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129
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AAAI
2011
14 years 21 days ago
Sparse Matrix-Variate t Process Blockmodels
We consider the problem of modeling network interactions and identifying latent groups of network nodes. This problem is challenging due to the facts i) that the network nodes are...
Zenglin Xu, Feng Yan, Yuan Qi
109
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DAGM
2003
Springer
15 years 6 months ago
Domain Decomposition for Parallel Variational Optical Flow Computation
We present an approach to parallel variational optical flow computation by using an arbitrary partition of the image plane and iteratively solving related local variational proble...
Timo Kohlberger, Christoph Schnörr, Andr&eacu...
81
Voted
NIPS
2007
15 years 2 months ago
Collapsed Variational Inference for HDP
A wide variety of Dirichlet-multinomial ‘topic’ models have found interesting applications in recent years. While Gibbs sampling remains an important method of inference in su...
Yee Whye Teh, Kenichi Kurihara, Max Welling
113
Voted
PAMI
2008
145views more  PAMI 2008»
15 years 19 days ago
Latent-Space Variational Bayes
Variational Bayesian Expectation-Maximization (VBEM), an approximate inference method for probabilistic models based on factorizing over latent variables and model parameters, has ...
JaeMo Sung, Zoubin Ghahramani, Sung Yang Bang
95
Voted
GECCO
2006
Springer
186views Optimization» more  GECCO 2006»
15 years 4 months ago
Characterizing large text corpora using a maximum variation sampling genetic algorithm
An enormous amount of information available via the Internet exists. Much of this data is in the form of text-based documents. These documents cover a variety of topics that are v...
Robert M. Patton, Thomas E. Potok