  Neural structure search (NAS), as the mainstream approach to automate deep
neural architecture design, has achieved much success in recent years. However,
the performance estimation component adhering to NAS is often prohibitively
costly, which leads to the enormous computational demand. Though a large number
of efforts have been dedicated to alleviating this pain point, no consensus has
been made yet on which is optimal. In this paper, we study the automated
architecture design from a new perspective that eliminates the need to
sequentially evaluate each neural architecture generated during algorithm
execution. Specifically, the proposed approach is built by learning the
knowledge of high-level experts in designing state-of-the-art architectures,
and then the new architecture is directly generated upon the knowledge learned.
We implemented the proposed approach by using a graph neural network for link
prediction and acquired the knowledge from NAS-Bench-101. Compared to existing
peer competitors, we found a competitive network with minimal cost. In
addition, we also utilized the learned knowledge from NAS-Bench-101 to automate
architecture design in the DARTS search space, and achieved 97.82% accuracy on
CIFAR10, and 76.51% top-1 accuracy on ImageNet consuming only $2\times10^{-4}$
GPU days. This also demonstrates the high transferability of the proposed
approach, and can potentially lead to a new, more computationally efficient
paradigm in this research direction.
