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Neural Architecture Search (NAS) has emerged as one of the effective methods to design the optimal neural network architecture automatically.
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2018
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N. Ma, X. Zhang, H.-T. Zheng, and J. Sun, “Shufflenet v2: Practical guidelines for efficient cnn architecture design,” in Proceedings of the European conference on computer vision , 2018, pp. 116–131
2018
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P. Ren, Y. Xiao, X. Chang, P.-Y. Huang, Z. Li, X. Chen, and X. Wang, “A comprehensive survey of neural architecture search: Challenges and solutions,” ACM Computing Surveys , vol. 54, no. 4, pp. 1–34, 2021
2021
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T. Zhang, C. Lei, Z. Zhang, X.-B. Meng, and C. P. Chen, “As-nas: Adaptive scalable neural architecture search with reinforced evolutionary algorithm for deep learning,” IEEE Transactions on Evolutionary Computation , vol. 25, no. 5, pp. 830–841, 2021
2021
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Y. Sun, X. Sun, Y. Fang, G. G. Yen, and Y. Liu, “A novel training protocol for performance predictors of evolutionary neural architecture search algorithms,” IEEE Transactions on Evolutionary Computation , vol. 25, no. 3, pp. 524–536, 2021
2021
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Y. Ci, C. Lin, M. Sun, B. Chen, H. Zhang, and W. Ouyang, “Evolving search space for neural architecture search,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 6659–6669
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H. Cai, T. Chen, W. Zhang, Y. Yu, and J. Wang, “Efficient architecture search by network transformation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 32, no. 1, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Y. Sun, B. Xue, M. Zhang, and G. G. Yen, “Evolving deep convolutional neural networks for image classification,” IEEE Transactions on Evolutionary Computation , vol. 24, no. 2, pp. 394–407, 2019
2019
Cited alongside, same era.
T. Elsken, J. H. Metzen, and F. Hutter, “Neural architecture search: A survey,” The Journal of Machine Learning Research , vol. 20, no. 1, pp. 1997–2017, 2019
2019
Cited alongside, same era.
H. Luo, W. Jiang, Y. Gu, F. Liu, X. Liao, S. Lai, and J. Gu, “A strong baseline and batch normalization neck for deep person re-identification,” IEEE Transactions on Multimedia , vol. 22, no. 10, pp. 2597–2609, 2019
2019
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M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International conference on machine learning . PMLR, 2019, pp. 6105–6114
2019
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 International Conference on Machine Learning . PMLR, 2019, pp. 7105–7114
2019
Cited alongside, same era.
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, 2019
2019
Cited alongside, same era.
2021
Later among the works it cites.
M. Ding, X. Lian, L. Yang, P. Wang, X. Jin, Z. Lu, and P. Luo, “Hr-nas: Searching efficient high-resolution neural architectures with lightweight transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 2982–2992
2021
Later among the works it cites.
B. Chen, P. Li, C. Li, B. Li, L. Bai, C. Lin, M. Sun, J. Yan, and W. Ouyang, “Glit: Neural architecture search for global and local image transformer,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 12–21
2021
Later among the works it cites.
Y. Sun, G. G. Yen, B. Xue, M. Zhang, and J. Lv, “Arctext: A unified text approach to describing convolutional neural network architectures,” IEEE Transactions on Artificial Intelligence , 2021
2021
Later among the works it cites.
J. Mellor, J. Turner, A. Storkey, and E. J. Crowley, “Neural architecture search without training,” in International Conference on Machine Learning . PMLR, 2021, pp. 7588–7598
2021
Later among the works it cites.
B. Chen, P. Li, B. Li, C. Lin, C. Li, M. Sun, J. Yan, and W. Ouyang, “Bn-nas: Neural architecture search with batch normalization,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 307–316
2021
Later among the works it cites.
A. E. Blanchard, M. C. Shekar, S. Gao, J. Gounley, I. Lyngaas, J. Glaser, and D. Bhowmik, “Automating genetic algorithm mutations for molecules using a masked language model,” IEEE Transactions on Evolutionary Computation , vol. 26, no. 4, pp. 793–799, 2022
2022
Later among the works it cites.
Y. Peng, A. Song, V. Ciesielski, H. M. Fayek, and X. Chang, “Pre-nas: Evolutionary neural architecture search with predictor,” IEEE Transactions on Evolutionary Computation , 2022
2022
Later among the works it cites.
C. Xue, M. Hu, X. Huang, and C.-G. Li, “Automated search space and search strategy selection for automl,” Pattern Recognition , vol. 124, p. 108474, 2022
2022
Later among the works it cites.
L. Zhang, S. Wang, F. Yuan, B. Geng, and M. Yang, “Lifelong language learning with adaptive uncertainty regularization,” Information Sciences , vol. 622, pp. 794–807, 2023
2023
Closest in time.
Y. Xue, C. Chen, and A. Słowik, “Neural architecture search based on a multi-objective evolutionary algorithm with probability stack,” IEEE Transactions on Evolutionary Computation , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
P. Bongini, F. Becattini, and A. Del Bimbo, “Is gpt-3 all you need for visual question answering in cultural heritage?” in Computer Vision–ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part I . Springer, 2023, pp. 268–281
2023
Closest in time.