English(EN) The Geometry of Polynomial Group Convolutional Neural Networks

发布了多项式群卷积神经网络新框架

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 04:00

研究人员使用分级群代数引入了一个新的多项式群卷积神经网络（PGCNNs）的数学框架。该框架提供了该架构的两种参数化，通过一个线性映射连接，并基于Hadamard积和Kronecker积。该研究计算了相关神经流形的维度，发现其仅取决于层数和群的大小，并提出了这些参数化的一般纤维的猜想。 AI

影响引入了新颖的神经网络架构数学结构，可能推动理论理解。

排序理由该集群包含一篇研究论文，详细介绍了某类神经网络的新数学框架。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

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arXiv cs.LG TIER_1 English(EN) · Yacoub Hendi, Daniel Persson, Magdalena Larfors · 2026-09-07 04:00

多项式群卷积神经网络的几何学

arXiv:2603.29566v2 Announce Type: replace Abstract: We study polynomial group convolutional neural networks (PGCNNs) for an arbitrary finite group $G$. In particular, we introduce a new mathematical framework for PGCNNs using the language of graded group algebras. This framework …