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English(EN) Implicit Variational Rejection Sampling

新的IVRS方法增强了贝叶斯机器学习后验近似

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员引入了隐式变分拒绝采样（IVRS），这是一种旨在增强贝叶斯机器学习中后验近似的新颖方法。该技术将神经网络建模的隐式分布与拒绝采样相结合，利用判别器网络通过估计密度比来改进近似。IVRS旨在克服传统均场变分推断的局限性以及单独使用神经网络约束可能产生的误差。所提出的方法还引入了隐式重采样证据下界（IR-ELBO）用于质量评估，并推导出了更紧的变分下界，实验结果表明其性能优于现有的变分推断方法。 AI

影响 这种新方法可能导致贝叶斯机器学习中更准确的后验近似，从而可能提高依赖概率建模的各种AI应用的性能。

排序理由 该集群包含一篇详细介绍贝叶斯机器学习新方法的论文。

在 arXiv cs.LG 阅读 →

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新的IVRS方法增强了贝叶斯机器学习后验近似

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Jian Xu, Shigui Li, Wei Chen, Jiacheng Li, Zhiqi Lin, Delu Zeng, Xinghao Ding, John Paisley, Qibin Zhao ·

    隐式变分拒绝采样

    arXiv:2606.14235v1 Announce Type: new Abstract: Variational Inference (VI) is a fundamental inference technique in Bayesian machine learning for approximating complex posterior distributions. Traditional VI often relies on the mean-field factorization, which can inadequately capt…

  2. arXiv cs.LG TIER_1 English(EN) · Qibin Zhao ·

    隐式变分拒绝采样

    Variational Inference (VI) is a fundamental inference technique in Bayesian machine learning for approximating complex posterior distributions. Traditional VI often relies on the mean-field factorization, which can inadequately capture true posterior complexity. Recent advancemen…

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