English(EN) Tensor Data Scattering and the Impossibility of Slicing Theorem

论文提出新的稀疏张量表示和散射定理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-03 04:00

本文介绍了一种新颖的稀疏张量表示方法，为深度学习中使用的张量数据散射技术建立了理论框架。它提出了一个解释张量数据散射中切片不可能性定理，这对于性能分析和加速器优化至关重要。该研究还提供了一个衡量稀疏效率的公式和一个Python实现。 AI

影响为深度学习中高效稀疏张量处理引入了理论框架和实现。

排序理由该条目是一篇提交到arXiv的学术论文，详细介绍了理论计算机科学研究。[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) · Wuming Pan · 2026-08-03 04:00

张量数据散射与切片定理的不可能性

arXiv:2012.01982v3 Announce Type: replace Abstract: This paper proposes a standard way to represent sparse tensors. A broad theoretical framework for tensor data scattering methods used in various deep learning frameworks is established. This paper presents a theorem that is very…