English(EN) Distributional Sensitivity Analysis: Enabling Differentiability in Sample-Based Inference

新框架实现基于样本推理的可微分性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-25 04:00

研究人员开发了一个名为分布敏感性分析的新数学框架，以实现基于样本推理的可微分性。该框架提供了用于估计随机样本相对于分布参数的敏感性的解析公式，这对于核物理等领域的逆问题至关重要。该方法可应用于黑盒或基于仿真的采样器，无需模型拟合或了解采样算法，并有助于集成到深度学习和自动微分框架中。 AI

影响能够将任意采样子程序集成到深度学习和自动微分框架中。

排序理由该集群包含一篇详细介绍新数学框架和算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Pi-Yueh Chuang, Ahmed Attia, Emil Constantinescu · 2026-08-25 04:00

分布敏感性分析：实现基于样本推理的可微分性

arXiv:2508.09347v2 Announce Type: replace Abstract: This work introduces a mathematical framework for estimating the space-parameter sensitivity of random samples in arbitrary dimensions. Such sensitivity effectively acts as gradients of random samples with respect to distributio…