Two new arXiv papers highlight significant instability in evaluating Bayesian deep learning methods, particularly under data scarcity. Researchers found that standard evaluation metrics can produce unreliable and dataset-dependent rankings, meaning a method's superiority can vary greatly depending on the specific dataset and sample size. The studies suggest that current evaluation practices may mislead practitioners, and propose uncertainty-aware methods and reporting of variance trajectories to provide more robust assessments of model performance. AI
影响 Highlights potential unreliability in current Bayesian deep learning evaluation methods, urging practitioners to adopt uncertainty-aware assessments.
排序理由 Two academic papers published on arXiv discussing methodological issues in evaluating Bayesian deep learning models.
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