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English(EN) On Generalisation Error Bounds for Transformers

两篇论文探讨 Transformer 泛化和位置编码

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇最新的 arXiv 论文深入探讨了 Transformer 模型的能力。第一篇论文研究了 RoPE 和 ALiBi 等不同的位置编码方案如何影响 Transformer 处理不同 token 间距离的能力，这一概念被称为距离泛化。第二篇论文则侧重于为单层 Transformer 建立泛化误差的理论界限，并提出了一些改进措施，这些措施独立于输入序列长度，并且在样本量增加时能提供更好的衰减率。 AI

影响 这些论文有助于更深入地理解 Transformer 模型在处理不同数据分布时的局限性和潜在改进。

排序理由 两篇发表在 arXiv 上的学术论文，讨论 Transformer 模型泛化。

在 arXiv cs.LG 阅读 →

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两篇论文探讨 Transformer 泛化和位置编码

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两篇发表在 arXiv 上的学术论文，讨论 Transformer 模型泛化。
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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Daniel Henrik Nevermann, Claudius Gros ·

    Transformer中的距离泛化：为何要关注位置编码？

    arXiv:2609.11913v1 Announce Type: new Abstract: Out-of-distribution length generalization, namely to extrapolate a task from short to longer context, has been studied intensively for transformers. Here we focus on distance generalization, which probes performance when inter-token…

  2. arXiv cs.LG TIER_1 English(EN) · Lan V. Truong ·

    关于Transformer的泛化误差界限

    arXiv:2410.11500v2 Announce Type: replace-cross Abstract: In this paper, we establish a collection of covering number bounds for linear function classes under various norm constraints on the inputs and matrices. We then combine these results with existing covering number bounds t…

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