·706· 智能系统学报 第17卷 J.Convolutional Gaussian processes[Cl//NIPS'17:Pro- tional Symposium on Performance Analysis of Systems ceedings of the 31st International Conference on Neural and Software.New York:IEEE.2017:55-64 Information Processing Systems.New York:ACM, [28]KRIZHEVSKY A.SUTSKEVER I.HINTON G E.Im- 2017:2845-2854. ageNet classification with deep convolutional neural net- [17]RASMUSSEN C E,WILLIAMS C K I.Gaussian pro- works[Cl//Proceedings of Advances in Neural Informa- cesses for machine learning[M].London:MIT,2006 tion Processing Systems 25.Nevada:Curran Associates, [18]张若非，付强，高斌.深度学习模型及应用详解M北 2012:1097-1105. 京：电子工业出版社，2019：2-6 [29]SIMONYAN K.ZISSERMAN A.Very deep convolu- [19]MITCHELL Tom M.机器学习M).曾华军，张银奎译 tional networks for large-scale image recognition[EB/OL]. 北京：机械工业出版社，2012：60-63. New York:arXiv,2014.(2014-09-04)[2021-10-29] [20]LECUN Y,BOSER B,DENKER J S,et al.Back- propagation applied to handwritten zip code recognition https://arxiv.org/abs/1409.1556 [J].Neural computation,1989,1(4):541-551. [30]CLEVERT D A,UNTERTHINER T,HOCHREITER S. [21]LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient- Fast and accurate deep network learning by exponential based learning applied to document recognition[J].Pro- linear units[EB/OL].New York:arXiv,2015.(2015- ceedings of the IEEE,1998,86(11):2278-2324. 11-23)[2021-10-29]https:/arxiv.org/abs/1511.07289. [22]BOUVRIE J.Notes on convolutional neural networks[J]. 作者简介： In practice,2006:47-60. 王佳锐，讲师，主要研究方向为机 [23]DUDA Richard O,HART Peter E,STORK David G. 器视觉、人工智能、深度学习算法应用。 式分类[M.李宏东，姚天翔译.北京：机械工业出版 社.2004. [24]HE Kaiming,ZHANG Xiangyu,REN Shaoqing,et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE transactions on pattern analysis and machine intelligence,2015,37(9):1904- 刘能锋，副教授，主要研究方向为 1916. 机器人控制、教学平台设计。 [25]ZEILER M D,FERGUS R.Stochastic pooling for regu- larization of deep convolutional neural networks[EB/OL]. New York:arXiv,2013.(2013-01-162021-10-29 https:∥ arxiv.org/abs/1301.3557. [26]LE CUN Y.BOSER B.DENKER J S,et al.Handwrit- ten digit recognition with a back-propagation network 曲鹏，讲师，主要研究方向为金属 [CV/NIPS'89:Proceedings of the 2nd International Con- 材料制备与表征、高熵合金力学性能 与检测、锆钛合金力学性能与检测。 ference on Neural Information Processing Systems.New York:ACM,1989:396-404. [27]KIM H,NAM H,JUNG W,et al.Performance analysis of CNN frameworks for GPUs[C]//2017 IEEE Interna-J. Convolutional Gaussian processes[C]//NIPS'17: Pro￾ceedings of the 31st International Conference on Neural Information Processing Systems. New York: ACM, 2017: 2845−2854. RASMUSSEN C E, WILLIAMS C K I. Gaussian pro￾cesses for machine learning[M]. London: MIT, 2006 [17] 张若非, 付强, 高斌. 深度学习模型及应用详解 [M]. 北 京: 电子工业出版社, 2019: 2−6. [18] MITCHELL Tom M. 机器学习 [M]. 曾华军, 张银奎译. 北京: 机械工业出版社, 2012: 60−63. [19] LECUN Y, BOSER B, DENKER J S, et al. Back￾propagation applied to handwritten zip code recognition [J]. Neural computation, 1989, 1(4): 541–551. [20] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient￾based learning applied to document recognition[J]. Pro￾ceedings of the IEEE, 1998, 86(11): 2278–2324. [21] BOUVRIE J. Notes on convolutional neural networks[J]. In practice, 2006: 47–60. [22] DUDA Richard O, HART Peter E, STORK David G. 模 式分类 [M]. 李宏东, 姚天翔译. 北京: 机械工业出版 社, 2004. [23] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(9): 1904– 1916. [24] ZEILER M D, FERGUS R. Stochastic pooling for regu￾larization of deep convolutional neural networks[EB/OL]. New York: arXiv, 2013. (2013−01−16)[2021−10−29].https:// arxiv.org/abs/1301.3557. [25] LE CUN Y, BOSER B, DENKER J S, et al. Handwrit￾ten digit recognition with a back-propagation network [C]//NIPS'89: Proceedings of the 2nd International Con￾ference on Neural Information Processing Systems. New York: ACM, 1989: 396−404. [26] KIM H, NAM H, JUNG W, et al. Performance analysis of CNN frameworks for GPUs[C]//2017 IEEE Interna- [27] tional Symposium on Performance Analysis of Systems and Software. New York: IEEE, 2017: 55−64. KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Im￾ageNet classification with deep convolutional neural net￾works[C]//Proceedings of Advances in Neural Informa￾tion Processing Systems 25. Nevada: Curran Associates, 2012: 1097−1105. [28] SIMONYAN K, ZISSERMAN A. Very deep convolu￾tional networks for large-scale image recognition[EB/OL]. New York: arXiv, 2014. (2014−09−04)[2021−10−29]. https://arxiv.org/abs/1409.1556. [29] CLEVERT D A, UNTERTHINER T, HOCHREITER S. Fast and accurate deep network learning by exponential linear units[EB/OL]. New York: arXiv, 2015. (2015− 11−23) [2021−10−29].https://arxiv.org/abs/1511.07289. [30] 作者简介： 王佳锐，讲师，主要研究方向为机 器视觉、人工智能、深度学习算法应用。 刘能锋，副教授，主要研究方向为 机器人控制、教学平台设计。 曲鹏，讲师，主要研究方向为金属 材料制备与表征、高熵合金力学性能 与检测、锆钛合金力学性能与检测。 ·706· 智 能 系 统 学 报 第 17 卷