Bibliographic record
Abstract
Optimal context quantization with respect to the minimum conditional entropy (MCECQ) is proven to be an efficient way for high order statistical modeling and model complexity reduction in data compression systems. The MCECQ merges together contexts that have similar statistics to reduce the size of the original model. In this technique, the number of output clusters (the model size) must be set before quantization. Optimal model size for the given data is not usually known in advance. We extend the MCECQ technique to a multi-model approach for context modeling, which overcomes this problem and gives the possibilities for better fitting the model to the actual data. The method is primarily intended for image compression algorithms. In our experiments, we applied the proposed technique to embedded conditional bit-plane entropy coding of wavelet transform coefficients. We show that the performance of the proposed modeling achieves the performance of the optimal model of fixed size found individually for given data using MCECQ (and in most cases it is even slightly better).
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".