English(EN) Beyond Structural Symmetries: Linear Mode Connectivity via Neuron Identifiability

新理论通过神经元可识别性探索线性模式连通性

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-03 11:37

研究人员开发了一个新的理论框架来理解深度学习中的线性模式连通性，重点关注神经元可识别性。这种方法表明，即使没有明确的结构对称性，神经网络也可以拥有多个等效解。研究结果表明，神经元可识别性促进了表示的合并，从而为组合这些表示提供了线性的低损耗路径。 AI

影响为深度学习的损失景观和解空间提供了理论见解。

排序理由该集群包含一篇在 arXiv 上发表的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Vincent B\"urgin, Daniel Herbst, Ya-Wei Eileen Lin, Stefanie Jegelka · 2026-06-04 04:00

超越结构对称性：通过神经元可识别性实现线性模式连通性

arXiv:2606.04754v1 Announce Type: new Abstract: Many striking phenomena in deep learning, such as linear mode connectivity and the structured behavior of training dynamics, are closely tied to parameter symmetries: transformations that leave the realized function unchanged. Despi…
arXiv cs.LG TIER_1 English(EN) · Stefanie Jegelka · 2026-06-03 11:37

超越结构对称性：通过神经元可识别性实现线性模式连通性

Many striking phenomena in deep learning, such as linear mode connectivity and the structured behavior of training dynamics, are closely tied to parameter symmetries: transformations that leave the realized function unchanged. Despite growing attention to parameter symmetries, th…