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» Approximating Gaussian Processes with H2-Matrices
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85
Voted
ICIP
2005
IEEE
16 years 1 months ago
Approximate separable 3D anisotropic Gauss filter
Anisotropic Gaussian filters are useful for adaptive smoothing and feature extraction. In our application, micro - tomographic images of fibers were smoothed by anisotropic Gaussi...
Oliver Wirjadi, Thomas M. Breuel
DSMML
2004
Springer
15 years 5 months ago
Extensions of the Informative Vector Machine
The informative vector machine (IVM) is a practical method for Gaussian process regression and classification. The IVM produces a sparse approximation to a Gaussian process by com...
Neil D. Lawrence, John C. Platt, Michael I. Jordan
91
Voted
NIPS
2004
15 years 1 months ago
Using the Equivalent Kernel to Understand Gaussian Process Regression
The equivalent kernel [1] is a way of understanding how Gaussian process regression works for large sample sizes based on a continuum limit. In this paper we show (1) how to appro...
Peter Sollich, Christopher K. I. Williams
ICML
2004
IEEE
16 years 13 days ago
Variational methods for the Dirichlet process
Variational inference methods, including mean field methods and loopy belief propagation, have been widely used for approximate probabilistic inference in graphical models. While ...
David M. Blei, Michael I. Jordan
ICASSP
2009
IEEE
15 years 6 months ago
Bounded conditional mean imputation with Gaussian mixture models: A reconstruction approach to partly occluded features
In this work we show how conditional mean imputation can be bounded through the use of box-truncated Gaussian distributions. That is of interest when signals or features are partl...
Friedrich Faubel, John W. McDonough, Dietrich Klak...