arXiv: cs.AI preprintsInternational6 November 2026
Selectivity and Shape in the Design of Forward-Forward Goodness Functions
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arXiv:2604.13081v3 Announce Type: replace-cross Abstract: The Forward-Forward (FF) algorithm trains networks layer-by-layer using a local "goodness function," yet the choice of activity statistic remains an important design question. We systematically study how selectivity and activity shape affect FF learning, introducing selective goodness functions based on top-$k$ and entmax-weighted energy alongside shape-sensitive objectives based on normalized higher-order moments. In particular, we propose burstiness goodness, a parameter-free, scale-invariant excess-kurtosis objective that rewards hea
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