Two new research papers propose advancements in Bayesian deep learning, focusing on improving inference methods for neural networks. The first paper argues that sampling-based inference (SAI) has reached computational parity with optimization methods and should become the standard for uncertainty quantification. The second paper introduces a novel, scalable score-based variational inference method that avoids reparameterized sampling and can handle large-scale networks like Vision Transformers, addressing issues like mode collapsing found in other methods. AI
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IMPACT These papers advance core research in Bayesian deep learning, potentially improving uncertainty quantification and enabling more scalable inference for complex models.
RANK_REASON Two academic papers published on arXiv proposing new methods for Bayesian deep learning.