Model averages sharpened into Occam’s razors: Deep learning enhanced by Rényi entropy
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
Ensemble methods of machine learning combine neural networks or other machine learning models in order to improve predictive performance. The proposed ensemble method is based on Occam’s razor idealized as adjusting hyperprior distributions over models according to a Rényi entropy of the data distribution that corresponds to each model. The entropy-based method is used to average a logistic regression model, a random forest, and a deep neural network. As expected, the deep leaning machine more accurately recognizes handwritten digits than the other two models. The combination of the three models performs even better than the neural network when they are combined according to the entropy-based method or according to methods that average the log odds of the classification probabilities reported by the models. Which of the best ensemble methods to choose for other applications may depend on the loss function that quantifies prediction performance and on a robustness consideration.
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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 | 0.002 | 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 | 0.000 |
| Open science | 0.001 | 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