English(EN) Learning-Theoretic Foundation for General Coded Computing: The Straggler Setting

新的通用编码计算框架适配深度学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员引入了通用编码计算（GCC）框架，该框架将编码计算原理应用于深度神经网络等机器学习工作负载。与以往专注于结构化计算精确恢复的方法不同，GCC采用端到端的均方误差损失来处理深度学习中常见的近似恢复需求。该框架在理论上保证了在掉队者条件下的性能，在最坏情况下显示出 O(S^3N^-3) 的损失衰减率，在概率设置下显示出 O(log_1/p^3(N)N^-3) 的损失衰减率。 AI

影响为更有效地分布式训练深度神经网络引入了理论框架，可能提高可扩展性。

排序理由关于分布式计算新理论框架的学术论文。[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) · Parsa Moradi, Behrooz Tahmasebi, Mohammad Ali Maddah-Ali · 2026-09-01 04:00

通用编码计算的学习理论基础：掉队者场景

arXiv:2608.28910v1 Announce Type: new Abstract: Coded computing has emerged as a powerful paradigm for mitigating the impact of straggling workers in distributed computing systems. However, existing coded-computing schemes are predominantly designed for the exact recovery of high…

本文如何被排名

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通用编码计算的学习理论基础：掉队者场景

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