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» On Bayesian Case Matching
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NIPS
2004
14 years 3 days ago
Log-concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning
Log-concavity is an important property in the context of optimization, Laplace approximation, and sampling; Bayesian methods based on Gaussian process priors have become quite pop...
Liam Paninski
NIPS
1996
14 years 1 days ago
Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning
Model learning combined with dynamic programming has been shown to be e ective for learning control of continuous state dynamic systems. The simplest method assumes the learned mod...
Jeff G. Schneider
VISAPP
2007
13 years 12 months ago
Image deconvolution using a stochastic differential equation approach
We consider the problem of image deconvolution. We foccus on a Bayesian approach which consists of maximizing an energy obtained by a Markov Random Field modeling. MRFs are classi...
Xavier Descombes, M. Lebellego, Elena Zhizhina
ICASSP
2010
IEEE
13 years 11 months ago
Sparse signal recovery in the presence of correlated multiple measurement vectors
Sparse signal recovery algorithms utilizing multiple measurement vectors (MMVs) are known to have better performance compared to the single measurement vector case. However, curre...
Zhilin Zhang, Bhaskar D. Rao
JMLR
2010
144views more  JMLR 2010»
13 years 5 months ago
Practical Approaches to Principal Component Analysis in the Presence of Missing Values
Principal component analysis (PCA) is a classical data analysis technique that finds linear transformations of data that retain the maximal amount of variance. We study a case whe...
Alexander Ilin, Tapani Raiko