Papers › Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search

Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search

18 Jul 2018arXiv:1807.06906archive 2025-07-28

Arber Zela, Aaron Klein, Stefan Falkner, Frank Hutter

While existing work on neural architecture search (NAS) tunes hyperparameters in a separate post-processing step, we demonstrate that architectural choices and other hyperparameter settings interact in a way that can render this separation suboptimal. Likewise, we demonstrate that the common practice of using very few epochs during the main NAS and much larger numbers of epochs during a post-processing step is inefficient due to little correlation in the relative rankings for these two training regimes. To combat both of these problems, we propose to use a recent combination of Bayesian optimization and Hyperband for efficient joint neural architecture and hyperparameter search.

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