과제정보
This work was supported by the Natural Science Foundation of Chongqing, China under Grant cstc2019jscx-mbdxX0021, and in part by the Major Industrial Technology Research and Development Project of Chongqing High-tech Industry (No. D2018-82).
참고문헌
- C. White, W. Neiswanger, and Y. Savani, "Bananas: Bayesian optimization with neural architectures for neural architecture search," in Proceedings of the 35th AAAI Conference on Artificial Intelligence, Virtual Event, 2021, pp. 10293-10301.
- M. Chatzianastasis, G. Dasoulas, G. Siolas, and M. Vazirgiannis, "Operation embeddings for neural architecture search," 2021 [Online]. Available: https://arxiv.org/abs/2105.04885.
- 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, vol. 97, pp. 7105-7114, 2019.
- X. Dong and Y. Yang, "Nas-bench-201: Extending the scope of reproducible neural architecture search," 2020 [Online]. Available: https://arxiv.org/abs/2001.00326.
- X. Zhang, Y. Zhang, Z. Zhang, and J. Liu, "Discriminative learning of imaginary data for few-shot classification," Neurocomputing, vol. 467, pp. 406-417, 2022. https://doi.org/10.1016/j.neucom.2021.09.070
- W. Wen, H. Liu, Y. Chen, H. Li, G. Bender, and P. J. Kindermans, "Neural predictor for neural architecture search," in Computer Vision - ECCV 2020. Cham, Switzerland: Springer, 2020, pp. 660-676.
- C. White, W. Neiswanger, S. Nolen, and Y. Savani, "A study on encodings for neural architecture search," Advances in Neural Information Processing Systems, vol. 33, pp. 20309-20319, 2020.
- H. Shi, P. Pi, H. Xu, Z. Li, J. Kwok, and T. Zhang, "Bridging the gap between sample-based and one-shot neural architecture search with BONAS," Advances in Neural Information Processing Systems, vol. 33, pp. 1808-1819, 2020.
- X. Ning, Y. Zheng, T. Zhao, Y. Wang, and H. Yang, "A generic graph-based neural architecture encoding scheme for predictor-based NAS," in Computer Vision - ECCV 2020. Cham, Switzerland: Springer, 2020, pp. 189-204.
- M. Zhang, S. Jiang, Z. Cui, R. Garnett, and Y. Chen, "D-VAE: a variational autoencoder for directed acyclic graphs," Advances in Neural Information Processing Systems, vol. 32, pp. 1586-1598, 2019.