273 search results - page 1 / 55 » Learning the Structure of Deep Sparse Graphical Models |
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...
ICML
2009
IEEE
14 years 5 months ago
2009
IEEE
The promise of unsupervised learning methods lies in their potential to use vast amounts of unlabeled data to learn complex, highly nonlinear models with millions of free paramete...
ICML
2009
IEEE
14 years 5 months ago
2009
IEEE
Recent work has shown that one can learn the structure of Gaussian Graphical Models by imposing an L1 penalty on the precision matrix, and then using efficient convex optimization...
TSP
2010
12 years 11 months ago
2010
Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques...
TSP
2010
12 years 11 months ago
2010
We consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale and the coarser, hidden variables serve both to captu...