Modulating transfer between tasks in gradient-based meta-learning

ICLR 2019 Erin GrantGhassen JerfelKatherine HellerThomas L. Griffiths

Learning-to-learn or meta-learning leverages data-driven inductive bias to increase the efficiency of learning on a novel task. This approach encounters difficulty when transfer is not mutually beneficial, for instance, when tasks are sufficiently dissimilar or change over time... (read more)

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