Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, pp.86-2278, 1998.
DOI : 10.1109/5.726791

URL : http://www.cs.berkeley.edu/~daf/appsem/Handwriting/papers/00726791.pdf

A. Krizhevsky, I. Sutskever, and G. Hinton, ImageNet classi-fication with deep convolutional neural networks, Pro-ceedings of Advances in Neural Information Processing Sys-tems 25, pp.2012-1097
DOI : 10.1145/3065386

URL : http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf

R. Girshick, J. Donahue, T. Darrell, and J. Malik, Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.580-587
DOI : 10.1109/CVPR.2014.81

URL : http://arxiv.org/pdf/1311.2524

K. M. He, X. Y. Zhang, S. Q. Ren, and J. Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.2015, issue.9, pp.371904-1916
DOI : 10.1109/tpami.2015.2389824

URL : http://arxiv.org/pdf/1406.4729

C. Szegedy, W. Liu, Y. Q. Jia, P. Sermanet, S. Reed et al., Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-9
DOI : 10.1109/CVPR.2015.7298594

URL : http://arxiv.org/pdf/1409.4842

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2016.

A. L. Maas, A. Y. Hannun, and A. Ng, Rectier nonlinearities improve neural network acoustic models, Proceedings of ICML Workshop on Deep Learning for Audio, Speech, and Language Processing

S. Ioffe and C. Szegedy, Batch normalization: accelerating deep network training by reducing internal covariate shift, Proceedings of the 32nd International Conference on Ma-chine Learning, pp.448-456

J. Shi and J. Malik, Motion segmentation and tracking using normalized cuts, Sixth International Conference on Computer Vision, pp.1154-1160, 1998.

E. Jung, S. Ranka, and S. Sahni, Bandwidth Allocation for Iterative Data-Dependent E-science Applications, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp.233-242, 2010.
DOI : 10.1109/CCGRID.2010.114