This paper addresses the efficiency challenge of Neural Architecture Search
(NAS) by formulating the task as a ranking problem. Previous methods require
numerous training examples to estimate the accurate performance of
architectures, although the actual goal is to find the distinction between
"good" and "bad" candidates. Here we do not resort to performance predictors.
Instead, we propose a performance ranking method (RankNAS) via pairwise
ranking. It enables efficient architecture search using much fewer training
examples. Moreover, we develop an architecture selection method to prune the
search space and concentrate on more promising candidates. Extensive
experiments on machine translation and language modeling tasks show that
RankNAS can design high-performance architectures while being orders of
magnitude faster than state-of-the-art NAS systems.

本文提出一种称之为 RankNAS 的性能排名方法，通过配对排名是为了提高神经架构搜索（NAS）的效率，并在机器翻译和语言建模任务中进行了广泛实验，结果表明 RankNAS 可以设计出高性能的架构，同时速度比现有的 NAS 系统快几个数量级。