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标准Transformer在非参数回归中达到最优速率

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的arXiv论文表明，当逼近Hölder函数时，标准Transformer模型可以在非参数回归任务中达到最优速率。该研究为Transformer在大型语言模型和计算机视觉等领域的有效性提供了理论基础。该研究还引入了表征Transformer结构的度量，这可能有助于未来对其泛化和优化误差的研究。 AI

影响 为Transformer模型在AI应用中的能力提供了理论依据。

排序理由 在arXiv上发表的学术论文，详细介绍了Transformer模型的理论特性。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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标准Transformer在非参数回归中达到最优速率

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在arXiv上发表的学术论文，详细介绍了Transformer模型的理论特性。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Yanming Lai, Defeng Sun ·

    标准Transformer在非参数回归中达到具有 $C^{s,\lambda}$ 目标的Minimax速率

    arXiv:2602.20555v2 Announce Type: replace Abstract: The tremendous success of Transformer models in fields such as large language models and computer vision necessitates a rigorous theoretical investigation. To the best of our knowledge, this paper is the first work proving that …

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