AWhat changed
Ba, Kiros and Hinton introduced layer normalization, computing activation mean and variance within each example instead of across a mini-batch, with learnable gains and biases and a recurrent-network formulation.
Scientific scope
Ba, Kiros and Hinton introduced layer normalization, computing activation mean and variance within each example instead of across a mini-batch, with learnable gains and biases and a recurrent-network formulation. Normalization is within one example, with learnable gain/bias and specified recurrent statistics. The stated invariances need their scaling/shift and nonzero-variance assumptions; they do not include arbitrary input translations or every per-unit transformation. Fixed numerical stabilizers can alter exact invariance. The experiments concern the reported architectures and training settings, not a universal performance or convergence guarantee.
Evidence
Source-linked, scoped account of the original work. The published scope states the scientific assumptions and remaining verification limits. WRS has not independently certified the full proof, experimental result or system benchmark.
Contributors
- Jimmy Lei Ba · SCIENCE: Original source-listed scientific author of this scoped work; byline does not determine effort fractions.
- Jamie Ryan Kiros · SCIENCE: Original source-listed scientific author of this scoped work; byline does not determine effort fractions.
- Geoffrey E. Hinton · SCIENCE: Original source-listed scientific author of this scoped work; byline does not determine effort fractions.
- Ba, Kirosand Hinton, Layer Normalization, exact 2016 ar Xiv 1607.06450 v 1
- Ba, Kirosand Hinton, Layer Normalization, exact 2016 ar Xiv 1607.06450 v 1 repositoryidentity
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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