科学网—深度学习在目标视觉检测中的应用进展与展望(8)

42 Yan J J, Lei Z, Wen L Y, Li S Z. The fastest deformable part model for object detection. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Columbus, Ohio, USA: IEEE, 2014. 2497-2504

43 Lazebnik S, Schmid C, Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In:Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). New York, NY, USA: IEEE, 2006. 2169-2178

44 Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. In:Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Columbus, Ohio, USA: IEEE, 2014. 580-587

45 Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z H, Karpathy A, Khosla A, Bernstein M, Berg A C, Fei-Fei L. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 2015, 115(3): 211-252.

46 Everingham M, Van Gool L, Williams C K I, Winn J, Zisserman A. The PASCAL visual object classes (VOC) challenge. International Journal of Computer Vision, 2010, 88(2): 303-338.

47 Xiao J X, Hays J, Ehinger K A, Oliva A, Torralba A. Sun database: large-scale scene recognition from abbey to zoo. In:Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). San Francisco, CA, USA: IEEE, 2010. 3485-3492

48 Lin T Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick C L. Microsoft COCO: common objects in context. In: Proceedings of the 13th European Conference on Computer Vision (ECCV). Zurich, Switzerland:Springer, 2014. 740-755

49 Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors. Nature, 1986, 323(6088): 533-536.

50 LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.

51 Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504-507.

52 Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets. Neural Computation, 2006, 18(7): 1527-1554.

53 Bengio Y, Lamblin P, Popovici D, Larochelle H. Greedy layer-wise training of deep networks. In: Proceedings of the 19th International Conference on Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2006. 153-160

初学opengl最好的教程是learn opengl, extensive tutorial resource for learning modern opengl。shaishalew-shwartzandshaiben-david.understanding machine learning:from theory to algorithms.cambridge university press.2014。cao z, qin t, liu t y, et al. learning to rank: from pairwise approach to listwise approach[c]//proceedings of the 24th international conference on machine learning. acm, 2007: 129-136. 另外附上《tutorial-ltr by hang li》《tutorial-ltr by ty liu》。

55 Lee H, Ekanadham C, Ng A Y. Sparse deep belief net model for visual area V2. In: Proceedings of the 2007 Advances in Neural Information Processing Systems (NIPS). Vancouver, British Columbia, Canada:MIT Press, 2007. 873-880

56 Hinton G, Deng L, Yu D, Dahl G E, Mohamed A R, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath T N, Kingsbury B. Deep neural networks for acoustic modeling in speech recognition:the shared views of four research groups. IEEE Signal Processing Magazine, 2012, 29(6): 82-97.

57 Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada, USA: MIT Press, 2012. 1097-1105

58 Girshick R. Fast R-CNN. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile:IEEE, 2015. 1440-1448

59 Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11): 2278-2324.

[33] coates, a., huval, b., wang, t., wu, d. j., ng, a. y. deep learning with cots hpc systems. in proceedings of the 30th international conference on machine learning (icml’13) (atlanta, georgia, usa, june 16–21, 2013). jmlr: w&cp volume 28(3), 2013, 1337-1345.。93. lingling zhang ,guangli nie, a way to implement intelligent knowledge push in knowledge management system. proceedings of the 20th international conference on multiple criteria decision making. chengdu/jiu zhai gou, china, june 21-26, 2009（istp）:202-208。10.cairong zou, chengwei huang, dong han, li zhao. detecting practical speech emotion in a cognitive task, computer communications and networks , 2011 proceedings of 20th international conference on, maui, hi, usa, 2011。

61 Masci J, Meier U, Cireşan D, Schmidhuber J. Stacked convolutional auto-encoders for hierarchical feature extraction. In:Proceedings of the 21th International Conference on Artificial Neural Networks. Berlin, Heidelberg, Germany: Springer, 2011. 52-59

harvey i., and bossomaier t.r.j, (1997). time out of joint: attractors in asynchronous boolean networks. in husbands and harvey (eds.), proceedings of the fourth european conference on artificial life, 67-75, mit press.。[26] krizhevsky, a. parallelizing convolutional neural networks. in tutorial of ieee conference on computer vision and pattern recognition (cvpr 2014). (columbus, ohio, usa, june 23-28, 2014). 2014.。proceedings - 2011 4th international conference on intelligent networks and intelligent systems, icinis 2011, p 177-180。