  Neural architecture search (NAS) is a promising research direction that has
the potential to replace expert-designed networks with learned, task-specific
architectures. In this work, in order to help ground the empirical results in
this field, we propose new NAS baselines that build off the following
observations: (i) NAS is a specialized hyperparameter optimization problem; and
(ii) random search is a competitive baseline for hyperparameter optimization.
Leveraging these observations, we evaluate both random search with
early-stopping and a novel random search with weight-sharing algorithm on two
standard NAS benchmarks---PTB and CIFAR-10. Our results show that random search
with early-stopping is a competitive NAS baseline, e.g., it performs at least
as well as ENAS, a leading NAS method, on both benchmarks. Additionally, random
search with weight-sharing outperforms random search with early-stopping,
achieving a state-of-the-art NAS result on PTB and a highly competitive result
on CIFAR-10. Finally, we explore the existing reproducibility issues of
published NAS results. We note the lack of source material needed to exactly
reproduce these results, and further discuss the robustness of published
results given the various sources of variability in NAS experimental setups.
Relatedly, we provide all information (code, random seeds, documentation)
needed to exactly reproduce our results, and report our random search with
weight-sharing results for each benchmark on multiple runs.
