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1,936 claims from 1,194 papers are on the record. 46 have been checked so far; the other 1,890 have no check with a result yet.

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Status: Unchecked ×Keyword: message passing ×Clear all

6 claims from 5 papers

1. Computer Science › Bayesian Modeling and Causal Inference

   Factor graphs and the sum-product algorithm

   Kschischang, Frey and Loeliger · IEEE Transactions on Information Theory · 2001

   This tutorial presents the sum-product algorithm, a message-passing method on factor graphs, and shows that many algorithms in AI, signal processing and communications are special cases of it.

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   1. UncheckedA single simple rule in the sum-product algorithm computes, exactly or approximately, various marginal functions derived from a complicated global function.“Following a single, simple computational rule, the sum-product algorithm computes-either exactly or approximately-various marginal functions derived from the global function.”

2. Materials Science › Machine Learning in Materials Science

   Atomistic Line Graph Neural Network for improved materials property predictions

   Choudhary and DeCost · npj Computational Materials · 2021

   The authors present ALIGNN, a graph neural network that passes messages on both the atomic bond graph and its line graph of bond angles, and report better accuracy on many property predictions.

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   1. UncheckedAdding bond-angle information to a graph neural network for atoms can be done efficiently and improved performance on several atomistic prediction tasks.“We demonstrate that angle information can be explicitly and efficiently included, leading to improved performance on multiple atomistic prediction tasks.”

3. Computer Science › Constraint Satisfaction and Optimization

   Survey propagation: an algorithm for satisfiability

   Braunstein, Mézard and Zecchina · arXiv (Cornell University) · 2002

   The paper presents survey propagation, a message-passing algorithm that passes surveys over clusters of solutions to tackle random satisfiability problems close to the hard threshold.

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   1. UncheckedThe authors present a new message-passing algorithm that can efficiently find a satisfying assignment for random formulas in the hardest region of the problem.“We introduce a new type of message passing algorithm which allows to find efficiently a satisfiable assignment of the variables in the difficult region.”

4. Computer Science › Constraint Satisfaction and Optimization

   A new look at survey propagation and its generalizations

   Maneva, Mossel and Wainwright · Journal of the ACM · 2007

   The paper gives a new view of survey propagation for random k-SAT by linking it to Markov random fields, partial assignments and cores, and to belief propagation on the uniform distribution over solutions.

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   1. UncheckedApplying belief propagation to a new family of Markov random fields recovers known algorithms, from survey propagation (ρ = 1) to standard belief propagation (ρ = 0).“We then show that applying belief propagation---a well-known “message-passing” technique for estimating marginal probabilities---to this family of MRFs recovers a known family of algorithms, ranging from pure survey propagation at one extreme (ρ = 1) to standard belief propagation on the uniform di…”
   2. UncheckedAny of the paper's Markov random fields with ρ above 0 can be seen as a smoothed version of the uniform distribution over satisfying assignments.“To that end, we investigate the associated lattice structure, and prove a weight-preserving identity that shows how any MRF with ρ > 0 can be viewed as a “smoothed” version of the uniform distribution over satisfying assignments (ρ = 0).”

5. Computer Science › Constraint Satisfaction and Optimization

   On the cavity method for decimated random constraint satisfaction problems and the analysis of belief propagation guided decimation algorithms

   Ricci-Tersenghi and Semerjian · Journal of Statistical Mechanics Theory and Experiment · 2009

   The paper develops a cavity method for partly decimated random constraint satisfaction problems, uses it to analyse belief-propagation-guided decimation algorithms, and compares the theory with numerical simulations.

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   1. UncheckedThe paper introduces a cavity-method variant for sparse random spin models that computes quantities akin to the Franz-Parisi quenched potential.“We introduce a version of the cavity method for diluted mean-field spin models that allows the computation of thermodynamic quantities similar to the Franz-Parisi quenched potential in sparse random graph models.”