Multidimensional autoregressive parameter estimation using iteratively reweighted least squares
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.
Bibliographic record
Abstract
Two-dimensional robust autoregressive parameter estimation is performed on image data using an iteratively reweighted least squares (IRLS) procedure which explicitly identifies the model outliers. In practice, these outliers often arise from nonhomogeneous image structures. An initial least median of squares estimate is used to obtain a more robust version of IRLS. Both versions of the IRLS algorithm are tested experimentally on synthetic and real image data. A whiteness measure, based on a two-dimensional version of the Box and Pierce portmanteau test, serves as a useful performance evaluator. The experimental results demonstrate that the robust parameter estimators can offer significant improvement over the classical least-squares estimator on image data that deviates from the autoregressive model. These results have potential applications in image processing, including image coding and object detection.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it