EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture Search

J. Siems, L. Zimmer, A. Zela, J. Lukasik, M. Keuper, and F. Hutter, “NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search,” CoRR , vol. abs/2008.09777, 2020. [Online]. Available: https://arxiv.org/abs/2008.09777

Original

2008

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S. Falkner, A. Klein, and F. Hutter, “BOHB: Robust and Efficient Hyperparameter Optimization at Scale,” in Proceedings of the 35th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, J. G. Dy and A. Krause, Eds., vol. 80. PMLR, 2018, pp. 1436–1445

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C. Ying, A. Klein, E. Christiansen, E. Real, K. Murphy, and F. Hutter, “NAS-Bench-101: Towards Reproducible Neural Architecture Search,” in Proceedings of the 36th International Conference on Machine Learning, ICML , K. Chaudhuri and R. Salakhutdinov, Eds., vol. 97. PMLR, 2019, pp. 7105–7114

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M. Tan, B. Chen, R. Pang, V. Vasudevan, M. Sandler, A. Howard, and Q. V. Le, “Mnasnet: Platform-aware neural architecture search for mobile,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 2820–2828

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B. Wu, X. Dai, P. Zhang, Y. Wang, F. Sun, Y. Wu, Y. Tian, P. Vajda, Y. Jia, and K. Keutzer, “Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 734–10 742