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JAIR
1998
198views more  JAIR 1998»
13 years 5 months ago
Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
Alberto Ruiz, Pedro E. López-de-Teruel, M. ...
SIAMIS
2011
13 years 21 days ago
Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
Abstract. Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maxi...
Matthias W. Seeger, Hannes Nickisch
ML
2010
ACM
142views Machine Learning» more  ML 2010»
13 years 4 months ago
Fast adaptive algorithms for abrupt change detection
We propose two fast algorithms for abrupt change detection in streaming data that can operate on arbitrary unknown data distributions before and after the change. The first algor...
Daniel Nikovski, Ankur Jain
IJCAI
1997
13 years 7 months ago
Combining Probabilistic Population Codes
We study the problemof statisticallycorrect inference in networks whose basic representations are population codes. Population codes are ubiquitous in the brain, and involve the s...
Richard S. Zemel, Peter Dayan
NN
2007
Springer
13 years 5 months ago
Perception through visuomotor anticipation in a mobile robot
Several scientists suggested that certain perceptual qualities are based on sensorimotor anticipation: for example, the softness of a sponge is perceived by anticipating the sensa...
Heiko Hoffmann