AWhat changed
Estimated connection sensitivity through derivatives of multiplicative masks, retaining high-sensitivity weights before training the resulting sparse network.
Scientific scope
Estimated connection sensitivity through derivatives of multiplicative masks, retaining high-sensitivity weights before training the resulting sparse network. The criterion approximates discrete removal effects using an infinitesimal derivative at initialization and is a heuristic, not a global solution to constrained training. It uses task data; it is not data-free. High unstructured sparsity does not itself guarantee measured runtime acceleration or preserve accuracy on every architecture.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. The criterion approximates discrete removal effects using an infinitesimal derivative at initialization and is a heuristic, not a global solution to constrained training. It uses task data; it is not data-free. High unstructured sparsity does not itself guarantee measured runtime acceleration or preserve accuracy on every architecture.
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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