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English(EN) Is Variational Monte Carlo Robust? Sharp Moment Thresholds and Heavy-tailed Stochastic Optimization

新的VMC变体PS-Clip-VMC增强了重尾优化的鲁棒性

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员发现，变分蒙特卡洛（VMC）在电子结构理论和像FermiNet这样的神经网络应用中是一个关键算法，但由于重尾估计器而面临鲁棒性问题。这些估计器受波函数节点几何形状的影响，通常无法获得更高阶矩，从而影响了随机优化的可靠性。为了解决这个问题，开发了一种名为PS-Clip-VMC的新方法，该方法对局部能量和梯度变量进行裁剪。该方法已被证明在较弱的矩条件下能够可靠收敛，并在初步实验中显示出在原子上训练FermiNet的鲁棒性有所提高。 AI

影响 为科学计算中的神经网络应用引入了一种更鲁棒的优化技术。

排序理由 该集群包含一篇详细介绍特定优化算法新方法的学术论文。

在 arXiv cs.LG 阅读 →

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新的VMC变体PS-Clip-VMC增强了重尾优化的鲁棒性

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Philipp Grohs, Davide Nobile ·

    变分蒙特卡洛是否鲁棒？尖锐的矩阈值和重尾随机优化

    arXiv:2606.26009v1 Announce Type: new Abstract: Variational Monte Carlo (VMC) is a central algorithm in electronic structure theory and has gained renewed importance through modern neural-network ans\"atze such as FermiNet. At its core, VMC seeks ground states by minimizing the R…

  2. arXiv cs.LG TIER_1 English(EN) · Davide Nobile ·

    变分蒙特卡洛是否稳健？尖锐的矩阈值和重尾随机优化

    Variational Monte Carlo (VMC) is a central algorithm in electronic structure theory and has gained renewed importance through modern neural-network ansätze such as FermiNet. At its core, VMC seeks ground states by minimizing the Rayleigh quotient by stochastic optimization. In th…

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