统一流程优化神经网络，让训练高效且可扩展。
Training Neural Networks at Any Scale
- 用统一模板设计高效优化算法
- 算法自动适应问题结构特性
- 适合关注大规模训练的从业者
本文综述了现代神经网络训练中的优化方法，重点强调效率与可扩展性。我们以统一的算法框架呈现当前最先进的优化技术，突出其对问题结构自适应的重要性。随后探讨如何使这些算法不受问题规模影响，具备良好的可扩展性。本文旨在为实践者和研究人员提供入门指引，助力参与这一前沿领域的发展。
原文摘要 · Abstract (English)
This article reviews modern optimization methods for training neural networks with an emphasis on efficiency and scale. We present state-of-the-art optimization algorithms under a unified algorithmic template that highlights the importance of adapting to the structures in the problem. We then cover how to make these algorithms agnostic to the scale of the problem. Our exposition is intended as an introduction for both practitioners and researchers who wish to be involved in these exciting new developments.
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