Lottery Ticket Structured Node Pruning for Tabular Datasets
Bibliographic record
Abstract
This paper experiments with well known pruning approaches, iterative and one-shot, and presents a new approach to lottery ticket pruning applied to tabular neural networks based on iterative pruning. Our contribution is a standard model for comparison in terms of speed and performance for tabular datasets that often do not get optimized through research. We show leading results in several tabular datasets that can compete with ensemble approaches. We tested on a wide range of datasets with a general improvement over the original (already leading) model in 6 of 8 datasets tested in terms of F1/RMSE. This includes a total reduction of over 85% of nodes with the additional ability to prune over 98% of nodes with minimal affect to accuracy. The new iterative approach we present will first optimize for lottery ticket quality by selecting an optimal architecture size and weights, then apply the iterative pruning strategy. The new iterative approach shows minimal degradation in accuracy compared to the original iterative approach, but it is capable of pruning models much smaller due to optimal weight pre-selection. Training and inference time improved over 50% and 10%, respectively, and up to 90% and 35%, respectively, for large datasets.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 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".