English(EN) Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers

新研究界定 Transformer 注意力分布，以提高训练稳定性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-10 04:00

研究人员开发了一种新方法来分析 Transformer 自注意力块的局部 Lipschitz 常数，揭示了其对注意力图分布的依赖性。这项工作引入了 JaSMin，一种旨在控制该常数并增强 Transformer 训练稳定性的正则化器。研究结果还阐明了注意力图分布如何影响梯度动态。 AI

影响提供了可能导致更稳定、更高效的 Transformer 训练的理论见解。

排序理由该集群包含一篇详细介绍 Transformer 架构理论进展的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Nikolay Yudin, Sergei Kudriashov, Alexander Gaponov, Maxim Rakhuba · 2026-08-10 04:00

关注注意力分布：Transformer 的新局部 Lipschitz 界限

arXiv:2507.07814v2 Announce Type: replace Abstract: We introduce a novel upper bound on the local Lipschitz constant of the dot-product self-attention block showing its dependence on the attention map distributions. The proposed bound is not only tighter than the prior art, but f…