recursive updating often leads to poor trade-off solutions across tasks because approximate inference is necessary for most models of interest. Here, we describe an alternative Bayesian approach
arXiv:2103.01133v3
fatcat:4tjj74x74vew7gqif4atmg5qjm