AWhat changed
Learned negative-side rectifier slopes jointly with network weights and analyzed initialization adapted to rectifier nonlinearities, studying deep image classifiers.
Scientific scope
Learned negative-side rectifier slopes jointly with network weights and analyzed initialization adapted to rectifier nonlinearities, studying deep image classifiers. Inherits ReLU/leaky-ReLU and earlier initialization analysis; not ResNet's later residual architecture. Surpassing human-level refers to a particular ImageNet top-five evaluation, not general human visual intelligence. Small parameter overhead is not proof of no overfitting; initialization arguments use model/distribution assumptions.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits ReLU/leaky-ReLU and earlier initialization analysis; not ResNet's later residual architecture. Surpassing human-level refers to a particular ImageNet top-five evaluation, not general human visual intelligence. Small parameter overhead is not proof of no overfitting; initialization arguments use model/distribution assumptions.
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.
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