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JMLR
2010
13 years 2 months ago
2010
We present a new approximate inference algorithm for Deep Boltzmann Machines (DBM's), a generative model with many layers of hidden variables. The algorithm learns a separate...
ECSQARU
2005
Springer
14 years 28 days ago
2005
Springer
In a Bayesian network with continuous variables containing a variable(s) that is a conditionally deterministic function of its continuous parents, the joint density function for t...
NN
1997
Springer
13 years 11 months ago
1997
Springer
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
IJAR
2008
13 years 7 months ago
2008
This paper presents a family of algorithms for approximate inference in credal networks (that is, models based on directed acyclic graphs and set-valued probabilities) that contai...
ICIP
2008
IEEE
14 years 1 months ago
2008
IEEE
In this paper we propose a novel algorithm for super resolution based on total variation prior and variational distribution approximations. We formulate the problem using a hierar...