Neural Architecture Search (NAS) has shown great potentials in finding better
neural network designs. Sample-based NAS is the most reliable approach which
aims at exploring the search space and evaluating the most promising
architectures. However, it is computationally very costly. As a remedy, the
one-shot approach has emerged as a popular technique for accelerating NAS using
weight-sharing. However, due to the weight-sharing of vastly different
networks, the one-shot approach is less reliable than the sample-based
approach. In this work, we propose BONAS (Bayesian Optimized Neural
Architecture Search), a sample-based NAS framework which is accelerated using
weight-sharing to evaluate multiple related architectures simultaneously.
Specifically, we apply Graph Convolutional Network predictor as a surrogate
model for Bayesian Optimization to select multiple related candidate models in
each iteration. We then apply weight-sharing to train multiple candidate models
simultaneously. This approach not only accelerates the traditional sample-based
approach significantly, but also keeps its reliability. This is because
weight-sharing among related architectures are more reliable than those in the
one-shot approach. Extensive experiments are conducted to verify the
effectiveness of our method over many competing algorithms.

本篇论文介绍了一种名为 BONAS 的基于贝叶斯优化的神经架构搜索框架，该框架使用图卷积网络预测器作为代理模型以在每次迭代中选择多个相关的候选模型，并采用权重共享方法来同时训练多个候选模型。该方法不仅可以显著加速传统的基于采样的方法，而且可以保持其可靠性。