Dansk(DA) Differentiable Filtering for Learning Hidden Markov Models

可微滤波框架可高效学习隐马尔可夫模型参数

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-27 04:00

研究人员开发了一个名为 Belief Net 的新框架，用于学习隐马尔可夫模型 (HMM)。该方法使用可微滤波过程，将前向滤波器视为通过随机梯度下降进行优化的结构化神经网络。Belief Net 在传统方法（如 Baum-Welch）方面提供了改进的收敛性，并在谱算法失败的情况下恢复参数，同时通过直接学习 HMM 参数来保持可解释性。 AI

影响引入了一种新颖、可解释的神经网络方法来学习序列数据模型，有可能在性能和收敛性方面优于现有方法。

排序理由这是一篇介绍学习隐马尔可夫模型新框架的研究论文。

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 Dansk(DA) · Reginald Zhiyan Chen, Heng-Sheng Chang, Prashant G. Mehta · 2026-04-27 04:00

Differentiable Filtering for Learning Hidden Markov Models

arXiv:2511.10571v2 Announce Type: replace Abstract: Hidden Markov Models (HMMs) are fundamental for modeling sequential data, yet learning their parameters from observations remains challenging. Classical methods like the Baum-Welch algorithm are computationally intensive and pro…

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Differentiable Filtering for Learning Hidden Markov Models

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