Decision-Theoretic Meta-Learning: Versatile and Efficient Amortization of Few-Shot Learning

This paper develops a general framework for data efficient and versatile deep learning that comprises a novel Bayesian decision theoretic approach to meta-learning probabilistic inference across many tasks, and a fast, flexible, and simple to train amortization network that can automatically generalize and extrapolate to a wide range of settings.