Name
Abstract | ||
|---|---|---|
Gaussian and quadratic approximations of message passing algorithms on graphs have attracted considerable recent attention due to their computational simplicity, analytic tractability, and wide applicability in optimization and statistical inference problems. This paper presents a systematic framework for incorporating such approximate message passing (AMP) methods in general graphical models. The... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TSP.2017.2713759 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Message passing,Inference algorithms,Signal processing algorithms,Optimization,Graphical models,Approximation algorithms,Standards | Discrete mathematics,Central limit theorem,Computer science,Quadratic equation,Algorithm,Theoretical computer science,Gaussian,Statistical inference,Graphical model,Message passing,Belief propagation,Variational message passing | Journal |
Volume | Issue | ISSN |
65 | 17 | 1053-587X |
Citations | PageRank | References |
2 | 0.39 | 27 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sundeep Rangan | 1 | 3101 | 163.90 |
| Alyson K. Fletcher | 2 | 552 | 41.10 |
| Vivek K. Goyal | 3 | 2031 | 171.16 |
| Evan Byrne | 4 | 2 | 0.73 |
| Philip Schniter | 5 | 1620 | 93.74 |