An efficient approach to lattice-based fixed-rate entropy-coded vector quantization
Bibliographic record
Abstract
In the absence of channel noise, variable-length quantizers perform better than fixed rate Lloyd-Max quantizers for any source with a non-uniform density function. How-ever, channel errors can lead to a loss of synchronization resulting in a propagation of error. To avoid having variable rate, one can use a vector quantizer selected as a subset of high probability points in the Cartesian product of a set of scalar quantizers and represent its elements with binary code-words of the same length (quantizer shaping). We choose these elements from a lattice resulting in a higher quantization gain in comparison to simply using the Cartesian product of a set of scalar quantizers. We introduce a class of lattices whichhave alow encoding complexity, and at the same time result in a noticeable quantization gain. We combine the procedure of lattice encoding with that of quantizer shaping using hierarchical dynamic programming. In addition, by devising appropriate partitioning and merging rules, we obtain sub-optimum schemes of low complexity and small performance degradation. The proposed methods show a substantial improvement in performance and/or a reduction in the complexity with respect to the best known results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".