English(EN) Universal interpolation for deep residual self-attention networks

深度自注意力网络通过深度实现通用插值

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-03 04:00

研究人员在深度残差自注意力网络中展示了通用插值能力，这是学习架构受益于缩放定律的关键特性。该研究侧重于完全通过深度实现近似能力，并在层之间进行显著的参数共享，其灵感来源于 Looped Transformers 等模型。他们的主要发现表明，一组固定的、冻结的两个单头块，并带有高斯初始化的投影矩阵，可以将任何序列集合映射到任何其他序列集合，应用顺序和持续时间会适应特定的插值任务。这对于连续和有限深度的残差 softmax 注意力都成立，并进一步表征了因果掩码的局限性和保证。 AI

影响为自注意力模型中基于深度的学习奠定了理论基础，可能指导未来的架构设计。

排序理由详细介绍深度学习架构新理论结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Sibylle Marcotte, Joan Bruna · 2026-10-03 04:00

深度残差自注意力网络的通用插值

arXiv:2610.01981v1 Announce Type: new Abstract: Universal approximation is a necessary qualitative property of learning architectures to benefit from scaling laws. While it is generically verified on a variety of neural architectures and random feature models, it typically involv…

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深度残差自注意力网络的通用插值

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