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» Adaptive inference on general graphical models
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UAI
2008
13 years 6 months ago
Adaptive inference on general graphical models
Many algorithms and applications involve repeatedly solving variations of the same inference problem; for example we may want to introduce new evidence to the model or perform upd...
Umut A. Acar, Alexander T. Ihler, Ramgopal R. Mett...
TNN
1998
123views more  TNN 1998»
13 years 4 months ago
A general framework for adaptive processing of data structures
—A structured organization of information is typically required by symbolic processing. On the other hand, most connectionist models assume that data are organized according to r...
Paolo Frasconi, Marco Gori, Alessandro Sperduti
TSP
2008
179views more  TSP 2008»
13 years 4 months ago
Estimation in Gaussian Graphical Models Using Tractable Subgraphs: A Walk-Sum Analysis
Graphical models provide a powerful formalism for statistical signal processing. Due to their sophisticated modeling capabilities, they have found applications in a variety of fie...
V. Chandrasekaran, Jason K. Johnson, Alan S. Wills...
FTML
2008
185views more  FTML 2008»
13 years 5 months ago
Graphical Models, Exponential Families, and Variational Inference
The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate stat...
Martin J. Wainwright, Michael I. Jordan
ICASSP
2009
IEEE
13 years 8 months ago
Graphical Models: Statistical inference vs. determination
Using discrete Hidden-Markov-Models (HMMs) for recognition requires the quantization of the continuous feature vectors. In handwritten whiteboard note recognition it turns out tha...
Joachim Schenk, Benedikt Hörnler, Artur Braun...