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2652 Journal of Software 软件学报 Vol.31, No.9, September 2020

[65] Pham H, Guan MY, Zoph B, Le QV, Dean J. Efficient neural architecture search via parameter sharing. In: Proc. of the 35th Int’l
Conf. on Machine Learning. 2018. 4092−4101.
[66] Elsken T, Metzen JH, Hutter F. Efficient multi-objective neural architecture search via lamarckian evolution. In: Proc. of the 7th
Int’l Conf. on Learning Representations. 2019.
[67] Cai H, Yang J, Zhang W, Han S, Yu Y. Path-Level network transformation for efficient architecture search. In: Proc. of the 35th
Int’l Conf. on Machine Learning. 2018. 677−686.
[68] Liu H, Simonyan K, Yang Y. DARTS: Differentiable architecture search. In: Proc. of the 7th Int’l Conf. on Learning
Representations. 2019.
[69] Zhong Z, Yan J, Wu W, Shao J, Liu CL. Practical block-wise neural network architecture generation. In: Proc. of the IEEE Conf.
on Computer Vision and Pattern Recognition. 2018. 2423−2432.
[70] Dong JD, Cheng AC, Juan DC, Wei W, Sun M. Dpp-Net: Device-aware progressive search for pareto-optimal neural architectures.
In: Proc. of the European Conf. on Computer Vision. 2018. 517−531.
[71] Zhong Z, Yan J, Wu W, Shao J, Liu CL. Practical block-wise neural network architecture generation. In: Proc. of the IEEE Conf.
on Computer Vision and Pattern Recognition. 2018. 2423−2432.
[72] Chen T, Goodfellow I, Shlens J. Net2net: Accelerating learning via knowledge transfer. In: Proc. of the 4th Int’l Conf. on Learning
Representations. 2016.
[73] Goldberg DE, Deb K. A comparative analysis of selection schemes used in genetic algorithms. In: Proc. of the Foundations of
Genetic Algorithms. Elsevier. 1991. 69−93.
[74] Cubuk ED, Zoph B, Schoenholz SS, Le QV. Intriguing properties of adversarial examples. In: Proc. of the 6th Int’l Conf. on
Learning Representations Workshop. 2018.
[75] Liu H, Simonyan K, Vinyals O, Fernando C, Kavukcuoglu K. Hierarchical representations for efficient architecture search. In: Proc.
of the 6th Int’l Conf. on Learning Representations. 2018.
[76] Elsken T, Metzen JH, Hutter F. Simple and efficient architecture search for convolutional neural networks. In: Proc. of the 6th Int’l
Conf. on Learning Representations Workshop. 2018.
[77] Wistuba M. Deep learning architecture search by neuro-cell-based evolution with function-preserving mutations. In: Proc. of the
Joint European Conf. on Machine Learning and Knowledge Discovery in Databases. 2018. 243−258.
[78] Real E, Moore S, Selle A, Saxena S, Suematsu YL, Tan J, Le QV, Kurakin A. Large-scale evolution of image classifiers. In: Proc.
of the 34th Int’l Conf. on Machine Learning, Vol.70. 2017. 2902−2911.
[79] Xie L, Yuille A. Genetic CNN. In: Proc. of the IEEE Int’l Conf. on Computer Vision. 2017. 1379−1388.
[80] Kandasamy K, Neiswanger W, Schneider J, Póczos B, Xing EP. Neural architecture search with bayesian optimisation and optimal
transport. In: Proc. of the Advances in Neural Information Processing Systems. 2018. 2016−2025.
[81] Luo R, Tian F, Qin T, Chen E, Liu TY. Neural architecture optimization. In: Proc. of the Advances in Neural Information
Processing Systems. 2018. 7816−7827.
[82] Bender G, Kindermans PJ, Zoph B, Vasudenvan V, Le QV. Understanding and simplifying one-shot architecture search. In: Proc.
of the Int’l Conf. on Machine Learning. 2018. 549−558.
[83] Brock A, Lim T, Ritchie JM, Weston N. SMASH: One-shot model architecture search through HyperNetworks. In: Proc. of the 6th
Int’l Conf. on Learning Representations. 2018.
[84] Zhang C, Ren M, Urtasun R. Graph HyperNetworks for neural architecture search. In: Proc. of the 7th Int’l Conf. on Learning
Representations. 2019.
[85] Real E, Aggarwal A, Huang Y, Le QV. Regularized evolution for image classifier architecture search. In: Proc. of the AAAI Conf.
on Artificial Intelligence, Vol.33. 2019. 4780−4789.
[86] Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein MS, Berg AC, Li FF.
Imagenet large scale visual recognition challenge. Int’l Journal of Computer Vision, 2015,115(3):211−252.
[87] Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL. Microsoft COCO: Common objects in context.
In: Proc. of the European Conf. on Computer Vision. 2014. 740−755.
[88] Zela A, Klein A, Falkner S, Frank H. Towards automated deep learning: Efficient joint neural architecture and hyperparameter
search. In: Proc. of the 21th Int’l Conf. on Artificial Intelligence and Statistics Workshop. 2018.
[89] Klein A, Falkner S, Bartels S, Hennig P, Hutter F. Fast Bayesian optimization of machine learning hyperparameters on large
datasets. In: Proc. of the 20th Int’l Conf. on Artificial Intelligence and Statistics. 2017. 528−536.