Bibliographic record
Abstract
Machine Learning (ML) applications are proliferating in the enterprise. Increasingly enterprise data are used to build sophisticated ML models to assist critical business functions. Relational data which are prevalent in enterprise applications are typically normalized; as a result data have to be denormalized via primary/foreign-key joins to be provided as input to ML algorithms. In this paper we study the implementation of popular nonlinear ML models and in particular independent Gaussian Mixture Models (IGMM) over normalized data. For the case of IGMM we propose algorithms taking the statistical properties of the Gaussians into account to construct mixture models, factorizing the computation. In that way we demonstrate that we can conduct the training of the models much faster compared to other applicable approaches, without any loss in accuracy. We present the results of a thorough experimental evaluation, varying several parameters of the input relations involved and demonstrate that our proposals both for the case of IGMM yield drastic performance improvements which become increasingly higher as parameters of the underlying data vary, without any loss in accuracy.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".