Design optimization of code-excited neural vector quantizers
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 LBG algorithm is the most common and important algorithm of classical vector quantization (VQ) for speech or image signal compression. However, this algorithm has two major weaknesses. First, its encoding complexity grows exponentially with the product of coding rate and vector dimension and the storage requirement of the codebook increases linearly with the product. Secondly, it easily gets trapped in local minima of the distortion surface, resulting in a suboptimal vector quantizer. Neural vector quantizers have been developed to overcome the first problem. To solve the second problem, a class of randomized search algorithms such as simulated annealing and cauchy annealing have been applied to codebook designs. This paper presents a method to solve the two problems simultaneously with globally optimal code-excited neural vector quantizers (CENVQs), which applies annealing procedures to global optimization of CENVQs. Comparisons among the different vector quantizers are presented for several data sources.
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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 | 0.001 | 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 it