30 search results - page 2 / 6 » Map approach to learning sparse Gaussian Markov networks |
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
This paper presents a novel Gaussian process (GP) approach to regression with inputdependent noise rates. We follow Goldberg et al.'s approach and model the noise variance us...
JMLR
2010
12 years 11 months ago
2010
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
CVPR
2008
IEEE
14 years 6 months ago
2008
IEEE
Discriminative approaches to human pose inference involve mapping visual observations to articulated body configurations. Current probabilistic approaches to learn this mapping ha...
SDM
2009
SIAM
14 years 2 months ago
2009
SIAM
We consider the task of performing anomaly detection in highly noisy multivariate data. In many applications involving real-valued time-series data, such as physical sensor data a...
NIPS
1997
13 years 6 months ago
1997
We address the problem of learning structure in nonlinear Markov networks with continuous variables. This can be viewed as non-Gaussian multidimensional density estimation exploit...