Accelerating General-purpose Lossless Compression via Simple and Scalable Parameterization
Bibliographic record
Abstract
The storage of multi-media data can benefit from the advancements in general-purpose lossless compression. The explosive growth of multi-media data volume in data centers demands a higher compression ratio and better compressors' run-time speed. However, recent deep-learning-based compressors with a high compression ratio usually build complicated dependencies on history symbols, leading to a long compression time. This paper investigates the behavior of historical symbols and finds an approximate order of importance. Namely, recent symbols have a substantially larger influence on the probability estimation of the next unknown symbol. This observation guides the designing of an interpretable structure for data compression, rather than learning implicitly from data like Recurrent Neural Network (RNN) and attention. Based on this observation, we disentangle the compression model into order learning and feature learning, which were fused in a large module in previous works. A parameterized ordered mask unit is established to learn the ordered importance of history symbols. A fast Multi-Layer Perceptron (MLP) network is designed for efficient feature learning. The proposed compressor can improve both compression performance and computational efficiency compared with transformer-based or RNN-based compressors. To further enhance computational efficiency, we propose a branch-MLP block to replace the original MLP layer. This block reduces the parameters and the FLOPs of the original MLP to a half, without sacrificing compression performance. Experiments on multi-media data demonstrate that our model improves the compression ratio by 10% on average across data domains while accelerating compression speed by 100% compared with the state-of-the-art. The source code and appendix are released at https://github.com/mynotwo/compressor_via_simple_and_scalable_parameterization.git.
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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 | 0.000 | 0.002 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".