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References [1] - Y. LeCun, L. Bottou, Y. Bengio and P. Haffner, Gradient-Based Learning Applied to Document Recognition, Proceedings of the IEEE, 86(11):2278-2324,

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Presentation on theme: "References [1] - Y. LeCun, L. Bottou, Y. Bengio and P. Haffner, Gradient-Based Learning Applied to Document Recognition, Proceedings of the IEEE, 86(11):2278-2324,"— Presentation transcript:

1 References [1] - Y. LeCun, L. Bottou, Y. Bengio and P. Haffner, Gradient-Based Learning Applied to Document Recognition, Proceedings of the IEEE, 86(11): , November 1998 [2] - M.D. Zeiler, R. Fergus, Visualizing and Understanding Convolutional Networks, ECCV 2014 [3] - C. Farabet, C. Couprie, L. Najman, Y. LeCun, “Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers”, in Proc. of the International Conference on Machine Learning (ICML'12), Edinburgh, Scotland, 2012 [4] – J. Long, E. Shelhamer, T. Darrell, Fully Convolutional Networks for Semantic Segmentation, CVPR 2015 [5] - C. Dong, C. C. Loy, K. He, and X. Tang, Learning a Deep Convolutional Network for Image Super-Resolution, ECCV 2014 [6] – R. Socher, C. Lin, A. Y. Ng, C. D. Manning, Parsing Natural Scenes and Natural Language with Recursive Neural Networks, ICML 2011 [7] – J. Pennington, R. Socher and C. D. Manning, Glove: Global Vectors for Word Representation, EMNLP 2014 [8] – K. Gregor, I. Danihelka, A. Graves, D. J. rezende, D. wierstra, DRAW: A Recurrent Neural Network For Image Generation, arXiv: v2 [9] – Hinton, G. E., Osindero, S., Teh, Y., A fast learning algorithm for deep belief nets, Neural Computation, 18, pp (2006) [10] - Hinton, G. E., Salakhutdinov, R, Discovering Binary Codes for Fast Document Retrieval by Learning Deep Generative Models, Topics in Cognitive Science, Vol 3, pp (2011) [11] – J. Xie, L. Xu, E. Chen, Image Denoising and Inpainting with Deep Neural Networks, NIPS 2012


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