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『AI科技大本营』低耗时、高精度，微软提基于半监督学习的神经网络结构搜索算法( 四 )


更多详情请见论文原文：
Semi-Supervised Neural Architecture Search
论文链接：https://arxiv.org/abs/2002.10389
论文代码现已开源 。
GitHub链接：https://github.com/renqianluo/SemiNAS
参考文献
[1] Luo, Renqian, et al. "Neural architecture optimization." Advances in neural information processing systems. 2018.
[2] Ying, Chris, et al. "NAS-Bench-101: Towards Reproducible Neural Architecture Search." International Conference on Machine Learning. 2019.
[3] Zoph, Barret, et al. "Learning transferable architectures for scalable image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
[4] Real, Esteban, et al. "Regularized evolution for image classifier architecture search." Proceedings of the aaai conference on artificial intelligence. Vol. 33. 2019.
[5] Liu, Hanxiao, Karen Simonyan, and Yiming Yang. "DARTS: Differentiable Architecture Search." (2018).
[6] Pham, Hieu, et al. "Efficient Neural Architecture Search via Parameters Sharing." International Conference on Machine Learning. 2018.
[7] Guo, Zichao, et al. "Single path one-shot neural architecture search with uniform sampling." arXiv preprint arXiv:1904.00420 (2019).
[8] Li, Naihan, et al. "Neural speech synthesis with transformer network." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. 2019.
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