研究人员开发了一个名为算子链（CHOP）的新框架，以提高上下文算子网络（ICON）的泛化能力。CHOP通过构建一个由基本变换和ICON本身组成的链来解决分布外算子任务，并利用一个固定的ICON模型。实验表明，CHOP可以减少推理误差并保持可解释性，甚至在不同偏微分方程族之间表现出泛化能力。 AI
影响 增强了算子学习模型的泛化能力，可能改进其在科学建模中的应用。
排序理由 该集群包含一篇详细介绍改进机器学习模型新框架的研究论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.12318v1 Announce Type: cross Abstract: Neural operators approximate mappings between function spaces, but often generalize poorly to other operators and usually require fine-tuning or retraining. In-Context Operator Networks (ICON) addresses this issue by prompting the…
Neural operators approximate mappings between function spaces, but often generalize poorly to other operators and usually require fine-tuning or retraining. In-Context Operator Networks (ICON) addresses this issue by prompting the model with numerical context so that the model le…