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Our approach uses\na sequential model-based optimization (SMBO) strategy, in which we search for\nstructures in order of increasing complexity, while simultaneously learning a\nsurrogate model to guide the search through structure space. Direct comparison\nunder the same search space shows that our method is up to 5 times more\nefficient than the RL method of Zoph et al. (2018) in terms of number of models\nevaluated, and 8 times faster in terms of total compute. The structures we\ndiscover in this way achieve state of the art classification accuracies on\nCIFAR-10 and ImageNet.","url_abs":"http://arxiv.org/abs/1712.00559v3","url_pdf":"http://arxiv.org/pdf/1712.00559v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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