The Neural Architecture Search (NAS) problem is typically formulated as a
graph search problem where the goal is to learn the optimal operations over
edges in order to maximise a graph-level global objective. Due to the large
architecture parameter space, efficiency is a key bottleneck preventing NAS
from its practical use. In this paper, we address the issue by framing NAS as a
multi-agent problem where agents control a subset of the network and coordinate
to reach optimal architectures. We provide two distinct lightweight
implementations, with reduced memory requirements (1/8th of state-of-the-art),
and performances above those of much more computationally expensive methods.
Theoretically, we demonstrate vanishing regrets of the form O(sqrt(T)), with T
being the total number of rounds. Finally, aware that random search is an,
often ignored, effective baseline we perform additional experiments on 3
alternative datasets and 2 network configurations, and achieve favourable
results in comparison.

本文提出了两种轻量级的神经架构搜索算法，并将 NAS 问题转化为多智能体问题以提高计算效率，理论上证明了 O (sqrt (T)) 的消失后悔率，实验结果在多个数据集和网络配置上表现优异，其中包括随机搜索。