Generalized Inner Loop Meta-Learning

Edward Grefenstette; Brandon Amos; Denis Yarats; Phu Mon Htut; Artem Molchanov; Franziska Meier; Douwe Kiela; Kyunghyun Cho; Soumith Chintala

arXiv · 1910.01727

Generalized Inner Loop Meta-Learning

Edward Grefenstette·Brandon Amos·Denis Yarats·Phu Mon Htut·Artem Molchanov·Franziska Meier·Douwe Kiela·Kyunghyun Cho·Soumith Chintala

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Abstract

Many (but not all) approaches self-qualifying as "meta-learning" in deep learning and reinforcement learning fit a common pattern of approximating the solution to a nested optimization problem. In this paper, we give a formalization of this shared pattern, which we call GIMLI, prove its general requirements, and derive a general-purpose algorithm for implementing similar approaches. Based on this analysis and algorithm, we describe a library of our design, higher, which we share with the community to assist and enable future research into these kinds of meta-learning approaches. We end the paper by showcasing the practical applications of this framework and library through illustrative experiments and ablation studies which they facilitate.

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Edward Grefenstette, Brandon Amos, Denis Yarats, Phu Mon Htut, Artem Molchanov, Franziska Meier, Douwe Kiela, Kyunghyun Cho, Soumith Chintala. 2019-10-03. Generalized Inner Loop Meta-Learning. https://arxiv.org/abs/1910.01727

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