Title: Differentiable Graph Optimization for Neural Architecture Search. Abstract: In this paper, we propose Graph Optimized Neural Architecture Learning (GOAL), a novel gradient-based method for Neural Architecture Search (NAS), to find better architectures with fewer evaluated samples. Popular NAS methods usually employ black-box optimization based approaches like reinforcement learning, evolution algorithm or Bayesian optimization, which may be inefficient when having huge combinatorial NAS search spaces. In contrast, we aim to explicitly model the NAS search space as graphs, and then perform gradient-based optimization to learn graph structure with efficient exploitation. To this end, we learn a differentiable graph neural network as a surrogate model to rank candidate architectures, which enable us to obtain gradient w.r.t the input architectures. To cope with the difficulty in gradient-based optimization on the discrete graph structures, we propose to leverage proximal gradient descent to find potentially better architectures.
Our empirical results show that GOAL outperforms mainstream black-box methods on existing NAS benchmarks in terms of search efficiency.