2007
NeurIPS
NeurIPS 2007
Cooled and Relaxed Survey Propagation for MRFs
Abstract
We describe a new algorithm, Relaxed Survey Propagation (RSP), for finding MAP configurations in Markov random fields. We compare its performance with state-of-the-art algorithms including the max-product belief propagation, its se- quential tree-reweighted variant, residual (sum-product) belief propagation, and tree-structured expectation propagation. We show that it outperforms all ap- proaches for Ising models with mixed couplings, as well as on a web person disambiguation task formulated as a supervised clustering problem.
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Interdisciplinary Bridge
- Artificial Intelligence and Data Science & Analytics
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Trend Setter
- Inference
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Keyword Pioneer
- constraint satisfaction
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Hot Topic Early Bird
- markov random field
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Clustering
Machine Learning > Core Methods > Representation Learning
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Optimization & Theory > Statistical Learning
Data Science & Analytics > Applications > Clustering
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Graphical Models
Machine Learning > Core Methods > Inference