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Neural architecture search (NAS) has attracted a lot of attention and has been illustrated to bring tangible benefits in a large number of applications in the past few years.
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H. Pham, M. Guan, B. Zoph, Q. Le, and J. Dean, “Efficient neural architecture search via parameters sharing,” in Proc. Int. Conf. Machine Learning , 2018, pp. 4095–4104
2018
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B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning transferable architectures for scalable image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2018, pp. 8697–8710
2018
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G. Ghiasi, T.-Y. Lin, and Q. V. Le, “Dropblock: A regularization method for convolutional networks,” in Proc. Advances Neural Inf. Process. Syst. , 2018, pp. 10 727–10 737
2018
Cited alongside, same era.
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz, “mixup: Beyond empirical risk minimization,” in Proc. Int. Conf. Learn. Representations , 2018
2018
Cited alongside, same era.
2019
Later among the works it cites.
X. Dong and Y. Yang, “Network pruning via transformable architecture search,” in Proc. Advances Neural Inf. Process. Syst. , 2019, pp. 760–771
2019
Later among the works it cites.
C. Liu, L.-C. Chen, F. Schroff, H. Adam, W. Hua, A. L. Yuille, and L. Fei-Fei, “Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 82–92
2019
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L. Li and A. Talwalkar, “Random search and reproducibility for neural architecture search,” in The Conf. on Uncertainty in Artificial Intelligence , 2019
2019
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2018
Cited alongside, same era.
Y. He, J. Lin, Z. Liu, H. Wang, L.-J. Li, and S. Han, “AMC: Automl for model compression and acceleration on mobile devices,” in Proc. Eur. Conf. Comput. Vis. , 2018, pp. 784–800
2018
Cited alongside, same era.
S. Falkner, A. Klein, and F. Hutter, “BOHB: Robust and efficient hyperparameter optimization at scale,” in Proc. Int. Conf. Machine Learning , 2018, pp. 1436–1445
2018
Cited alongside, same era.
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar, “Hyperband: A novel bandit-based approach to hyperparameter optimization,” The Journal of Machine Learning Research (JMLR) , vol. 18, no. 1, pp. 6765–6816, 2018
2018
Cited alongside, same era.
B. Baker, O. Gupta, R. Raskar, and N. Naik, “Accelerating neural architecture search using performance prediction,” in Proc. Int. Conf. Learn. Representations Workshop , 2018
2018
Cited alongside, same era.
H. Cai, T. Chen, W. Zhang, Y. Yu, and J. Wang, “Efficient architecture search by network transformation,” in AAAI Conference on Artificial Intelligence (AAAI) , 2018, pp. 2787–2794
2018
Cited alongside, same era.
A. Brock, T. Lim, J. M. Ritchie, and N. Weston, “SMASH: one-shot model architecture search through hypernetworks,” in Proc. Int. Conf. Learn. Representations , 2018
2018
Cited alongside, same era.
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, “Regularized evolution for image classifier architecture search,” in AAAI Conference on Artificial Intelligence (AAAI) , 2019, pp. 4780–4789
2019
Cited alongside, same era.
J. Yu and T. S. Huang, “Universally slimmable networks and improved training techniques,” in Proc. IEEE Int. Conf. Comput. Vis. , 2019, pp. 1803–1811
2019
Later among the works it cites.
C. Zhang, M. Ren, and R. Urtasun, “Graph hypernetworks for neural architecture search,” in Proc. Int. Conf. Learn. Representations , 2019
2019
Later among the works it cites.
A. Zela, J. Siems, and F. Hutter, “NAS-BENCH-1SHOT1: Benchmarking and dissecting one shot neural architecture search,” in Proc. Int. Conf. Learn. Representations , 2020
2020
Closest in time.
Y. Shu, W. Wang, and S. Cai, “Understanding architectures learnt by cell-based neural architecture search,” in Proc. Int. Conf. Learn. Representations , 2020
2020
Closest in time.
2020
Closest in time.
M. Tan, R. Pang, and Q. V. Le, “EfficientDet: Scalable and efficient object detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2020, pp. 10 781–10 790
2020
Closest in time.
X. Dong and Y. Yang, “NAS-Bench-201: Extending the scope of reproducible neural architecture search,” in Proc. Int. Conf. Learn. Representations , 2020
2020
Closest in time.
K. Yu, C. Sciuto, M. Jaggi, C. Musat, and M. Salzmann, “Evaluating the search phase of neural architecture search,” in Proc. Int. Conf. Learn. Representations , 2020
2020
Closest in time.
H. Cai, C. Gan, and S. Han, “Once for all: Train one network and specialize it for efficient deployment,” in Proc. Int. Conf. Learn. Representations , 2020
2020
Closest in time.
A. Wan, X. Dai, P. Zhang, Z. He, Y. Tian, S. Xie, B. Wu, M. Yu, T. Xu, K. Chen et al. , “FBNetV2: Differentiable neural architecture search for spatial and channel dimensions,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2020, pp. 12 965–12 974
2020
Closest in time.
G. Bender, H. Liu, B. Chen, G. Chu, S. Cheng, P.-J. Kindermans, and Q. V. Le, “Can weight sharing outperform random architecture search? an investigation with tunas,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2020, pp. 14 323–14 332
2020
Closest in time.
2020
Closest in time.
D. Peng, X. Dong, E. Real, M. Tan, Y. Lu, G. Bender, H. Liu, A. Kraft, C. Liang, and Q. Le, “PyGlove: Symbolic programming for automated machine learning,” in Proc. Advances Neural Inf. Process. Syst. , 2020
2020
Closest in time.