AWhat changed
Used learned transform/carry gates to interpolate between nonlinear updates and identity paths in feed-forward layers, enabling directly trained very deep networks.
Scientific scope
Used learned transform/carry gates to interpolate between nonlinear updates and identity paths in feed-forward layers, enabling directly trained very deep networks. Inherits LSTM gating and deep-network initialization; distinct from later ungated residual-network design, not invention of all skip connections. Initial sigmoid gates approximate but never exactly attain hard zero/one; hundreds-layer experiments do not prove optimization independent of arbitrary depth or problem.
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 LSTM gating and deep-network initialization; distinct from later ungated residual-network design, not invention of all skip connections. Initial sigmoid gates approximate but never exactly attain hard zero/one; hundreds-layer experiments do not prove optimization independent of arbitrary depth or problem.
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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