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Bayesian Neural Networks: Infinite Width Matches Polynomial Width Learnability

By PulseAugur Editorial · [2 sources] ·

Researchers have published a paper detailing a width-robust learnability theorem for mean-field Bayesian neural networks. The study establishes that for Boolean-cube targets, learnability at infinite width is equivalent to learnability at polynomial width, provided the reduced entropy is polynomially bounded. This finding suggests that the infinite-width limit accurately describes learning without introducing spurious generalization power, by effectively subsampling neurons while preserving learned functions. AI

IMPACT This research provides theoretical grounding for understanding neural network behavior at scale, potentially informing future model architectures and training methodologies.

RANK_REASON The cluster contains an academic paper published on arXiv detailing theoretical research on Bayesian Neural Networks.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Bayesian Neural Networks: Infinite Width Matches Polynomial Width Learnability

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The cluster contains an academic paper published on arXiv detailing theoretical research on Bayesian Neural Networks.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Dmitry Vaintrob, Kaarel H\"anni ·

    Width-Robust Learnability in Mean-Field Bayesian Neural Networks

    arXiv:2607.05735v1 Announce Type: new Abstract: Infinite-width limits are a standard way to reason about neural networks, but it is not automatic that the limiting learner has the same complexity-theoretic inductive bias as large finite networks. We study this question for Bayesi…

  2. arXiv stat.ML TIER_1 English(EN) · Kaarel Hänni ·

    Width-Robust Learnability in Mean-Field Bayesian Neural Networks

    Infinite-width limits are a standard way to reason about neural networks, but it is not automatic that the limiting learner has the same complexity-theoretic inductive bias as large finite networks. We study this question for Bayesian neural networks at the mean-field, or critica…

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