Entropy‐based variational Bayes learning framework for data clustering
Why this work is in the frame
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Bibliographic record
Abstract
A novel framework is developed for the modelling and clustering of proportional data (i.e. normalised histograms) based on the Beta‐Liouville mixture model. This framework is based on incremental model selection, by testing if a given component was truly Beta‐Liouville distributed. Specifically, the authors compare the theoretical maximum entropy of the given component with the estimated entropy obtained by the MeanNN estimator. If a significant difference was gained from this comparison, this component is considered as not well fitted and is then splitted into two new components with a proper initialisation. Our approach is tested through synthetic data sets and real‐world applications which involve human gesture recognition and vehicle tracking for traffic monitoring purposes, which demonstrate that the authors' approach is superior to comparable techniques.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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