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Record W2152004013 · doi:10.1109/spawc.2005.1506272

The factor graph EM algorithm: applications for LDPC codes

2005· article· en· W2152004013 on OpenAlex
Andrew W. Eckford

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFactor graphLow-density parity-check codeComputer scienceAlgorithmDecoding methodsFadingBlock codeMessage passingTanner graphFactor (programming language)Error floorCoding (social sciences)Channel codeTheoretical computer scienceMathematicsParallel computing

Abstract

fetched live from OpenAlex

The factor graph EM (FGEM) algorithm is introduced, which is a way of describing the EM algorithm as a message-passing algorithm over a factor graph. Some interesting properties of this algorithm are discussed, such as its ability to break certain cycles in factor graphs. Systems with LDPC codes are used as a starting point for practical applications of the FGEM algorithm. In particular, in channels with an unknown channel state, FGEM-based estimation-decoding algorithms can be straightforwardly obtained, and specific examples are given using the block fading channel. Applications for LDPC-based source coding, especially the Slepian-Wolf coding problem, are also given.

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.

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)0.000
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: Methods
Teacher disagreement score0.926
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.291
Teacher spread0.272 · 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

Quick stats

Citations9
Published2005
Admission routes1
Has abstractyes

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