暂无分享,去创建一个
Pascal Vincent | Yoshua Bengio | Grégoire Mesnil | Salah Rifai | Xavier Glorot | Xavier Muller | Xavier Glorot | Yoshua Bengio | Pascal Vincent | X. Muller | S. Rifai | Grégoire Mesnil
[1] R. Fergus,et al. Learning invariant features through topographic filter maps , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[2] Aapo Hyv. Estimation of Non-Normalized Statistical Models by Score Matching , 2005 .
[3] Honglak Lee,et al. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.
[4] Honglak Lee,et al. Sparse deep belief net model for visual area V2 , 2007, NIPS.
[5] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[6] Yann LeCun,et al. What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[7] Yoshua Bengio,et al. An empirical evaluation of deep architectures on problems with many factors of variation , 2007, ICML '07.
[8] Hossein Mobahi,et al. Deep Learning via Semi-supervised Embedding , 2012, Neural Networks: Tricks of the Trade.
[9] Yann LeCun,et al. Regularized estimation of image statistics by Score Matching , 2010, NIPS.
[10] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[11] Thomas Hofmann,et al. Greedy Layer-Wise Training of Deep Networks , 2007 .
[12] Marc'Aurelio Ranzato,et al. Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition , 2010, ArXiv.
[13] Graham W. Taylor,et al. Deconvolutional networks , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[14] Jason Weston,et al. Deep learning via semi-supervised embedding , 2008, ICML '08.
[15] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[16] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[17] Christopher M. Bishop,et al. Training with Noise is Equivalent to Tikhonov Regularization , 1995, Neural Computation.
[18] Nathalie Japkowicz,et al. Nonlinear Autoassociation Is Not Equivalent to PCA , 2000, Neural Computation.
[19] Kurt Hornik,et al. Neural networks and principal component analysis: Learning from examples without local minima , 1989, Neural Networks.
[20] David J. Field,et al. Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.
[21] Quoc V. Le,et al. Measuring Invariances in Deep Networks , 2009, NIPS.
[22] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[23] Marc'Aurelio Ranzato,et al. Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.
[24] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..