期刊
MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE, LOD 2022, PT II
卷 13811, 期 -, 页码 1-16出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-031-25891-6_1
关键词
Performance prediction; Neural architecture search; Graph neural networks
Neural Architecture Search can find high-performance task specific neural network architectures. Using surrogate models as performance predictors can reduce the need for costly evaluations. Our deep graph learning approach achieves state-of-the-art performance in multiple NAS performance prediction benchmarks.
Neural Architecture Search can help in finding high-performance task specific neural network architectures. However, the training of architectures that is required for fitness computation can be prohibitively expensive. Employing surrogate models as performance predictors can reduce or remove the need for these costly evaluations. We present a deep graph learning approach that achieves state-of-the-art performance in multiple NAS performance prediction benchmarks. In contrast to other methods, this model is purely supervised, which can be a methodologic advantage, as it does not rely on unlabeled instances sampled from complex search spaces.
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