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New PST estimator enables gradient-based machine learning for discrete stochastic systems

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为 Propensity Straight-Through (PST) 估计器的新方法，用于通过基于梯度的机器学习来训练离散随机系统。该技术通过吉尔斯皮类型模拟实现精确梯度计算，解决了当前方法的局限性。PST 在准确性方面与 Gumbel Softmax 等现有方法具有竞争力，同时收敛速度更快，并提供了一种无温度和无 Gumbel 的方法，可实现可扩展学习。 AI

影响 能够更有效地训练用于机器学习应用的复杂随机模型。

排序理由 介绍新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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New PST estimator enables gradient-based machine learning for discrete stochastic systems

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介绍新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jose M. G. Vilar, Leonor Saiz ·

    离散随机系统的倾向性直通梯度

    arXiv:2608.25631v1 Announce Type: cross Abstract: Continuous-time Markov chains (CTMCs) provide the backbone for modeling discrete stochastic dynamics across applied, physical, and biological sciences. Their integration with modern gradient-based machine learning, however, is lim…

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