An inference perspective on meta-learning
Schedule
Thursday, November 26, 2020
15:00 UTC
Kate Rakelly
University of California Berkeley
Host: Sheffield ML
Recording
Open recording
Event Information
Recording
Available
Host
Sheffield ML
Duration
70 minutes
Abstract
While meta-learning algorithms are often viewed as algorithms that learn to learn, an alternative viewpoint frames meta-learning as inferring a hidden task variable from experience consisting of observations and rewards. From this perspective, learning to learn is learning to infer. This viewpoint can be useful in solving problems in meta-RL, which I’ll demonstrate through two examples: (1) enabling off-policy meta-learning, and (2) performing efficient meta-RL from image observations. I’ll also discuss how this perspective leads to an algorithm for few-shot image segmentation.
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