正在加载图片...
第5期 刘帅师,等:深度学习方法研究新进展 ·575· Tong University,2011 Artificial Neural Networks,Part I.Berlin Heidelberg,Ger- [18]LAROCHELLE H,BENGIO Y.Classification using dis- many,2011:52.59. criminative restricted Boltzmann machines [Cl//Proceed- [29]王雅思.深度学习中的自编码器的表达能力研究[D] ings of the 25th International Conference on Machine 哈尔滨:哈尔滨工业大学,2014。 Learning.New York,NY,USA,2008:536-543 WANG Yasi.Representation ability research of auto-en- [19]张春霞,姬楠楠,王冠伟.受限波尔兹曼机[J].工程数 coders in deep learning[D].Harbin:Harbin Institute of 学学报,2015,32(2):159-173 Technology,2014. ZHANG Chunxia,JI Nannan,WANG Guanwei.Restricted [30]李远豪.基于深度自编码器的人脸美丽吸引力预测研 Boltzmann machines[J].Chinese journal of engineering 究[D].江门:五色大学,2014 mathematics,2015,32(2):159-173 LI Yuanhao.A study for facial beauty attractiveness predic- [20]刘银华.LBP和深度信念网络在非限制条件下人脸识 tion based on deep autoencoder[D].Jiangmen:Wuyi Uni- 别研究[D].江门:五邑大学,2014. versity,2014. LIU Yinhua.The research of face recognition under uncon- [31]林洲汉.基于自动编码机的高光谱图像特征提取及分 strained condition via LBP and deep belief network [D]. 类方法研究[D].哈尔滨:哈尔滨工业大学,2014. Jiangmen:Wuyi University,2014. LIN Zhouhan.Hyperspectral image feature extraction and [21]LEE H,GROSSE R,RANGANATH R,et al.Unsuper- classification based on autoencoders[D].Harbin:Harbin vised learning of hierarchical representations with convolu- Institute of Technology,2014. tional deep belief networks [J].Communications of the [32]曲建岭,杜辰飞,邸亚洲,等.深度自动编码器的研究 ACM.2011,54(10):95-103. 与展望[J].计算机与现代化.2014(8):128-134. [22]HALKIAS X C,PARIS S,GLOTIN H.Sparse penalty in QU Jianling,DU Chenfei,DI Yazhou,et al.Research and deep belief networks:using the mixed norm constraint prospect of deep auto-encoders[J].Jisuanji yu xiandaihua, [EB/oL].[2014-05-08].http://axiv.org/pdf/1301. 2014(8):128-134. 3533.pdf. [33]林少飞,盛惠兴,李庆武.基于堆叠稀疏自动编码器的 [23]LIU Yan,ZHOU Shusen,CHEN Qingcai.Discriminative 手写数字分类[J].微处理机,2015(1):47-51. deep belief networks for visual data classification[]].Pat- LIN Shaofei,SHENG Huixing,LI Qingwu.Handwritten tern recognition,2011,44(10/11):2287-2296. digital classification based on the stacked sparse autoencod- [24]郑胤,陈权崎,章毓晋.深度学习及其在目标和行为识 ers[J].Microprocessors,2015(1):47-51. 别中的新进展[J].中国图象图形学报,2014,19(2): [34]陈硕.深度学习神经网络在语音识别中的应用研究 175-184. [D].广州:华南理工大学,2013. ZHENG Yin,CHEN Quanqi,ZHANG Yujin.Deep learn- CHEN Shuo.Research of deep learning neural networks ing and its new progress in object and behavior recognition applications in speech recognition[D].Guangzhou,Chi- [J].Journal of image and graphics,2014,19(2):175- na:South China University of Technology,2013. 184. [35]郭丽丽,丁世飞.深度学习研究进展[J刀].