Deep Neural Architecture Search with Deep Graph Bayesian Optimization

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Authors Lizheng Ma, Bo Yang, Jiaxu Cui
Journal/Conference Name Proceedings - 2019 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019
Paper Category
Paper Abstract Bayesian optimization (BO) is an effective method of finding the global optima of black-box functions. Recently BO has been applied to neural architecture search and shows better performance than pure evolutionary strategies. All these methods adopt Gaussian processes (GPs) as surrogate function, with the handcraft similarity metrics as input. In this work, we propose a Bayesian graph neural network as a new surrogate, which can automatically extract features from deep neural architectures, and use such learned features to fit and characterize black-box objectives and their uncertainty. Based on the new surrogate, we then develop a graph Bayesian optimization framework to address the challenging task of deep neural architecture search. Experiment results show our method significantly outperforms the comparative methods on benchmark tasks.
Date of publication 2019
Code Programming Language Python

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