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Meta-Learning with Implicit Gradients

Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine

Neural Information Processing Systems

A core aspect of intelligence is the ability to quickly learn new tasks by drawing upon prior experience from related tasks. Recent work has studied how meta-learning algorithms [51, 55, 41] can acquire such a capability by learning to efficiently learn a range of tasks, thereby enabling learning of a new task with as little as a single example [50, 57, 15].

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