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A Tensorial Affine Projection Algorithm

2021· article· en· W3190122703 on OpenAlex
Laura-Maria Dogariu, Camelia Elisei-Iliescu, Constantin Paleologu, Jacob Benesty, Silviu Ciochină

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
FieldMathematics
TopicTensor decomposition and applications
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
FundersMinistry of Education
KeywordsMultilinear mapIdentification (biology)Affine transformationTensor (intrinsic definition)Projection (relational algebra)AlgorithmComputer scienceSystem identificationSpace (punctuation)MathematicsData modelingPure mathematics

Abstract

fetched live from OpenAlex

The affine projection algorithm (APA) represents a popular choice in system identification scenarios, especially with correlated input signals. In this paper, we address the multilinear identification problem, in the framework of multiple-input single-output systems. The main challenge consists of a large parameter space, which can be efficiently reshaped into a tensorial form. The proposed tensor-based APA is tailored for the identification of such multilinear forms, since it combines the solutions provided by the individual (shorter) filters. Simulation results indicate the appealing performance of this algorithm.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.297
Threshold uncertainty score0.999

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.338
Teacher spread0.291 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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