English(EN) Optimality of Sub-network Laplace Approximations: New Results and Methods

新方法改进神经网络不确定性的拉普拉斯近似

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-09 17:37

研究人员开发了用于近似深度神经网络中拉普拉斯近似的新方法，解决了大型Hessian矩阵求逆的计算挑战。提出的Gradient-Laplace和Greedy-Laplace方法为选择子网络近似的参数提供了原则性的方法，旨在减少现有启发式方法固有的预测方差低估问题。理论分析和数值研究表明，这些新方法提供了更强的最优性保证，并且优于当前基准。 AI

影响改进了深度学习模型中的不确定性量化，有望带来更可靠的AI系统。

排序理由该集群包含一篇详细介绍机器学习技术新方法和理论分析的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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arXiv stat.ML TIER_1 English(EN) · Rohit K Patra · 2026-05-09 17:37

子网络拉普拉斯近似的最优性：新结果与新方法

Although the Laplace approximation offers a simple route to uncertainty quantification in deep neural networks, its reliance on inverting large Hessian matrices has motivated a range of computationally feasible low-dimensional or sparse approximations. A prominent class of such m…

本文如何被排名

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子网络拉普拉斯近似的最优性：新结果与新方法

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