FNAS: Uncertainty-Aware Fast Neural Architecture Search
Jihao Liu,
Ming Zhang,
Yangting Sun
et al.
Abstract:Reinforcement learning (RL)-based neural architecture search (NAS) generally guarantees better convergence yet suffers from the requirement of huge computational resources compared with gradient-based approaches, due to the rollout bottleneck -exhaustive training of each sampled architecture on the proxy tasks. In this paper, we propose a general pipeline to accelerate the convergence of the rollout process as well as the RL process in NAS. It is motivated by the interesting observation that both the architect… Show more
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