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SynFlow: data-free iterative pruning with conserved synaptic-flow scores

Contribution grade

Sparse neural-network training · Human-designed research method or resource

Reference score125,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Derived saliency conservation relations for homogeneous feedforward networks and used positive flow scores with iterative pruning to reduce premature whole-layer collapse without training data.

Scientific scope

Derived saliency conservation relations for homogeneous feedforward networks and used positive flow scores with iterative pruning to reduce premature whole-layer collapse without training data. Conservation assumes the stated homogeneous feedforward setting. The maximal-critical-compression theorem requires positive conservative scores re-evaluated for every removal; practical finite batches of pruning are not automatically the same theorem. Avoiding an empty layer is not a theorem of useful accuracy, connected information flow in arbitrary architectures or hardware speedup. Empirical finite schedules are reported separately.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Conservation assumes the stated homogeneous feedforward setting. The maximal-critical-compression theorem requires positive conservative scores re-evaluated for every removal; practical finite batches of pruning are not automatically the same theorem. Avoiding an empty layer is not a theorem of useful accuracy, connected information flow in arbitrary architectures or hardware speedup. Empirical finite schedules are reported separately.

Contributors

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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