计算机科学, [25]VINCENT P,LAROCHELLE H,BENGIO Y,et al.Ex- 2015,42(5):28-33. tracting and composing robust features with denoising au- GOU Lili,DING Shifei.Research progress on deep learn- toencoders [C]//Proceedings of the 25th International ing[J].Computer science,2015,42(5):28-33. Conference on Machine Learning.New York,NY,USA, [36]VAN DEN OORD A,DIELEMAN S,SCHRAUWEN B. 2008:1096-1103. Deep content-based music recommendation M]//Ad- 26]BENGIO Y,LAMBLIN P,POPOVICI D,et al.Greedy vances in Neural Information Processing Systems 26:27th layer-wise training of deep networks [C]//Advances in Annual Conference on Neural Information Processing Sys- Neural Information Processing Systems 19:20th Annual tems.Lake Tahoe,2013:2643-2651. Conference on Neural Information Processing Systems. [37]HANNUN A,CASE C,CASPER J,et al.Deep speech: Vancouver,British Columbia,Canada,2006:153-160. scaling up end-to-end speech recognition[EB/OL].Eprint [27]RIFAI S,VINCENT P,MULLER X,et al.Contractive Arxiv:Arxiv,2014.[2014-12-19]https://arxiv.org/pdf/ auto-encoders:explicit invariance during feature extraction 1412.5567v2.pdf. [C]//Proceedings of the 28th Intemational Conference on [38]余凯,贾磊,陈雨强.深度学习的昨天、今天和明天 Machine Learning.Bellevue,WA,USA,2011. [J].计算机研究与发展,2013,50(9):1799-1804. [28]MASCI J,MEIER U,CIRESAN D,et al.Stacked convo- YU Kai,JIA Lei,CHEN Yuqiang.Deep learning:yester- lutional auto-encoders for hierarchical feature extraction day,today,and tomorrow[J ]Journal of computer re- [C]//Proceedings of the 21st International Conference on search and development,2013,50(9):1799-1804Tong University, 2011. [18] LAROCHELLE H, BENGIO Y. Classification using dis⁃ criminative restricted Boltzmann machines[C] / / Proceed⁃ ings of the 25th International Conference on Machine Learning. New York, NY, USA, 2008: 536⁃543. [19]张春霞, 姬楠楠, 王冠伟. 受限波尔兹曼机[J]. 工程数 学学报, 2015, 32(2): 159⁃173. ZHANG Chunxia, JI Nannan, WANG Guanwei. Restricted Boltzmann machines [ J]. Chinese journal of engineering mathematics, 2015, 32(2): 159⁃173. [20]刘银华. LBP 和深度信念网络在非限制条件下人脸识 别研究[D]. 江门: 五邑大学, 2014. LIU Yinhua. The research of face recognition under uncon⁃ strained condition via LBP and deep belief network [ D]. Jiangmen: Wuyi University, 2014. [21]LEE H, GROSSE R, RANGANATH R, et al. Unsuper⁃ vised learning of hierarchical representations with convolu⁃ tional deep belief networks [ J]. Communications of the ACM, 2011, 54(10): 95⁃103. [22]HALKIAS X C, PARIS S, GLOTIN H. Sparse penalty in deep belief networks: using the mixed norm constraint [EB/ OL ]. [ 2014⁃05⁃08 ]. http: / / arxiv. org / pdf / 1301. 3533.pdf. [23]LIU Yan, ZHOU Shusen, CHEN Qingcai. Discriminative deep belief networks for visual data classification[ J]. Pat⁃ tern recognition, 2011, 44(10 / 11): 2287⁃2296. [24]郑胤, 陈权崎, 章毓晋. 深度学习及其在目标和行为识 别中的新进展[ J]. 