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119
Voted
ICRA
2010
IEEE
15 years 1 months ago
2010
IEEE
Abstract— We consider the problem of apprenticeship learning when the expert’s demonstration covers only a small part of a large state space. Inverse Reinforcement Learning (IR...
127
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IJAR
2010
15 years 1 months ago
2010
We consider the problem of learning Bayesian network models in a non-informative setting, where the only available information is a set of observational data, and no background kn...
213
Voted
ICIP
2005
IEEE
16 years 4 months ago
2005
IEEE
In this paper, we propose a novel framework for face super-resolution based on a layered predictor network. In the first layer, multiple predictors are trained online with a dynami...
113
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ICMCS
2005
IEEE
15 years 8 months ago
2005
IEEE
The ability of humans for color constancy, i.e. the ability to correct for color deviation caused by a different illumination, is far beyond computer vision performances: nowadays...
114
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AAAI
2008
15 years 4 months ago
2008
Modern Bayesian Network learning algorithms are timeefficient, scalable and produce high-quality models; these algorithms feature prominently in decision support model development...