An empirical evaluation of deep architectures on problems with many factors of variation
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Yoshua Bengio | Dumitru Erhan | Hugo Larochelle | Aaron C. Courville | James Bergstra | Yoshua Bengio | D. Erhan | H. Larochelle | J. Bergstra
[1] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[2] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[3] Geoffrey E. Hinton,et al. Exponential Family Harmoniums with an Application to Information Retrieval , 2004, NIPS.
[4] Bernhard Schölkopf,et al. Training Invariant Support Vector Machines , 2002, Machine Learning.
[5] Y. LeCun,et al. Learning methods for generic object recognition with invariance to pose and lighting , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..
[6] Amos Storkey,et al. Advances in Neural Information Processing Systems 20 , 2007 .
[7] Yoshua Bengio,et al. Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.
[8] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[9] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[10] Geoffrey E. Hinton,et al. To recognize shapes, first learn to generate images. , 2007, Progress in brain research.
[11] Jason Weston,et al. Large-scale kernel machines , 2007 .
[12] Yoshua Bengio,et al. Scaling learning algorithms towards AI , 2007 .
[13] Geoffrey E. Hinton,et al. Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure , 2007, AISTATS.