Shared Kernel Information Embedding for Discriminative Inference
暂无分享,去创建一个
[1] Miguel Á. Carreira-Perpiñán,et al. Parametric dimensionality reduction by unsupervised regression , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[2] Trevor Darrell,et al. Factorized Orthogonal Latent Spaces , 2010, AISTATS.
[3] Michael J. Black,et al. HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion , 2010, International Journal of Computer Vision.
[4] Cristian Sminchisescu,et al. Twin Gaussian Processes for Structured Prediction , 2010, International Journal of Computer Vision.
[5] Bohyung Han,et al. Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] Cristian Sminchisescu,et al. Fast algorithms for large scale conditional 3D prediction , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[7] Trevor Darrell,et al. Sparse probabilistic regression for activity-independent human pose inference , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[8] Roland Memisevic,et al. Non-linear Latent Factor Models for Revealing Structure in High-dimensional Data , 2008 .
[9] Andrew W. Fitzgibbon,et al. The Joint Manifold Model for Semi-supervised Multi-valued Regression , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[10] Cristian Sminchisescu,et al. Spectral Latent Variable Models for Perceptual Inference , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[11] Michael J. Black,et al. Combined discriminative and generative articulated pose and non-rigid shape estimation , 2007, NIPS.
[12] Miguel Á. Carreira-Perpiñán,et al. People Tracking with the Laplacian Eigenmaps Latent Variable Model , 2007, NIPS.
[13] Hans-Peter Seidel,et al. Nonparametric Density Estimation with Adaptive, Anisotropic Kernels for Human Motion Tracking , 2007, Workshop on Human Motion.
[14] Neil D. Lawrence,et al. Gaussian Process Latent Variable Models for Human Pose Estimation , 2007, MLMI.
[15] Cristian Sminchisescu,et al. Semi-supervised Hierarchical Models for 3D Human Pose Reconstruction , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[16] Miguel Á. Carreira-Perpiñán,et al. The Laplacian Eigenmaps Latent Variable Model , 2007, AISTATS.
[17] Marc'Aurelio Ranzato,et al. Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.
[18] Luc Van Gool,et al. Monocular Tracking with a Mixture of View-Dependent Learned Models , 2006, AMDO.
[19] Roland Memisevic,et al. Kernel information embeddings , 2006, ICML.
[20] David W. Murray,et al. Regression-based Hand Pose Estimation from Multiple Cameras , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[21] Björn Stenger,et al. Multivariate Relevance Vector Machines for Tracking , 2006, ECCV.
[22] Michael J. Black,et al. HumanEva: Synchronized Video and Motion Capture Dataset for Evaluation of Articulated Human Motion , 2006 .
[23] Rajesh P. N. Rao,et al. Learning Shared Latent Structure for Image Synthesis and Robotic Imitation , 2005, NIPS.
[24] Neil D. Lawrence,et al. Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models , 2005, J. Mach. Learn. Res..
[25] Carl E. Rasmussen,et al. A Unifying View of Sparse Approximate Gaussian Process Regression , 2005, J. Mach. Learn. Res..
[26] Helge J. Ritter,et al. Principal surfaces from unsupervised kernel regression , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[27] Cristian Sminchisescu,et al. Discriminative density propagation for 3D human motion estimation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[28] Ramani Duraiswami,et al. The improved fast Gauss transform with applications to machine learning , 2005 .
[29] Ankur Agarwal,et al. Learning to track 3D human motion from silhouettes , 2004, ICML.
[30] Ankur Agarwal,et al. 3D human pose from silhouettes by relevance vector regression , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..
[31] David J. C. MacKay,et al. Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.
[32] Nicolas Le Roux,et al. Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering , 2003, NIPS.
[33] Trevor Darrell,et al. Fast pose estimation with parameter-sensitive hashing , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[34] Malte Kuss,et al. The Geometry Of Kernel Canonical Correlation Analysis , 2003 .
[35] Dorin Comaniciu,et al. Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[36] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[37] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[38] Miguel Á. Carreira-Perpiñán,et al. Reconstruction of Sequential Data with Probabilistic Models and Continuity Constraints , 1999, NIPS.
[39] Michael E. Tipping,et al. Probabilistic Principal Component Analysis , 1999 .
[40] Bernhard Schölkopf,et al. Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.
[41] D. W. Scott. Multivariate Density Estimation: Theory, Practice, and Visualization , 1992, Wiley Series in Probability and Statistics.
[42] D. W. Scott,et al. Multivariate Density Estimation, Theory, Practice and Visualization , 1992 .
[43] Thomas M. Cover,et al. Elements of Information Theory , 2005 .