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» Hierarchical Gaussian Process Regression
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ICIP
2007
IEEE
14 years 9 months ago
Image Denoising with Nonparametric Hidden Markov Trees
We develop a hierarchical, nonparametric statistical model for wavelet representations of natural images. Extending previous work on Gaussian scale mixtures, wavelet coefficients ...
Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jord...
IROS
2009
IEEE
155views Robotics» more  IROS 2009»
14 years 2 months ago
Active learning using mean shift optimization for robot grasping
— When children learn to grasp a new object, they often know several possible grasping points from observing a parent’s demonstration and subsequently learn better grasps by tr...
Oliver Kroemer, Renaud Detry, Justus H. Piater, Ja...
ICASSP
2008
IEEE
14 years 2 months ago
Generalized Gaussian Markov random field image restoration using variational distribution approximation
In this paper we propose novel algorithms for image restoration and parameter estimation with a Generalized Gaussian Markov Random Field prior utilizing variational distribution a...
S. Derin Babacan, Rafael Molina, Aggelos K. Katsag...
JMLR
2011
167views more  JMLR 2011»
13 years 2 months ago
Logistic Stick-Breaking Process
A logistic stick-breaking process (LSBP) is proposed for non-parametric clustering of general spatially- or temporally-dependent data, imposing the belief that proximate data are ...
Lu Ren, Lan Du, Lawrence Carin, David B. Dunson
NIPS
2007
13 years 9 months ago
Selecting Observations against Adversarial Objectives
In many applications, one has to actively select among a set of expensive observations before making an informed decision. Often, we want to select observations which perform well...
Andreas Krause, H. Brendan McMahan, Carlos Guestri...