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New SCROLL method optimizes Bayesian neural networks via Bethe free energy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for training Bayesian neural networks called SCROLL (Shared-Cavity fRee-rOuting Last-Layer). This approach optimizes the Bethe free energy rather than the typical evidence lower bound (ELBO), aiming for local consistency between network components. SCROLL is designed for single-pass training and can handle various likelihood functions, showing improved performance in predictive accuracy and calibration across several benchmark datasets. AI

IMPACT Introduces a novel training objective for Bayesian neural networks that may improve performance and efficiency.

RANK_REASON Academic paper detailing a new method for training Bayesian neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SCROLL method optimizes Bayesian neural networks via Bethe free energy

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Academic paper detailing a new method for training Bayesian neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Pavel Prochazka ·

    Direct Bethe Free Energy Minimization for Bayesian Neural Networks

    arXiv:2605.08446v3 Announce Type: replace Abstract: Bayesian neural networks are typically trained against the evidence lower bound (ELBO), whose Jensen gap closes only when the variational posterior is exact. We instead train by local consistency: gradient descent on the Bethe f…

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