  We introduce a new, rigorously-formulated Bayesian meta-learning algorithm
that learns a probability distribution of model parameter prior for few-shot
learning. The proposed algorithm employs a gradient-based variational inference
to infer the posterior of model parameters to a new task. Our algorithm can be
applied to any model architecture and can be implemented in various machine
learning paradigms, including regression and classification. We show that the
models trained with our proposed meta-learning algorithm are well calibrated
and accurate, with state-of-the-art calibration and classification results on
two few-shot classification benchmarks (Omniglot and Mini-ImageNet), and
competitive results in a multi-modal task-distribution regression.
