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mL-BFGS:用于分布式大规模神经网络优化的基于动量的L-BFGS

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概括
此摘要是机器生成的。

我们介绍了mL-BFGS, 一种基于动量的算法, 这种方法稳定了融合,并加速了大规模分布式模型的训练.

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科学领域:

  • 机器学习
  • 优化算法
  • 深度神经网络

背景情况:

  • 准牛顿方法,包括L-BFGS,由于计算成本和随机设置的不稳定性,在大规模深度神经网络训练中面临挑战.
  • 现有的L-BFGS适用于随机训练通常会带来显著的开销,从而抵消了趋同的好处.

研究的目的:

  • 提出mL-BFGS,一个轻量级的,基于动量的L-BFGS算法,旨在高效大规模分布式深度神经网络优化.
  • 在深度学习中提高准牛顿方法的稳定性和减少计算负担.

主要方法:

  • 开发了mL-BFGS,将动量方案纳入L-BFGS更新中,以减轻赫森近似中的随机噪声.
  • 在mL-BFGS中实现区块智能赫斯近似,以在大型训练中跨节点分配计算和内存成本.
  • 在随机优化场景中提供了mL-BFGS的理论收分析.

主要成果:

  • 在随机优化过程中,mL-BFGS通过降低赫斯近似的噪声来证明稳定收.
  • 区块智能赫斯近似使得分布式训练能够有效地扩展计算和内存.
  • 与SGD和Adam等基线方法相比,对基准神经模型的实证结果显示了显著的代和壁表加快.

结论:

  • 在大型深度神经网络训练中,mL-BFGS提供了一种有前途的方法.
  • 拟议的算法有效地平衡了计算效率与收稳定性,优于现有方法.