English(EN) On the Representational Geometry of Dynamic Programs

新研究探讨动态规划的几何特性以实现神经网络泛化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-27 04:00

研究人员探讨了动态规划（DP）的几何特性，以理解标准神经网络在DP任务中泛化到更长输入时为何会遇到困难。他们确定有限的最小加法DP问题等价于有向无环图上的最短路径问题，这些问题也可以表示为热带多项式。该研究引入了关于这些DP结构降维和组合的两个结构性负面结论，表明当前长度泛化方法的局限性。 AI

排序理由该条目是提交给arXiv cs.LG的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Richard F. M. Lim, Ruriko Yoshida · 2026-08-27 04:00

动态规划的表征几何

arXiv:2608.25034v1 Announce Type: new Abstract: Standard neural architectures often fail to generalize to longer inputs for dynamic programming (DP) targets. We investigate what makes this hard geometrically. Every finite min-plus DP is a shortest path on a DAG, which is equivale…