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
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IMPACT Highlights potential unreliability in current Bayesian deep learning evaluation methods, urging practitioners to adopt uncertainty-aware assessments.
RANK_REASON Two academic papers published on arXiv discussing methodological issues in evaluating Bayesian deep learning models.