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Record W2148339888 · doi:10.1109/ccece.2007.123

Reduced Complexity Belief Propagation Algorithm Based on Iterative Groupwise Multiuser Detection

2007· article· en· W2148339888 on OpenAlexaff
Sara Bavarian, J.K. Cavers

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBelief propagationMultiuser detectionComputer scienceAlgorithmIterative methodComputational complexity theoryDetectorJoint (building)Maximum a posteriori estimationFunction (biology)MathematicsMaximum likelihoodDecoding methodsEngineeringTelecommunications

Abstract

fetched live from OpenAlex

We propose a new method to reduce the complexity of belief propagation algorithm (BP) using an iterative groupwise multiuser detection approach. Replacing the optimal joint maximum a posteriori (JMAP) detectors in BP function nodes by the iterative multiuser detection algorithm (IMUD) reduces the computational load of BP. We explain why IMUD is a good choice for this task and investigate the performance of this reduced complexity BP via simulation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.927
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.288
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2007
Admission routes1
Has abstractyes

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