Selectivity and Shape in the Design of Forward-Forward Goodness Functions
The Forward-Forward (FF) algorithm trains networks layer-by-layer using a local "goodness function," yet sum-of-squares (SoS) has remained the only choice studied. We systematically explore the goodness-function design space and identify a unifying principle: the goodness function must be sensitive to the shape of neural activity, not its total energy. This principle is motivated by the observation that deep network activations follow heavy-tailed distributions and that discriminative information is often concentrated in peak activities. We propose two complementary families: selective functions (top-k, entmax-weighted energy) that measure only peak activity, and shape-sensitive functions...
| Authors | Talha Ruzgar Akkus, Suayp Talha Kocabay, Kamer Ali Yuksel et al. |
|---|---|
| Primary category | cs.LG |
| Published | 28 March 2026 |
| Last updated at source | 16 April 2026 |
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