BWhat changed
Uses trained split-data classifiers and Bayesian policy exploration to find augmentation policies whose transformed examples match a learned data distribution, then retrains on the augmented full training set.
Scientific scope
Uses trained split-data classifiers and Bayesian policy exploration to find augmentation policies whose transformed examples match a learned data distribution, then retrains on the augmented full training set. Reduced search cost is not absence of search or final model training; search-time comparisons are hardware/setting dependent. First two authors contribute equally.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Reduced search cost is not absence of search or final model training; search-time comparisons are hardware/setting dependent. First two authors contribute equally.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
Full scientific record · Browse contribution grades · Request a correction