Channel importance splits into task relevance and local replaceability; local-axis metrics predict safe removal under pruning better than target-axis metrics across multiple CNNs and datasets.
IEEE Trans
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Meta-analysis of 28 FFS studies shows experimental design choices explain 33% of variance in new method performance against baselines.
EBBS augments the MIO best-subsets objective with an aggregated expert prior expressed as a log-odds penalty so that selected features align with domain consensus while reducing to ordinary best subsets when experts provide no input.
Kernel ridge regression combined with mRMR feature selection improves prediction of full benchmark scores from question subsets over existing efficient benchmarking techniques.
A replicator-type dynamic on the standard simplex for feature weights from a normalized data matrix converges globally to a unique interior equilibrium.
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