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AAAI
2011
14 years 1 months ago
Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models
Coarse-to-fine approaches use sequences of increasingly fine approximations to control the complexity of inference and learning. These techniques are often used in NLP and visio...
Chloe Kiddon, Pedro Domingos
MICCAI
2006
Springer
16 years 2 months ago
Probabilistic Brain Atlas Encoding Using Bayesian Inference
This paper addresses the problem of creating probabilistic brain atlases from manually labeled training data. We propose a general mesh-based atlas representation, and compare diff...
Koen Van Leemput
ICRA
2003
IEEE
127views Robotics» more  ICRA 2003»
15 years 7 months ago
The bridge test for sampling narrow passages with probabilistic roadmap planners
Probabilistic roadmap (PRM) planners have been successful in path planning of robots with many degrees of freedom, but narrow passages in a robot’s configuration space create s...
David Hsu, Tingting Jiang, John H. Reif, Zheng Sun
SDM
2007
SIAM
162views Data Mining» more  SDM 2007»
15 years 3 months ago
Probabilistic Joint Feature Selection for Multi-task Learning
We study the joint feature selection problem when learning multiple related classification or regression tasks. By imposing an automatic relevance determination prior on the hypo...
Tao Xiong, Jinbo Bi, R. Bharat Rao, Vladimir Cherk...
IJCAI
2001
15 years 3 months ago
Complexity of Probabilistic Planning under Average Rewards
A general and expressive model of sequential decision making under uncertainty is provided by the Markov decision processes (MDPs) framework. Complex applications with very large ...
Jussi Rintanen