Learning to Disentangle Factors of Variation with Manifold Interaction
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[1] Rich Caruana,et al. Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.
[2] David G. Lowe,et al. Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[3] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[4] Joshua B. Tenenbaum,et al. Separating Style and Content with Bilinear Models , 2000, Neural Computation.
[5] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[6] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[7] Yann LeCun,et al. The mnist database of handwritten digits , 2005 .
[8] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[9] Yann LeCun,et al. Dimensionality Reduction by Learning an Invariant Mapping , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[10] Massimiliano Pontil,et al. Multi-Task Feature Learning , 2006, NIPS.
[11] Honglak Lee,et al. Sparse deep belief net model for visual area V2 , 2007, NIPS.
[12] 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.
[13] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[14] Tijmen Tieleman,et al. Training restricted Boltzmann machines using approximations to the likelihood gradient , 2008, ICML '08.
[15] Yoshua Bengio,et al. Classification using discriminative restricted Boltzmann machines , 2008, ICML '08.
[16] Geoffrey E. Hinton. Reducing the Dimensionality of Data with Neural , 2008 .
[17] Takeo Kanade,et al. Multi-PIE , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.
[18] R. Fergus,et al. Learning invariant features through topographic filter maps , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[19] Geoffrey E. Hinton,et al. Deep Boltzmann Machines , 2009, AISTATS.
[20] Geoffrey E. Hinton,et al. Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines , 2010, Neural Computation.
[21] Veselin Stoyanov,et al. Empirical Risk Minimization of Graphical Model Parameters Given Approximate Inference, Decoding, and Model Structure , 2011, AISTATS.
[22] Pascal Vincent,et al. Contractive Auto-Encoders: Explicit Invariance During Feature Extraction , 2011, ICML.
[23] Quoc V. Le,et al. Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis , 2011, CVPR 2011.
[24] Geoffrey E. Hinton,et al. Modeling the joint density of two images under a variety of transformations , 2011, CVPR 2011.
[25] Andrew Y. Ng,et al. The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization , 2011, ICML.
[26] Yoshua Bengio,et al. A Spike and Slab Restricted Boltzmann Machine , 2011, AISTATS.
[27] Honglak Lee,et al. Unsupervised learning of hierarchical representations with convolutional deep belief networks , 2011, Commun. ACM.
[28] Geoffrey E. Hinton,et al. On deep generative models with applications to recognition , 2011, CVPR 2011.
[29] Honglak Lee,et al. Learning to Align from Scratch , 2012, NIPS.
[30] Yoshua Bengio,et al. Disentangling Factors of Variation via Generative Entangling , 2012, ArXiv.
[31] Honglak Lee,et al. Learning Invariant Representations with Local Transformations , 2012, ICML.
[32] Honglak Lee,et al. Learning hierarchical representations for face verification with convolutional deep belief networks , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[33] Geoffrey E. Hinton. A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.
[34] Pascal Vincent,et al. Disentangling Factors of Variation for Facial Expression Recognition , 2012, ECCV.
[35] Geoffrey E. Hinton,et al. Tensor Analyzers , 2013, ICML.
[36] Yoshua Bengio,et al. Multi-Prediction Deep Boltzmann Machines , 2013, NIPS.
[37] Honglak Lee,et al. Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines , 2013, ICML.
[38] Yoshua Bengio,et al. Better Mixing via Deep Representations , 2012, ICML.