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Neural architecture search (NAS) is a promising method for automatically design neural architectures.
C. M. Bishop, Pattern recognition and machine learning, 5th Edition , ser. Information science and statistics. Springer, 2007
2007
Earlier work this paper cites.
A. Krizhevsky, “Learning multiple layers of features from tiny images,” Tech. Rep., 2009
2009
Earlier work this paper cites.
D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, “The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains,” IEEE Signal Process. Mag. , vol. 30, no. 3, pp. 83–98, 2013
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas, “Taking the human out of the loop: A review of bayesian optimization,” Proceedings of the IEEE , vol. 104, pp. 148–175, 2016
2016
Earlier work this paper cites.
E. Real, S. Moore, A. Selle, S. Saxena, Y. L. Suematsu, J. Tan, Q. V. Le, and A. Kurakin, “Large-scale evolution of image classifiers,” in ICML , 2017
2017
Earlier work this paper cites.
B. Zoph and Q. V. Le, “Neural architecture search with reinforcement learning,” in International Conference on Learning Representations (ICLR) , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning transferable architectures for scalable image recognition,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2017, pp. 8697–8710
2017
Earlier work this paper cites.
D. Russo, B. V. Roy, A. Kazerouni, and I. Osband, “A tutorial on thompson sampling,” Foundations and Trends in Machine Learning , vol. 11, pp. 1–96, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in CACM , 2017
2017
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “Sgdr: Stochastic gradient descent with warm restarts,” in ICLR , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
B. Baker, O. Gupta, R. Raskar, and N. Naik, “Accelerating neural architecture search using performance prediction,” in 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Workshop Track Proceedings . OpenReview.net, 2018
2018
Cited alongside, same era.
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean, “Efficient neural architecture search via parameter sharing,” in ICML , 2018
2018
Cited alongside, same era.
C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy, “Progressive neural architecture search,” in Computer Vision – ECCV 2018 , V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss, Eds. Springer International Publishing, 2018, pp. 19–35
2018
Cited alongside, same era.
T. Elsken, J. H. Metzen, and F. Hutter, “Neural architecture search: A survey,” Journal of Machine Learning research , vol. 20, pp. 55:1–55:21, 2018
2018
Cited alongside, same era.
X. Chen, L. Xie, J. Wu, and Q. Tian, “Progressive differentiable architecture search: Bridging the depth gap between search and evaluation,” 2019 IEEE/CVF International Conference on Computer Vision (ICCV) , pp. 1294–1303, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in NeurIPS , 2019
2019
Later among the works it cites.
M. Fey and J. E. Lenssen, “Fast graph representation learning with PyTorch Geometric,” in ICLR Workshop on Representation Learning on Graphs and Manifolds , 2019
2019
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P. I. Frazier, “A tutorial on bayesian optimization,” CoRR , vol. abs/1807.02811, 2018
2018
Cited alongside, same era.
R. Luo, F. Tian, T. Qin, and T.-Y. Liu, “Neural architecture optimization,” in NeurIPS , 2018
2018
Cited alongside, same era.
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 , ser. Proceedings of Machine Learning Research, K. Chaudhuri and R. Salakhutdinov, Eds., vol. 97. PMLR, 09–15 Jun 2019, pp. 7105–7114
2019
Cited alongside, same era.
2019
Cited alongside, same era.
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, “Regularized evolution for image classifier architecture search,” in AAAI , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
H. Liu, K. Simonyan, and Y. Yang, “DARTS: Differentiable architecture search,” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
H. Zhou, M. Yang, J. Wang, and W. Pan, “Bayesnas: A bayesian approach for neural architecture search,” in ICML , 2019
2019
Cited alongside, same era.
Later among the works it cites.
2020
Closest in time.
Y. Sun, B. Xue, M. Zhang, G. G. Yen, and J. Lv, “Automatically designing cnn architectures using the genetic algorithm for image classification,” IEEE Transactions on Cybernetics , pp. 1–15, 2020
2020
Closest in time.
Y. Sun, B. Xue, M. Zhang, and G. G. Yen, “Completely automated cnn architecture design based on blocks,” IEEE Transactions on Neural Networks and Learning Systems , vol. 31, no. 4, pp. 1242–1254, 2020
2020
Closest in time.
——, “Evolving deep convolutional neural networks for image classification,” IEEE Transactions on Evolutionary Computation , vol. 24, no. 2, pp. 394–407, 2020
2020
Closest in time.
X. Dong and Y. Yang, “Nas-bench-201: Extending the scope of reproducible neural architecture search,” in 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net, 2020
2020
Closest in time.
L. Wang, Y. Zhao, Y. Jinnai, Y. Tian, and R. Fonseca, “Neural architecture search using deep neural networks and monte carlo tree search,” in The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020 . AAAI Press, 2020, pp. 9983–9991
2020
Closest in time.
X. Ning, Y. Zheng, T. Zhao, Y. Wang, and H. Yang, “A generic graph-based neural architecture encoding scheme for predictor-based NAS,” in ECCV , 2020
2020
Closest in time.
Y. Xu, L. Xie, X. Zhang, X. Chen, G.-J. Qi, Q. Tian, and H. Xiong, “PC-DARTS: Partial channel connections for memory-efficient architecture search,” in International Conference on Learning 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 International Conference on Learning Representations , 2020
2020
Closest in time.
C. Wei. (2020) Code for NPENAS. [Online]. Available: https://github.com/auroua/NPENASv1
2020
Closest in time.
K. Kandasamy, W. Neiswanger, J. Schneider, B. Poczos, and E. P. Xing, “Neural architecture search with bayesian optimisation and optimal transport,” in Advances in Neural Information Processing Systems , 2018, pp. 2016–2025
2025
Closest in time.