  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.
