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» Exploring Parallelism in Learning Belief Networks
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UAI
1994
13 years 8 months ago
Global Conditioning for Probabilistic Inference in Belief Networks
In this paper we propose a new approach to probabilistic inference on belief networks, global conditioning, which is a simple generalization of Pearl's (1986b) method of loop...
Ross D. Shachter, Stig K. Andersen, Peter Szolovit...
CVPR
2005
IEEE
14 years 9 months ago
Learning to Estimate Human Pose with Data Driven Belief Propagation
We propose a statistical formulation for 2-D human pose estimation from single images. The human body configuration is modeled by a Markov network and the estimation problem is to...
Gang Hua, Ming-Hsuan Yang, Ying Wu
ICML
2006
IEEE
14 years 7 months ago
Using query-specific variance estimates to combine Bayesian classifiers
Many of today's best classification results are obtained by combining the responses of a set of base classifiers to produce an answer for the query. This paper explores a nov...
Chi-Hoon Lee, Russell Greiner, Shaojun Wang
ICS
2009
Tsinghua U.
13 years 11 months ago
Exploring pattern-aware routing in generalized fat tree networks
New static source routing algorithms for High Performance Computing (HPC) are presented in this work. The target parallel architectures are based on the commonly used fattree netw...
Germán Rodríguez, Ramón Beivi...
SKG
2006
IEEE
14 years 1 months ago
Using Bayesian Networks to Implement Adaptivity in Mobile Learning
Mobile learning technologies have the potential to revolutionize distance education by bringing the concept of anytime and anywhere to reality. However, the development of mobile ...
Dan Yu, Xinmeng Chen