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» Complexity of Inference in Graphical Models
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ISBI
2008
IEEE
16 years 3 months ago
Segmentation of the evolving left ventricle by learning the dynamics
We propose a method for recursive segmentation of the left ventricle (LV) across a temporal sequence of magnetic resonance (MR) images. The approach involves a technique for learn...
Walter Sun, Müjdat Çetin, Raymond Chan...
NIPS
2004
15 years 3 months ago
Message Errors in Belief Propagation
Belief propagation (BP) is an increasingly popular method of performing approximate inference on arbitrary graphical models. At times, even further approximations are required, wh...
Alexander T. Ihler, John W. Fisher III, Alan S. Wi...
JMLR
2010
164views more  JMLR 2010»
14 years 9 months ago
Solving the Uncapacitated Facility Location Problem Using Message Passing Algorithms
The Uncapacitated Facility Location Problem (UFLP) is one of the most widely studied discrete location problems, whose applications arise in a variety of settings. We tackle the U...
Nevena Lazic, Brendan J. Frey, Parham Aarabi
PACT
2009
Springer
15 years 7 months ago
Parallel Evidence Propagation on Multicore Processors
In this paper, we design and implement an efficient technique for parallel evidence propagation on state-of-the-art multicore processor systems. Evidence propagation is a major ste...
Yinglong Xia, Xiaojun Feng, Viktor K. Prasanna
NIPS
2004
15 years 3 months ago
Dynamic Bayesian Networks for Brain-Computer Interfaces
We describe an approach to building brain-computer interfaces (BCI) based on graphical models for probabilistic inference and learning. We show how a dynamic Bayesian network (DBN...
Pradeep Shenoy, Rajesh P. N. Rao