2007
NIPS
NeurIPS 2007
CPR for CSPs: A Probabilistic Relaxation of Constraint Propagation
Abstract
This paper proposes constraint propagation relaxation (CPR), a probabilistic approach to classical constraint propagation that provides another view on the whole parametric family of survey propagation algorithms SP(ρ), ranging from belief propagation (ρ = 0) to (pure) survey propagation(ρ = 1). More importantly, the approach elucidates the implicit, but fundamental assumptions underlying SP(ρ), thus shedding some light on its effectiveness and leading to applications beyond k-SAT.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
📈
Trend Setter
— Causal Inference
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Keyword Pioneer
— constraint propagation
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Data Science & Analytics, Deep Learning, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics
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Hot Topic Early Bird
— constraint satisfaction
Authors
Topics
Artificial Intelligence > Core AI > Causal Inference
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Probabilistic Modeling
Machine Learning > Core Methods > Structured Prediction