273 search results - page 4 / 55 » Learning the Structure of Deep Sparse Graphical Models |
ICASSP
2011
IEEE
13 years 1 months ago
2011
IEEE
Graphical models are well established in providing compact conditional probability descriptions of complex multivariable interactions. In the Gaussian case, graphical models are d...
ICML
2009
IEEE
14 years 10 months ago
2009
IEEE
In many real-world domains, undirected graphical models such as Markov random fields provide a more natural representation of the dependency structure than directed graphical mode...
ICML
2004
IEEE
14 years 10 months ago
2004
IEEE
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...
ICML
2010
IEEE
13 years 11 months ago
2010
IEEE
Deep Belief Networks (DBNs) are hierarchical generative models which have been used successfully to model high dimensional visual data. However, they are not robust to common vari...
SCALESPACE
2007
Springer
14 years 3 months ago
2007
Springer
This paper presents a statistical model for textures that uses a non-negative decomposition on a set of local atoms learned from an exemplar. This model is described by the varianc...