#bayesian-inference2 articles

2 articles

AINeutralarXiv – CS AI · Feb 277/105

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Calibrated Test-Time Guidance for Bayesian Inference

Researchers have identified flaws in existing test-time guidance methods for diffusion models that prevent proper Bayesian posterior sampling. They propose new estimators that enable calibrated inference, significantly outperforming previous methods on Bayesian tasks and matching state-of-the-art results in black hole image reconstruction.

AINeutralarXiv – CS AI · Feb 277/105

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On the Equivalence of Random Network Distillation, Deep Ensembles, and Bayesian Inference

Researchers establish theoretical connections between Random Network Distillation (RND), deep ensembles, and Bayesian inference for uncertainty quantification in deep learning models. The study proves that RND's uncertainty signals are equivalent to deep ensemble predictive variance and can mirror Bayesian posterior distributions, providing a unified theoretical framework for efficient uncertainty quantification methods.