[1] ZOPH B,VASUDEVAN V,SHLENS J,et al.Learning transferable architectures for scalable image recognition[EB/OL].[2020-05-20].https://openaccess.thecvf.com/content_cvpr_2018/papers/Zoph_Learning_Transferable_Architectures_CVPR_2018_paper.pdf. [2] PHAM H,GUAN M,ZOPH B,et al.Efficient neural architecture search via parameter sharing[EB/OL].[2020-05-20].https://arxiv.org/abs/1802.03268. [3] CAI Han,CHEN Tianyao,ZHANG Wennan,et al.Efficient architecture search by network transformation[C]//Proceed-ings of the 32nd Conference on Artificial Intelligence.Washington D.C.,USA:IEEE Press,2018:151-163. [4] LIU C,ZOPH B,SHLENS J,et al.Progressive neural architecture search[EB/OL].[2020-05-20].https://arxiv.org/pdf/1712.00559.pdf. [5] BAKER B,GUPTA O,NAIK N,et al.Designing neural network architectures using reinforcement learning[EB/OL].[2020-05-20].https://arxiv.org/abs/1611.02167. [6] ZHONG Zhao,YAN Junjie,WU Wei,et al.Practical block-wise neural network architecture generation[EB/OL].[2020-05-20].https://arxiv.org/abs/1708.05552. [7] LIU Yong,YAO Xin.A population-based learning algorithm which learns both architectures and weights of neural networks[J].Chinese Journal of Advanced Software Research,1996,3(1):54-65. [8] PIERGIOVANNI A,ANGELOVA A,TOSHEV A,et al.Evolving space-time neural architectures for videos[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2019:16-28. [9] DENG Ningyi,SHEN Zhiqiang,GUO Yuefei.Significance detection based on category prior and deep neural network features[J].Computer Engineering,2017,43(6):225-229.(in Chinese) 邓凝旖,沈志强,郭跃飞.基于类别先验与深度神经网络特征的显著性检测[J].计算机工程,2017,43(6):225-229. [10] VAHDAT A,MALLYA A,LIU M Y,et al.UNAS:differentiable architecture search meets reinforcement learning[EB/OL].[2020-05-20].https://arxiv.org/abs/1912.07651. [11] KANDASAMY K,NEISWANGER W,SCHNEIDER J,et al.Neural architecture search with Bayesian optimisa-tion and optimal transport[EB/OL].[2020-05-20].https://arxiv.org/abs/1802.07191. [12] VOGELSTEIN J T,CONROY J M,LYZINSKI V,et al.Fast approximate quadratic programming for graph matching[J].PLoS One,2015,10(4):1-17. [13] SINGH R,XU J,BERGER B.Global alignment of multiple protein interaction networks with application to functional orthology detection[J].Proceedings of the National Academy of Sciences of the United States of America,2008,105(35):12763-12768. [14] LIAO C S,LU K,BAYM M,et al.IsoRankN:spectral methods for global alignment of multiple protein networks[J].Bioinformatics,2009,25(12):1253-1258. [15] KOLLIAS G,MOHAMMADI S,GRAMA A.Network Similarity Decomposition(NSD):a fast and scalable approach to network alignment[J].IEEE Transactions on Knowledge and Data Engineering,2012,24(12):2232-2243. [16] GLIGORIJEVI V,MALOD-DOGNIN N,PRŽULJ N.Fuse:multiple network alignment via data fusion[J].Bioinformatics,2016,32(8):1195-1203. [17] BAYATI M,GERRITSEN M,GLEICH D F,et al.Algorithms for large,sparse network alignment problems[C]//Proceedings of 2009 IEEE International Conference on Data Mining.Washington D.C.,USA:IEEE Press,2009:705-710. [18] KOUTRA D,TONG H H,LUBENSKY D.BIG-ALIGN:fast bipartite graph alignment[C]//Proceedings of 2013 IEEE International Conference on Data Mining.Washington D.C.,USA:IEEE Press,2013:389-398. [19] BAYATI M,SHAH D,SHARMA M.Maximum weight matching via max-product belief propagation[J].IEEE Transactions on Information Theory,2005,54(3):1763-1767. [20] MALMI E,CHAWLA S,GIONIS A.Lagrangian relaxations for multiple network alignment[J].Data Mining and Knowledge Discovery,2017,31(5):1331-1358. [21] ZHANG Si,TONG Hanghang.Final:fast attributed network alignment[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York,USA:ACM Press,2016:1224-1237. [22] HEIMANN M,SHEN H M,SAFAVI T,et al.REGAL:representation learning-based graph alignment[EB/OL].[2020-05-20].https://arxiv.org/abs/1802.06257. [23] DRINEAS P,MAHONEY M W.Approximating a gram matrix for improved kernel-based learning[M].Berlin,Germany:Springer,2005. |