English(EN) A Non-Monotone Preconditioned Trust-Region Method for Neural Network Training

新的NAPTS方法将神经网络训练时间缩短了30%

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-14 14:06

研究人员开发了一种名为非单调预条件信任域策略（NAPTS）的新优化方法，专门用于训练深度神经网络。该方法通过使用域分解和全局信任域机制来增强并行训练。据报道，与APTS等先前方法相比，NAPTS将训练时间缩短了30%，并显著减少了被拒绝的步骤。 AI

影响这项新的优化技术可能导致更大神经网络的更快、更有效的训练，从而加速AI发展。

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arXiv cs.LG TIER_1 English(EN) · Rolf Krause · 2026-05-14 14:06

一种非单调预条件信任域方法用于神经网络训练

Training deep neural networks at scale can benefit from domain decomposition, where the network is split into subdomains trained in parallel and coupled by a global trust-region mechanism. Building on the Additively Preconditioned Trust-Region Strategy (APTS), we propose a non-mo…