English(EN) In-context Learning of Single-index Targets: Comparing Kernel and Feature Learners

新研究比较了非线性上下文学习中的核学习器和特征学习器

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员通过比较两个单层注意力架构：核学习器和特征学习器，分析了非线性上下文学习（ICL）。利用副本方法，他们推导出了记忆和泛化误差的预测，并考虑了预训练大小、任务多样性和上下文长度。该分析产生了相图，指示了基于这些因素的每种架构何时更有优势，并确定了两种学习器不同的上下文长度缩放。 AI

影响为架构选择如何影响非线性上下文学习提供了理论见解，可能指导未来的模型开发。

排序理由该集群包含一篇在arXiv上发表的研究论文，详细介绍了对机器学习技术的新分析。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该集群包含一篇在arXiv上发表的研究论文，详细介绍了对机器学习技术的新分析。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv stat.ML TIER_1 English(EN) · Haotian Gu, Yizhou Xu, Lenka Zdeborov\'a · 2026-10-02 04:00

单索引目标的上下文学习：核方法与特征学习器的比较

arXiv:2610.01712v1 Announce Type: cross Abstract: In-context learning (ICL) enables a pretrained model to infer a task from demonstrations without updating its parameters. While much of the existing theory focuses on linear target functions, in this paper we study nonlinear cases…