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3.Depth dependence: The product QD i=j+1 C0Ci shows how spectral norms accumulate through the network depth

Data dependence: The bound explicitly depends on ∥X∥ F , the norm of the training inputs, unlike PAC-Bayes bounds which depend on the posterior-prior KL divergence

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Singular Bayesian Neural Networks

stat.ML · 2026-01-30 · unverdicted · novelty 7.0

Low-rank weight factorization creates singular posteriors in Bayesian neural networks that scale as sqrt(r(m+n)) in complexity and use up to 33x fewer parameters than ensembles.

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  • Singular Bayesian Neural Networks stat.ML · 2026-01-30 · unverdicted · none · ref 9

    Low-rank weight factorization creates singular posteriors in Bayesian neural networks that scale as sqrt(r(m+n)) in complexity and use up to 33x fewer parameters than ensembles.