273 search results - page 3 / 55 » Learning the Structure of Deep Sparse Graphical Models |
EMMCVPR
2005
Springer
13 years 11 months ago
2005
Springer
This contribution proposes a compositionality architecture for visual object categorization, i.e., learning and recognizing multiple visual object classes in unsegmented, cluttered...
JMLR
2010
13 years 12 days ago
2010
We propose a non-linear graphical model for structured prediction. It combines the power of deep neural networks to extract high level features with the graphical framework of Mar...
COLING
2008
13 years 7 months ago
2008
This paper describes a parameter estimation method for multi-label classification that does not rely on approximate inference. It is known that multi-label classification involvin...
CVIU
2008
13 years 5 months ago
2008
A key problem in video content analysis using dynamic graphical models is to learn a suitable model structure given some observed visual data. We propose a Completed Likelihood AI...
ICML
2006
IEEE
14 years 6 months ago
2006
IEEE
We consider the problem of fitting a large-scale covariance matrix to multivariate Gaussian data in such a way that the inverse is sparse, thus providing model selection. Beginnin...