English(EN) Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance

t分布在贝叶斯神经网络中优于高斯分布

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-29 04:00

研究人员探讨了不同似然分布对贝叶斯神经网络（BNNs）性能的影响。虽然高斯分布因计算方便常用于模拟BNNs中的不确定性，但本研究调查了替代分布是否能产生更好的结果。研究结果表明，无论数据或网络架构如何，使用t分布作为似然函数都能持续提高预测性能，优于高斯假设。此外，t分布在保持易于实现的同时，还有可能缩短训练时间。 AI

影响这项研究通过提出一种更优的不确定性建模分布，可能带来更准确、更高效的贝叶斯神经网络。

排序理由详细介绍一种改进模型性能的新颖方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Pei-Hsuan Hsia, Lars H. Heyen, Arvid Weyrauch, Markus Goetz, Achim Streit, Sebastian Krumscheid, Charlotte Debus · 2026-07-29 04:00

重新思考似然分布：学生t似然提升贝叶斯神经网络性能

arXiv:2607.25376v1 Announce Type: cross Abstract: In Bayesian neural networks (BNNs), variational inference is a widely adopted framework for modeling uncertainty in a distributional way, with the evidence lower bound (ELBO) serving as the standard objective function. Several dis…

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重新思考似然分布：学生t似然提升贝叶斯神经网络性能

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