FFR: Forward-Forward Learning for Regression

Excerpt

arXiv:2606.03927v2 Announce Type: replace Abstract: The Forward-Forward (FF) algorithm offers a computationally efficient and biologically plausible alternative to backpropagation (BP) by training neural networks through purely local, layer-wise optimization. However, FF is inherently designed for classification via contrastive positive-negative sample pairs, and extending it to regression poses fundamental challenges: continuous target space lacks natural "opposites" for contrastive learning, a