161 search results - page 7 / 33 » Dirichlet process mixture models with multiple modalities |
ICML
2004
IEEE
15 years 10 months ago
2004
IEEE
Variational inference methods, including mean field methods and loopy belief propagation, have been widely used for approximate probabilistic inference in graphical models. While ...
159
click to vote
ICML
2010
IEEE
14 years 10 months ago
2010
IEEE
The hierarchical Dirichlet process (HDP) is a Bayesian nonparametric mixed membership model--each data point is modeled with a collection of components of different proportions. T...
NIPS
2001
14 years 11 months ago
2001
We present an extension to the Mixture of Experts (ME) model, where the individual experts are Gaussian Process (GP) regression models. Using an input-dependent adaptation of the ...
56
Voted
ICML
2008
IEEE
15 years 10 months ago
2008
IEEE
The dynamic hierarchical Dirichlet process (dHDP) is developed to model the timeevolving statistical properties of sequential data sets. The data collected at any time point are r...
ICASSP
2011
IEEE
14 years 1 months ago
2011
IEEE
The Dirichlet Process Mixture (DPM) models represent an attractive approach to modeling latent distributions parametrically. In DPM models the Dirichlet process (DP) is applied es...