  Meta-learning allows an intelligent agent to leverage prior learning episodes
as a basis for quickly improving performance on a novel task. Bayesian
hierarchical modeling provides a theoretical framework for formalizing
meta-learning as inference for a set of parameters that are shared across
tasks. Here, we reformulate the model-agnostic meta-learning algorithm (MAML)
of Finn et al. (2017) as a method for probabilistic inference in a hierarchical
Bayesian model. In contrast to prior methods for meta-learning via hierarchical
Bayes, MAML is naturally applicable to complex function approximators through
its use of a scalable gradient descent procedure for posterior inference.
Furthermore, the identification of MAML as hierarchical Bayes provides a way to
understand the algorithm's operation as a meta-learning procedure, as well as
an opportunity to make use of computational strategies for efficient inference.
We use this opportunity to propose an improvement to the MAML algorithm that
makes use of techniques from approximate inference and curvature estimation.
