The Manifold Tangent Classifier
暂无分享,去创建一个
Pascal Vincent | Yoshua Bengio | Yann Dauphin | Salah Rifai | Xavier Muller | Yoshua Bengio | Pascal Vincent | Yann Dauphin | X. Muller | S. Rifai | Y. Dauphin | Salah Rifai
[1] Yann LeCun,et al. Tangent Prop - A Formalism for Specifying Selected Invariances in an Adaptive Network , 1991, NIPS.
[2] Yann LeCun,et al. Efficient Pattern Recognition Using a New Transformation Distance , 1992, NIPS.
[3] Harris Drucker,et al. Improving generalization performance using double backpropagation , 1992, IEEE Trans. Neural Networks.
[4] Yoshua Bengio,et al. Object Recognition with Gradient-Based Learning , 1999, Shape, Contour and Grouping in Computer Vision.
[5] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[6] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[7] Pascal Vincent,et al. Manifold Parzen Windows , 2002, NIPS.
[8] Matthew Brand,et al. Charting a Manifold , 2002, NIPS.
[9] Yoshua Bengio,et al. Non-Local Manifold Tangent Learning , 2004, NIPS.
[10] Lawrence Cayton,et al. Algorithms for manifold learning , 2005 .
[11] Pascal Vincent,et al. Non-Local Manifold Parzen Windows , 2005, NIPS.
[12] Yoshua Bengio,et al. Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.
[13] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[14] Marc'Aurelio Ranzato,et al. Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.
[15] Tom Minka,et al. Principled Hybrids of Generative and Discriminative Models , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[16] Geoffrey E. Hinton,et al. Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure , 2007, AISTATS.
[17] Marc'Aurelio Ranzato,et al. Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[18] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[19] Jason Weston,et al. Deep learning via semi-supervised embedding , 2008, ICML '08.
[20] M. Trebar,et al. Application of distributed SVM architectures in classifying forest data cover types , 2008 .
[21] Quoc V. Le,et al. Measuring Invariances in Deep Networks , 2009, NIPS.
[22] R. Fergus,et al. Learning invariant features through topographic filter maps , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[23] Geoffrey E. Hinton,et al. Deep Boltzmann Machines , 2009, AISTATS.
[24] Yihong Gong,et al. Nonlinear Learning using Local Coordinate Coding , 2009, NIPS.
[25] Yoshua Bengio,et al. Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.
[26] Tong Zhang,et al. Improved Local Coordinate Coding using Local Tangents , 2010, ICML.
[27] Hariharan Narayanan,et al. Sample Complexity of Testing the Manifold Hypothesis , 2010, NIPS.
[28] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[29] Pascal Vincent,et al. Contractive Auto-Encoders: Explicit Invariance During Feature Extraction , 2011, ICML.
[30] Pascal Vincent,et al. Higher Order Contractive Auto-Encoder , 2011, ECML/PKDD.