Gradient-EM Bayesian Meta-learning

• URL: http://arxiv.org/abs/2006.11764v2
• Date: Wed, 18 Nov 2020 06:34:49 GMT
• Title: Gradient-EM Bayesian Meta-learning
• Authors: Yayi Zou, Xiaoqi Lu
• Abstract summary: Key idea behind Bayesian meta-learning is empirical Bayes inference of hierarchical model. In this work, we extend this framework to include a variety of existing methods, before proposing our variant based on gradient-EM algorithm. Experiments on sinusoidal regression, few-shot image classification, and policy-based reinforcement learning show that our method not only achieves better accuracy with less computation cost, but is also more robust to uncertainty.
• Score: 6.726255259929496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bayesian meta-learning enables robust and fast adaptation to new tasks with uncertainty assessment. The key idea behind Bayesian meta-learning is empirical Bayes inference of hierarchical model. In this work, we extend this framework to include a variety of existing methods, before proposing our variant based on gradient-EM algorithm. Our method improves computational efficiency by avoiding back-propagation computation in the meta-update step, which is exhausting for deep neural networks. Furthermore, it provides flexibility to the inner-update optimization procedure by decoupling it from meta-update. Experiments on sinusoidal regression, few-shot image classification, and policy-based reinforcement learning show that our method not only achieves better accuracy with less computation cost, but is also more robust to uncertainty.

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