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Forward-Forward learning explained via likelihood-ratio tests

By PulseAugur Editorial · [3 sources] ·

A new paper proposes a likelihood-ratio account for the Forward-Forward (FF) learning algorithm, suggesting that its core components are not mere heuristics. The research posits that the scalar goodness metric, defined as the sum of squared activations, acts as a sufficient statistic for a likelihood-ratio test. This framework extends to anisotropic and heavy-tailed populations, yielding generalized goodness measures and explaining phenomena like divisive normalization. AI

IMPACT Provides a theoretical framework for understanding and potentially improving the Forward-Forward learning algorithm.

RANK_REASON The cluster contains an academic paper detailing a new theoretical account of an existing machine learning algorithm.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Forward-Forward learning explained via likelihood-ratio tests

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COVERAGE [3]

  1. arXiv stat.ML TIER_1 English(EN) · Paolo Giannitrapani ·

    What Does Goodness Measure? A Likelihood-Ratio Account of Forward-Forward Learning

    arXiv:2607.12501v1 Announce Type: cross Abstract: The Forward-Forward (FF) algorithm trains each layer locally, so that a scalar goodness - the sum of squared activations - is high on real inputs and low on contrastive ones, with activations normalized between layers. Both choice…

  2. arXiv stat.ML TIER_1 English(EN) · Paolo Giannitrapani ·

    What Does Goodness Measure? A Likelihood-Ratio Account of Forward-Forward Learning

    The Forward-Forward (FF) algorithm trains each layer locally, so that a scalar goodness - the sum of squared activations - is high on real inputs and low on contrastive ones, with activations normalized between layers. Both choices are usually treated as heuristics. Under an expl…

  3. arXiv stat.ML TIER_1 English(EN) · Paolo Giannitrapani ·

    What Does Goodness Measure? A Likelihood-Ratio Account of Forward-Forward Learning

    The Forward-Forward (FF) algorithm trains each layer locally, so that a scalar goodness - the sum of squared activations - is high on real inputs and low on contrastive ones, with activations normalized between layers. Both choices are usually treated as heuristics. Under an expl…

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