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» Aggregating Learned Probabilistic Beliefs
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AAAI
2011
13 years 9 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
KDD
2005
ACM
177views Data Mining» more  KDD 2005»
15 years 3 months ago
Combining partitions by probabilistic label aggregation
Data clustering represents an important tool in exploratory data analysis. The lack of objective criteria render model selection as well as the identification of robust solutions...
Tilman Lange, Joachim M. Buhmann
ICMLA
2010
14 years 7 months ago
A Probabilistic Graphical Model of Quantum Systems
Quantum systems are promising candidates of future computing and information processing devices. In a large system, information about the quantum states and processes may be incomp...
Chen-Hsiang Yeang
ISIPTA
2005
IEEE
125views Mathematics» more  ISIPTA 2005»
15 years 3 months ago
Evidential modeling for pose estimation
Pose estimation involves reconstructing the configuration of a moving body from images sequences. In this paper we present a general framework for pose estimation of unknown obje...
Fabio Cuzzolin, Ruggero Frezza
AAAI
2010
14 years 11 months ago
Efficient Belief Propagation for Utility Maximization and Repeated Inference
Many problems require repeated inference on probabilistic graphical models, with different values for evidence variables or other changes. Examples of such problems include utilit...
Aniruddh Nath, Pedro Domingos