AWhat changed
Reparameterized neuron weight vectors by a scalar norm and normalized direction, deriving gradients and studying optimization without minibatch-dependent weight normalization.
Scientific scope
Reparameterized neuron weight vectors by a scalar norm and normalized direction, deriving gradients and studying optimization without minibatch-dependent weight normalization. Inspired by batch normalization and earlier reparameterization methods. Normalizes parameters, not guaranteed zero-mean/unit-variance activations; data-dependent initialization and a mean-only batch-normalization variant are separate components. Reported optimization improvements are not a universal Hessian-conditioning or convergence theorem.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inspired by batch normalization and earlier reparameterization methods. Normalizes parameters, not guaranteed zero-mean/unit-variance activations; data-dependent initialization and a mean-only batch-normalization variant are separate components. Reported optimization improvements are not a universal Hessian-conditioning or convergence theorem.
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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