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Feb, 2024
基于偏差梯度估计的分布式动量方法
Distributed Momentum Methods Under Biased Gradient Estimations
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Ali Beikmohammadi, Sarit Khirirat, Sindri Magnússon
TL;DR
通过对分布式动量法中的有偏梯度估计建立非渐近收敛界限,并且在元学习和压缩梯度等特殊情况下证明动量法在训练深度神经网络中比传统有偏梯度下降方法有更快的收敛性能。
Abstract
distributed stochastic gradient methods
are gaining prominence in solving large-scale machine learning problems that involve data distributed across multiple nodes. However, obtaining
unbiased stochastic gradients
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