MANAS: Multi-Agent Neural Architecture SearchDownload PDF

25 Sep 2019 (modified: 24 Dec 2019)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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  • Keywords: Neural Architecture Search, NAS, AutoML, Computer Vision
  • TL;DR: Scalable multi-agent formulation of neural architecture search
  • Abstract: 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 maximize 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 ($1/8$th of state-of-the-art), and performances above those of much more computationally expensive methods. Theoretically, we demonstrate vanishing regrets of the form $\mathcal{O}(\sqrt{T})$, 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 favorable results in comparison with this baseline and other competing methods.
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