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.
Rather than under-confident (overfitting) predictions, the low-rank posterior spreads mass more broadly, yielding conservative predictions even in-domain
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Singular Bayesian Neural Networks
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.