[1] |
ZEILER M D, FERGUS R. Stochastic pooling for regularization of deep convolutional neural networks [J]. Eprint, 2013: arXiv:1301.3557.
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[2] |
HUANG Yuchi, SUN Xiuyu, LU Ming, et al. Channel-max, channel-drop and stochastic max-pooling [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. Boston, USA: IEEE, 2015: 9-17.
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[3] |
CAI Meng, SHI Yongzhe, LIU Jia. Stochastic pooling maxout networks for low-resource speech recognition [C]// Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing. Florence, Italy: IEEE, 2014: 3266-3270.
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[4] |
HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016: 770-778.
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[5] |
VEIT A, WILBER M, BELONGIE S. Residual networks are exponential ensembles of relatively shallow networks [EB/OL]. [2017-02-14] https://arxiv.org/abs/1605.06431v1.
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[6] |
ZHAO Liming, WANG Jingdong, LI Xi, et al. On the connection of deep fusion to ensembling[EB/OL]. [2017-02-14] https://arxiv.org/abs/1611.07718.
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[7] |
WU Haibing, GU Xiaodong. Max-pooling dropout for regularization of convolutional neural networks [C]// Proceedings of the International Conference on Neural Information Processing. Berlin: Springer, 2015: 46-54.
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[8] |
HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Identity mappings in deep residual networks [C]// Proceedings of the 14th European Conference on Computer Vision. Berlin: Springer, 2016: 630-645.
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[9] |
SIMARD P Y, STEINKRAUS D, PLATT J C, et al. Best practices for convolutional neural networks applied to visual document analysis [C]// Proceedings of the International Conference on Document Analysis and Recognition. Washington: IEEE Computer Society, 2003: 958-962.
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LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition [J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
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[11] |
JIA Y Q, SHELHAMER E, DONAHUE J, et al. Caffe: Convolutional architecture for fast feature embedding [C]// Proceedings of the 22nd ACM International Conference on Multimedia. Orlando, USA: ACM, 2014: 675-678.
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[12] |
KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks [J]. International Conference on Neural Information Processing Systems, 2012, 25(2): 1097-1105.
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[13] |
SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition [J]. Eprint, 2015: arXiv:1409.1556.
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[14] |
DENG J, DONG W, SOCHER R, et al. ImageNet: A large-scale hierarchical image database [C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA: IEEE, 2009: 248-255.
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[15] |
KRIZHEVSKY A, HINTON G. Learning multiple layers of features from tiny images[J]. Eprint, 2009: arXiv:1011.1669v3.
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[16] |
GEMAN S, BIENENSTOCK E, DOURSAT R. Neural networks and the bias/variance dilemma [J]. Neural computation, 1992, 4(1): 1-58.
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[17] |
HUANG Gao, SUN Yu, LIU Zhuang, et al. Deep networks with stochastic depth [C]// Proceedings of the 14th European Conference on Computer Vision. Berlin: Springer, 2016: 646-661.
|
[18] |
CHEN Tianqi, LI Mu, LI Yutian, et al. Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems [J]. Eprint, 2015: arXiv:1512.01274.
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[19] |
DANIELS H, KAMP B, VERKOOIJEN W. Application of Neural Networks to House Pricing and Bond Rating [M]. Tilburg University, 1997.
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[20] |
COBHAM A. The intrinsic computational difficulty of functions [J]. International Congress for Logic, 1969, 31(1): 43-52.
|
[21] |
EDMONDS J. Paths, trees, and flowers [J]. Canadian Journal of Mathematics, 2009, 17(3):361-379.
|
[22] |
IOFFE S, SZEGEDY C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift [C]// Proceedings of the 32nd International Conference on Machine Learning. Lille, France: ACM, 2015: 448-456.
|
[23] |
HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification [C]// Proceedings of the 27th International Conference on Computer Vision. Santiago, USA: ACM, 2015: 1026-1034.
