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

新方法改进深度神经网络中的不确定性量化

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新论文介绍了两种用于近似深度神经网络中Hessian矩阵的新方法：Gradient-Laplace和Greedy-Laplace。这些方法旨在通过解决全拉普拉斯近似的计算挑战来改进不确定性量化。研究表明，现有的子网络拉普拉斯近似倾向于低估预测方差，而所提出的方法为最优性提供了理论保证，并比现有的启发式方法性能有所提高。 AI

影响 这些方法可能带来更可靠的深度学习模型不确定性量化，提高其在关键应用中的可信度。

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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新方法改进深度神经网络中的不确定性量化

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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]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Swarnali Raha, Kshitij Khare, Rohit K Patra ·

    子网络拉普拉斯近似的最优性：新结果与新方法

    arXiv:2605.09075v2 Announce Type: replace Abstract: 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 o…

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