Relaxed Gaussian Belief Propagation
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
The Gaussian Belief Propagation (GaBP) algorithm executed on Gaussian Markov Random Fields can take a large number of iterations to converge if the inverse covariance matrix of the underlying Gaussian distribution is ill-conditioned and weakly diagonally dominant. Such matrices can arise from many practical problem domains. In this study, we propose a relaxed GaBP algorithm that results in a significant reduction in the number of GaBP iterations (of up to 12.7 times). We also propose a second relaxed GaBP algorithm that avoids the need of determining the relaxation factor a priori which can also achieve comparable reductions in iterations by only setting two basic heuristic measures. We show that the new algorithms can be implemented without any significant increase, over the original GaBP, in both the computational complexity and the memory requirements. We also present detailed experimental results of the new algorithms and demonstrate their effectiveness in achieving significant reductions in the iteration count.
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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 | 0.000 |
| 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) | 0.000 | 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