|
[1] |
ZEILER M D, FERGUS R. Stochastic pooling for regularization of deep convolutional neural networks [J]. Eprint, 2013: arXiv:1301.3557.
|
[2] |
HUANG Yuchi, SUN Xiuyu, LU Ming, et al. Channel-max, channel-drop and stochastic max-pooling [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. Boston, USA: IEEE, 2015: 9-17.
|
[3] |
CAI Meng, SHI Yongzhe, LIU Jia. Stochastic pooling maxout networks for low-resource speech recognition [C]// Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing. Florence, Italy: IEEE, 2014: 3266-3270.
|
[4] |
HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016: 770-778.
|
[5] |
VEIT A, WILBER M, BELONGIE S. Residual networks are exponential ensembles of relatively shallow networks [EB/OL]. [2017-02-14] https://arxiv.org/abs/1605.06431v1.
|
[6] |
ZHAO Liming, WANG Jingdong, LI Xi, et al. On the connection of deep fusion to ensembling[EB/OL]. [2017-02-14] https://arxiv.org/abs/1611.07718.
|
[7] |
WU Haibing, GU Xiaodong. Max-pooling dropout for regularization of convolutional neural networks [C]// Proceedings of the International Conference on Neural Information Processing. Berlin: Springer, 2015: 46-54.
|
[8] |
HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Identity mappings in deep residual networks [C]// Proceedings of the 14th European Conference on Computer Vision. Berlin: Springer, 2016: 630-645.
|
[9] |
SIMARD P Y, STEINKRAUS D, PLATT J C, et al. Best practices for convolutional neural networks applied to visual document analysis [C]// Proceedings of the International Conference on Document Analysis and Recognition. Washington: IEEE Computer Society, 2003: 958-962.
|
[10] |
LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition [J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
|
[11] |
JIA Y Q, SHELHAMER E, DONAHUE J, et al. Caffe: Convolutional architecture for fast feature embedding [C]// Proceedings of the 22nd ACM International Conference on Multimedia. Orlando, USA: ACM, 2014: 675-678.
|
[12] |
KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks [J]. International Conference on Neural Information Processing Systems, 2012, 25(2): 1097-1105.
|
[13] |
SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition [J]. Eprint, 2015: arXiv:1409.1556.
|
[14] |
DENG J, DONG W, SOCHER R, et al. ImageNet: A large-scale hierarchical image database [C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA: IEEE, 2009: 248-255.
|
[15] |
KRIZHEVSKY A, HINTON G. Learning multiple layers of features from tiny images[J]. Eprint, 2009: arXiv:1011.1669v3.
|
[16] |
GEMAN S, BIENENSTOCK E, DOURSAT R. Neural networks and the bias/variance dilemma [J]. Neural computation, 1992, 4(1): 1-58.
|
[17] |
HUANG Gao, SUN Yu, LIU Zhuang, et al. Deep networks with stochastic depth [C]// Proceedings of the 14th European Conference on Computer Vision. Berlin: Springer, 2016: 646-661.
|
[18] |
CHEN Tianqi, LI Mu, LI Yutian, et al. Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems [J]. Eprint, 2015: arXiv:1512.01274.
|
[19] |
DANIELS H, KAMP B, VERKOOIJEN W. Application of Neural Networks to House Pricing and Bond Rating [M]. Tilburg University, 1997.
|
[20] |
COBHAM A. The intrinsic computational difficulty of functions [J]. International Congress for Logic, 1969, 31(1): 43-52.
|
[21] |
EDMONDS J. Paths, trees, and flowers [J]. Canadian Journal of Mathematics, 2009, 17(3):361-379.
|
[22] |
IOFFE S, SZEGEDY C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift [C]// Proceedings of the 32nd International Conference on Machine Learning. Lille, France: ACM, 2015: 448-456.
|
[23] |
HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification [C]// Proceedings of the 27th International Conference on Computer Vision. Santiago, USA: ACM, 2015: 1026-1034.
|