Suchergebnisse

Langovoy, M.: Algebraic polynomials and moments of stochastic integrals (EURANDOM Reports, 2009-031). (2009)

Gretton, A.; Györfi, L.: Consistent Nonparametric Tests of Independence (Technical Report of the Max Planck Institute for Biological Cybernetics, 172). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2009), 35 S.

Hennig, P.: Expectation Propagation on the Maximum of Correlated Normal Variables. (2009), 11 S.

Shelton, J.; Blaschko, M.; Bartels, A.: Semi-supervised subspace analysis of human functional magnetic resonance imaging data (Technical Report of the Max Planck Institute for Biological Cybernetics, 185). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2009), 10 S.

Berens, P.; Velasco, M.: The circular statistics toolbox for Matlab (Technical Report of the Max Planck Institute for Biological Cybernetics, 184). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2009), 9 S.

Langovoy, M.: Model selection, large deviations and consistency of data-driven tests (EURANDOM Reports, 2009-007). (2009), 30 S.

Macke, J.; Opper, M.; Bethge, M.: The effect of pairwise neural correlations on global population statistics (Technical Report Max Planck Institute for Biological Cybernetics, 183). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2009), 21 S.

Berger, D.: Scene scaling during simulated forward accelerations: Are explicit size cues used? (Technical Report of the Max Planck Institute for Biological Cybernetics, 182). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2009), 14 S.

Franchi, A.; Oriolo, G.; Stegagno, P.: Mutual localization in a multi-robot system with anonymous relative position measures. (2009)

Blaschko, M.; Gretton, A.: Taxonomy Inference Using Kernel Dependence Measures (Technical Report of the Max Planck Institute for Biological Cybernetics, 181). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2008), 11 S.

Giesen, J.; Maier, M.; Schölkopf, B.: Simultaneous Implicit Surface Reconstruction and Meshing (Technical Report of the Max Planck Institute for Biological Cybernetics, 179). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2008), 11 S.

Nowozin, S.; Tsuda, K.: Frequent Subgraph Retrieval in Geometric Graph Databases (Technical Report of the Max Planck Institute for Biological Cybernetics, 180). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2008), 13 S.

Gehler, P.; Nowozin, S.: Infinite Kernel Learning (Technical Report of the Max Planck Institute for Biological Cybernetics, 178). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2008), 12 S.

Seeger, M.; Nickisch, H.: Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models (Technical Report of the Max Planck Institute for Biological Cybernetics, 175). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2008), 34 S.

Sra, S.: Block-Iterative Algorithms for Non-Negative Matrix Approximation (Technical Report of the Max Planck Institute for Biological Cybernetics, 176). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2008), 16 S.

Sra, S.; Jegelka, S.; Banerjee, A.: Approximation Algorithms for Bregman Clustering Co-clustering and Tensor Clustering (Technical Report of the Max Planck Institute for Biological Cybernetics, 177). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2008), 21 S.

Dhillon, P.; Nowozin, S.; Lampert, C.: Combining Appearance and Motion for Human Action Classification in Videos (Technical Report of the Max Planck Institute for Biological Cybernetics, 174). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2008), 10 S.

Kim, K.; Kwon, Y.: Example-based Learning for Single-image Super-resolution and JPEG Artifact Removal (Technical Report of the Max Planck Institute for Biological Cybernetics, 173). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2008), 28 S.

Chiappa, S.: Unsupervised Bayesian Time-series Segmentation based on Linear Gaussian State-space Models (Technical Report of the Max Planck Institute for Biological Cybernetics, 171). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2008)

Kim, D.; Sra, S.; Dhillon, I.: A New Non-monotonic Gradient Projection Method for the Non-negative Least Squares Problem. University of Texas: Computer Sciences, Austin, TX, USA (2008)