English(EN) Where to Approximate in Neurosymbolic Inference?

新的神经符号推理管道提高了加权模型计数精度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员开发了新的神经符号推理管道，通过战略性地分配近似预算来提高加权模型计数（WMC）的准确性。这些管道受张量网络启发，利用张量链分解和奇异值分解（SVD），在各种推理任务中提供了比以往方法显著更准确的WMC估计。这种增强的准确性对于需要符号因子之间紧密交互的任务特别有利，从而带来更强的下游学习。 AI

影响这些管道可以通过提高结合神经和符号方法的准确性，从而实现更强大的AI系统推理能力。

排序理由该集群包含一篇详细介绍神经符号推理新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Samy Badreddine, Emile van Krieken, Luciano Serafini, Antonio Vergari · 2026-11-11 05:00

神经符号推理中的近似在哪里？

arXiv:2611.13862v1 Announce Type: new Abstract: Probabilistic neurosymbolic methods rely on weighted model counting (WMC) to combine neural predictors with symbolic constraints. Exact WMC is #P-hard and typically scales poorly, so many methods resort to approximations. By unifyin…

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报道来源 [1]

神经符号推理中的近似在哪里？

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