English(EN) SETAREH Transform: A Sorting-Assisted Haar-Wavelet Sparsification Method for Approximate Inner-Product Computation

SETAREH Transform 提供用于高效内积计算的Haar小波方法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员推出SETAREH Transform，这是一种利用Haar小波近似高维向量运算中内积的新方法，这种运算在深度学习中很常见。该技术旨在降低与存储、传输和处理大型向量相关的成本，尤其是在分布式和边缘计算场景中。通过应用一系列置换和Haar变换，SETAREH在截断前保留了精确的内积，仅保留一部分变换后的系数-输入对，以最小化表示成本，同时允许可控的近似误差。在合成数据和一项真实年龄估计任务上的实验表明，与原始向量维度相比，SETAREH能够显著减少所需对的数量。 AI

影响该方法可以通过降低高维向量运算的计算成本来提高深度学习系统的效率。

排序理由该集群包含一篇详细介绍深度学习新计算方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Seyed Kamaledin Setarehdan · 2026-10-19 04:00

SETAREH 变换：用于近似内积计算的排序辅助 Haar 小波稀疏化方法

arXiv:2610.23338v1 Announce Type: new Abstract: Modern deep learning systems increasingly rely on high-dimensional vector operations, in which large input and parameter vectors are repeatedly stored, transferred, and processed. Although substantial research has focused on reducin…

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SETAREH 变换：用于近似内积计算的排序辅助 Haar 小波稀疏化方法

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