English(EN) EM-NeSy: Expectation Maximization for Neurosymbolic Learning

新的EM-NeSy方法增强了神经符号AI学习

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-12 13:54

研究人员推出了一种新颖的神经符号学习方法EM-NeSy，该方法将该过程构建为期望最大化（EM）算法的一个实例。这种方法允许近似推理，而无需符号组件可微，这是当前最先进模型中的一个常见限制。EM-NeSy使用基于计算出的后验的梯度下降来更新神经网络参数，并在实验中展示了可扩展性和计算效率。 AI

影响这项研究为训练神经符号AI模型提供了一种更灵活、更有效的方法，有望提高鲁棒性和可解释性。

排序理由该集群包含一篇详细介绍神经符号学习新方法的学术论文。

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arXiv cs.LG TIER_1 English(EN) · Annegret Seibt, Luc De Raedt, Giuseppe Marra · 2026-06-15 04:00

EM-NeSy：神经符号学习的期望最大化

arXiv:2606.14463v1 Announce Type: new Abstract: Neurosymbolic (NeSy) models integrate neural networks and symbolic reasoning for robust and interpretable AI. State-of-the-art NeSy models require that the symbolic component is expressed in a differentiable way, often complicating …
arXiv cs.LG TIER_1 English(EN) · Giuseppe Marra · 2026-06-12 13:54

EM-NeSy：神经符号学习的期望最大化

Neurosymbolic (NeSy) models integrate neural networks and symbolic reasoning for robust and interpretable AI. State-of-the-art NeSy models require that the symbolic component is expressed in a differentiable way, often complicating the use of approximate inference. We propose EM-…