English(EN) Tensor network representations of discrete maximum entropy distributions via mean polytopes

新的张量网络架构表示离散最大熵分布

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一种新的张量网络架构，称为计算激活网络（CompActNets），用于在期望约束下表示离散最大熵分布。该框架利用凸多面体的几何形状来表示分布及其支撑，并将张量网络秩视为面的复杂度度量。对布尔统计的案例研究表明，0/1多面体的几何形状与命题公式之间存在直接联系。 AI

影响引入了一种表示复杂分布的新颖数学框架，可能影响人工智能模型的开发和理论理解。

排序理由该集群包含一篇详细介绍新理论框架和架构的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Alex Goessmann, Martin Eigel · 2026-09-10 04:00

通过均值多面体实现离散最大熵分布的张量网络表示

arXiv:2609.07184v1 Announce Type: cross Abstract: We present tensor network representations for discrete maximum entropy distributions under expectation constraints. To this end, we introduce Computation-Activation Networks (CompActNets), a tensor network architecture that subsum…