  Neural Architecture Search (NAS) has been quite successful in constructing
state-of-the-art models on a variety of tasks. Unfortunately, the computational
cost can make it difficult to scale. In this paper, we make the first attempt
to study Meta Architecture Search which aims at learning a task-agnostic
representation that can be used to speed up the process of architecture search
on a large number of tasks. We propose the Bayesian Meta Architecture SEarch
(BASE) framework which takes advantage of a Bayesian formulation of the
architecture search problem to learn over an entire set of tasks
simultaneously. We show that on Imagenet classification, we can find a model
that achieves 25.7% top-1 error and 8.1% top-5 error by adapting the
architecture in less than an hour from an 8 GPU days pretrained meta-network.
By learning a good prior for NAS, our method dramatically decreases the
required computation cost while achieving comparable performance to current
state-of-the-art methods - even finding competitive models for unseen datasets
with very quick adaptation. We believe our framework will open up new
possibilities for efficient and massively scalable architecture search research
across multiple tasks.