中国图象图形学报, 2014, 19(2): 175⁃184. ZHENG Yin, CHEN Quanqi, ZHANG Yujin. Deep learn⁃ ing and its new progress in object and behavior recognition [J]. Journal of image and graphics, 2014, 19( 2): 175⁃ 184. [25] VINCENT P, LAROCHELLE H, BENGIO Y, et al. Ex⁃ tracting and composing robust features with denoising au⁃ toencoders [ C ] / / Proceedings of the 25th International Conference on Machine Learning. New York, NY, USA, 2008: 1096⁃1103. [26] BENGIO Y, LAMBLIN P, POPOVICI D, et al. Greedy layer⁃wise training of deep networks [ C] / / Advances in Neural Information Processing Systems 19: 20th Annual Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada, 2006: 153⁃160. [27] RIFAI S, VINCENT P, MULLER X, et al. Contractive auto⁃encoders: explicit invariance during feature extraction [C] / / Proceedings of the 28th International Conference on Machine Learning. Bellevue, WA, USA, 2011. [28]MASCI J, MEIER U, CIREŞAN D, et al. Stacked convo⁃ lutional auto⁃encoders for hierarchical feature extraction [C] / / Proceedings of the 21st International Conference on Artificial Neural Networks, Part I. Berlin Heidelberg, Ger⁃ many, 2011: 52⁃59. [29]王雅思. 深度学习中的自编码器的表达能力研究[D]. 哈尔滨: 哈尔滨工业大学, 2014. WANG Yasi. Representation ability research of auto⁃en⁃ coders in deep learning[D]. Harbin: Harbin Institute of Technology, 2014. [30]李远豪. 基于深度自编码器的人脸美丽吸引力预测研 究[D]. 江门: 五邑大学, 2014. LI Yuanhao. A study for facial beauty attractiveness predic⁃ tion based on deep autoencoder[D]. Jiangmen: Wuyi Uni⁃ versity, 2014. [31]林洲汉. 基于自动编码机的高光谱图像特征提取及分 类方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2014. LIN Zhouhan. Hyperspectral image feature extraction and classification based on autoencoders[D]. Harbin: Harbin Institute of Technology, 2014. [32]曲建岭, 杜辰飞, 邸亚洲, 等. 深度自动编码器的研究 与展望[J]. 计算机与现代化, 2014(8): 128⁃134. QU Jianling, DU Chenfei, DI Yazhou, et al. Research and prospect of deep auto⁃encoders[J]. Jisuanji yu xiandaihua, 2014(8): 128⁃134. [33]林少飞, 盛惠兴, 李庆武. 基于堆叠稀疏自动编码器的 手写数字分类[J]. 微处理机, 2015(1): 47⁃51. LIN Shaofei, SHENG Huixing, LI Qingwu. Handwritten digital classification based on the stacked sparse autoencod⁃ ers[J]. Microprocessors, 2015(1): 47⁃51. [34]陈硕. 深度学习神经网络在语音识别中的应用研究 [D]. 广州: 华南理工大学, 2013. CHEN Shuo. Research of deep learning neural networks applications in speech recognition [ D]. Guangzhou, Chi⁃ na: South China University of Technology, 2013. [35]郭丽丽, 丁世飞. 深度学习研究进展[ J]. 计算机科学, 2015, 42(5): 28⁃33. GOU Lili, DING Shifei. Research progress on deep learn⁃ ing[J]. Computer science, 2015, 42(5): 28⁃33. [36] VAN DEN OORD A, DIELEMAN S, SCHRAUWEN B. Deep content⁃based music recommendation [ M ] / / Ad⁃ vances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Sys⁃ tems. Lake Tahoe, 2013: 2643⁃2651. [37]HANNUN A, CASE C, CASPER J, et al. Deep speech: scaling up end⁃to⁃end speech recognition[EB/ OL]. Eprint Arxiv: Arxiv, 2014.[2014⁃12⁃19] https: / / arxiv.org / pdf / 1412.5567v2.pdf. [38]余凯, 贾磊, 陈雨强. 深度学习的昨天、今天和明天 [J]. 计算机研究与发展, 2013, 50(9): 1799⁃1804. YU Kai, JIA Lei, CHEN Yuqiang. Deep learning: yester⁃ day, today, and tomorrow [ J]. Journal of computer re⁃ search and development, 2013, 50(9): 1799⁃1804. 第 5 期 刘帅师,等:深度学习方法研究新进展 ·575·
<<向上翻页向下翻页>>
©2008-现在 cucdc.com 高等教育资讯网 版权所有