AWhat changed
Applied sparsity penalties to channel scaling factors, notably batch-normalization gamma, then removed low-scale channels and fine-tuned the narrower dense network.
Scientific scope
Applied sparsity penalties to channel scaling factors, notably batch-normalization gamma, then removed low-scale channels and fine-tuned the narrower dense network. Inherits channel pruning, batch normalization and sparsity regularization. Training, pruning and fine-tuning remain necessary; the search changes layer widths rather than discovering arbitrary architectures. Residual/pre-activation networks require channel-selection handling. Smaller operation counts do not prescribe identical latency gains across platforms.
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 channel pruning, batch normalization and sparsity regularization. Training, pruning and fine-tuning remain necessary; the search changes layer widths rather than discovering arbitrary architectures. Residual/pre-activation networks require channel-selection handling. Smaller operation counts do not prescribe identical latency gains across platforms.
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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