New Bounds for Transformer Training via Optimal Control and Robust Optimization

作者 PulseAugur 编辑部 · [2 个来源] · 2026-07-30 10:22

研究人员为 Transformer 训练开发了新的有限样本泛化界限，将该过程构建为一个马尔可夫控制问题。通过分析量化模型并使用集中不等式，他们为度量空间上的经验定律推导出了显式界限。这项工作还将 Transformer 泛化与 Wasserstein 分布鲁棒优化联系起来。 AI

影响在 Transformer 训练方面引入了理论进步，有可能提高泛化能力。

排序理由该集群包含一篇学术论文，详细介绍了 Transformer 模型训练的新颖理论贡献。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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Newsworthiness bucket: Research; 该集群包含一篇学术论文，详细介绍了 Transformer 模型训练的新颖理论贡献。
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arXiv cs.LG TIER_1 English(EN) · Ka\u{g}an Akman, Naci Saldi, Serdar Y\"uksel · 2026-07-31 04:00

Transformer 训练和 Wasserstein 分布鲁棒性的最优控制的泛化界限

arXiv:2607.27975v1 Announce Type: new Abstract: We derive finite-sample generalization bounds for Transformers trained with dynamic programming recursions. Building on the doubly lifted, measure-valued formulation of Transformer dynamics, we view data sets as probability laws on …
Hugging Face Daily Papers TIER_1 English(EN) · 2026-07-30 10:22

Transformer 训练和 Wasserstein 分布鲁棒性的最优控制泛化界

We derive finite-sample generalization bounds for Transformers trained with dynamic programming recursions. Building on the doubly lifted, measure-valued formulation of Transformer dynamics, we view data sets as probability laws on pairs of empirical input-output measures, allowi…