RESEARCHarXiv CS.LG·4/16/2026
Sparse Goodness: How Selective Measurement Transforms Forward-Forward Learning
This research systematically studies and enhances the Forward-Forward (FF) algorithm by redesigning its local goodness function, which distinguishes positive from negative data. It introduces 'top-k goodness' and 'entmax-weighted energy,' demonstrating substantial accuracy improvements on benchmarks like Fashion-MNIST.
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