Neural Architecture Search via Combinatorial Multi-Armed Bandit

Abstract

Neural Architecture Search (NAS) has gained significant popularity as an effective tool for designing high performance deep neural networks (DNNs). NAS can be performed via reinforcement learning, evolutionary algorithms, differentiable architecture search or tree-search methods. While significant progress has been made for both reinforcement learning and differentiable architecture search, tree-search methods have so far failed to achieve comparable accuracy or search efficiency. In this paper, we formulate NAS as a Combinatorial Multi-Armed Bandit (CMAB) problem (CMAB-NAS). This allows the decomposition of a large search space into smaller blocks where tree-search methods can be applied mo..