Page 74 - 水利学报2021年第52卷第5期
P. 74

/
                [ 13] TURHAN C G,BILGE H S,IEEE . Recent Trends in Deep Generative Models:a Review[C]/International Con⁃
                       ference on Computer Science and Engineering,2018 .
                [ 14] PAPAMAKARIOS G,NALISNICK E,JIMENEZ REZENDE D,et al . Normalizing Flows for Probabilistic Model⁃
                       ing and Inference[J/OL]. 2019,[2021-1-29] https://arxiv.org/abs/1912.02762/html .
                                                        .
                [ 15] KOBYZEV I,PRINCE S J D,BRUBAKER M A . Normalizing Flows:An Introduction and Review of Current
                       Methods[Z]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2020 .
                [ 16] ZHANG T-F,TILKE P,DUPONT E,et al . Generating geologically realistic 3D reservoir facies models using
                       deep learning of sedimentary architecture with generative adversarial networks[J]. Petroleum Science,2019,16
                      (3):541-549 .
                [ 17] BERGEN K J,JOHNSON P A,DE HOOP M V,et al . Machine learning for data-driven discovery in solid Earth
                       geoscience[J]. Science,2019,363(6433):1299 .
                [ 18] PRABHAKARAN R,BRUNA P-O,BERTOTTI G,et al . An automated fracture trace detection technique using
                       the complex shearlet transform[J]. Solid Earth,2019,10(6):2137-2166 .
                                                                                                     /
                [ 19] PAPAMAKARIOS G,PAVLAKOU T,MURRAY I . Masked Autoregressive Flow for Density Estimation[C]/Ad⁃
                       vances in Neural Information Processing Systems,2017 .
                [ 20] KINGMA D,SALIMANS T,JOZEFOWICZ R,et al . Improved Variational Inference with Inverse Autoregressive
                             /
                       Flow[C]/Advances in Neural Information Processing Systems,2016 .
                [ 21] PAPAMAKARIOS G . Neural Density Estimation and Likelihood-free Inference[D]. Edinburgh:University of
                       Edinburgh,2019 .
                                                                                           /
                [ 22] KINGMA D P,DHARIWAL P . Glow:Generative Flow with Invertible 1 x 1 Convolutions[C]/Advances in Neu⁃
                       ral Information Processing Systems,2018 .
                [ 23] PRENGER R,VALLE R,CATANZARO B . Waveglow:A Flow-based Generative Network for Speech Synthesis
                      [C]/proceedings of the International Conference on Acoustics Speech and Signal Processing,2019 .
                          /
                                                                                                       /
                [ 24] TRAN D,VAFA K,AGRAWAL K K,et al . Discrete Flows:Invertible Generative Models of Discrete Data[C]/
                       Advances in Neural Information Processing Systems,2019 .
                [ 25] RODRIGUEZ A,LAIO A . Clustering by fast search and find of density peaks[J]. Science,2014,344(6191):
                       1492-1496 .
                [ 26] 陈叶旺,申莲莲,钟才明,等 . 密度峰值聚类算法综述[J]. 计算机研究与发展,2020,57(2):378-394 .
                [ 27] 岳攀,钟登华,吴含,等 . 基于 LHS 的坝基岩体三维裂隙网络模拟[J]. 水力发电学报,2016,35(10):
                       93-102 .
                [ 28] 岳攀 . 基于不确定性分析的水电工程地质构造建模理论与方法研究[D]. 天津:天津大学,2016 .
                                                                                                     /
                [ 29] GERMAIN M,GREGOR K,MURRAY I,et al . MADE:Masked Autoencoder for Distribution Estimation[C]/In⁃
                       ternational Conference on Machine Learning,2015 .
                [ 30] DILOKTHANAKUL N,MEDIANO P A M,GARNELO M,et al . Deep Unsupervised Clustering with Gaussian
                                                    .
                                                                    .
                       Mixture Variational Autoencoders[J/OL] 2017,[2021-1-29] https://arxiv.org/abs/1611.02648/html .
                [ 31] IZMAILOV P,KIRICHENKO P,FINZI M, et al . Semi-Supervised Learning with Normalizing Flows[J/OL] .
                                     .
                       2019,[2021-1-29] https://arxiv.org/abs/1912.13025/html .
                                                                                                 /
                [ 32] NALISNICK E,HERTEL L,SMYTH P . Approximate inference for deep latent gaussian mixtures[C]/Proceed⁃
                       ings of the NIPS . Barcelona,2016 .
                [ 33] KENNEDY J,EBERHART R C . Particle swarm optimization[C]/International Conference on Networks,2002 .
                                                                      /
                [ 34] SHANLEY R J,MAHTAB M A . Delineation and analysis of clusters in orientation data[J]. Journal of the Inter⁃
                       national Association for Mathematical Geology,1976,8(1):9-23 .
                                                                                              /
                [ 35] ARJOVSKY M,CHINTALA S,BOTTOU L . Wasserstein Generative Adversarial Networks[C]/International
                       Conference on Machine Learning,2017 .
                [ 36] XU B,PANG R,ZHOU Y,et al . Verification of stochastic seismic analysis method and seismic performance
                       evaluation based on multi-indices for high CFRDs[J]. Engineering Geology,2020,264:105412 .
                [ 37] 钟 登 华 ,关 涛 ,任 炳 昱 . 基 于 改 进 重 抽 样 法 的 高 拱 坝 施 工 进 度 仿 真 研 究[J]. 水 利 学 报 ,2016,47(4):
                       473-482 .
                [ 38] ZHANG Y,YUE P,ZHANG G,et al . Augmented reality mapping of rock mass discontinuities and rockfall sus⁃
                       ceptibility based on unmanned aerial vehicle photogrammetry[J]. Remote Sensing,2019,11(11):1-34 .

                 — 576  —
   69   70   71   72   73   74   75   76   77   78   79