Bayesian Meta-network Architecture Learning

For deep neural networks, the particular structure often plays a vital role in achieving state-of-the-art performances in many practical applications. However, existing architecture search methods can only learn the architecture for a single task at a time. In this paper, we first propose a Bayesian inference view of architecture learning and use this novel view to derive a variational inference method to learn the architecture of a meta-network, which will be shared across multiple tasks. To account for the task distribution in the posterior distribution of the architecture and its corresponding weights, we exploit the optimization embedding technique to design the parameterization of the posterior. Our method finds architectures which achieve state-of-the-art performance on the few-shot learning problem and demonstrates the advantages of meta-network learning for both architecture search and meta-learning.