English(EN) Resolution-Independent Analysis of Encoder--Decoder Operator Learning via Limiting Kernels

新框架通过极限核分析编码器-解码器算子学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

本文介绍了一种分析编码器-解码器架构中算子学习的新颖框架。它在函数空间上构建算子学习，解决了有限维训练数据表示的挑战。研究表明，随着输入和输出分辨率的增加，诱导核收敛到极限核，从而实现分辨率无关的正则性假设。该工作还为正则化随机梯度下降提供了理论界限，并将分析扩展到使用极限神经切线核的编码器-解码器神经网络。 AI

影响为理解和改进复杂神经网络架构中的算子学习提供了理论基础。

排序理由该条目是一篇学术论文，详细介绍了用于编码器-解码器架构中算子学习的新理论框架和分析。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Lei Shi, Jia-Qi Yang, Ding-Xuan Zhou · 2026-09-15 04:00

通过极限核函数对编码器-解码器算子学习进行分辨率无关分析

arXiv:2609.13798v1 Announce Type: cross Abstract: Operator learning is formulated on function spaces, but training data are typically available only through finite-dimensional representations. In encoder--decoder architectures, a matrix-valued kernel on the encoded space induces …

本文如何被排名

报道来源 [1]

通过极限核函数对编码器-解码器算子学习进行分辨率无关分析

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