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CORR
2012
Springer
170views Education» more  CORR 2012»
12 years 5 days ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson
CGF
2010
156views more  CGF 2010»
13 years 4 months ago
Mixed Finite Elements for Variational Surface Modeling
Many problems in geometric modeling can be described using variational formulations that define the smoothness of the shape and its behavior w.r.t. the posed modeling constraints....
Alec Jacobson, Elif Tosun, Olga Sorkine, Denis Zor...
JMLR
2010
143views more  JMLR 2010»
12 years 11 months ago
Incremental Sigmoid Belief Networks for Grammar Learning
We propose a class of Bayesian networks appropriate for structured prediction problems where the Bayesian network's model structure is a function of the predicted output stru...
James Henderson, Ivan Titov
PCM
2004
Springer
168views Multimedia» more  PCM 2004»
13 years 9 months ago
Approximating Inference on Complex Motion Models Using Multi-model Particle Filter
Abstract. Due to its great ability of conquering clutters, which is especially useful for high-dimensional tracking problems, particle filter becomes popular in the visual trackin...
Jianyu Wang, Debin Zhao, Shiguang Shan, Wen Gao
ICML
2010
IEEE
13 years 5 months ago
Continuous-Time Belief Propagation
Many temporal processes can be naturally modeled as a stochastic system that evolves continuously over time. The representation language of continuous-time Bayesian networks allow...
Tal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman