English(EN) RINO: Renormalization Group Distillation with No Labels

新的RINO方法利用重整化群进行物理学中的自监督学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员推出了一种新颖的高能物理自监督学习方法RINO，该方法利用了喷流形成的尺度间结构。与先前的方法不同，RINO利用重整化群原理和DINO风格的自蒸馏，创建了用于在无标签数据上训练的物理基础视图。这种方法显著提高了模型向真实世界对撞机数据的可迁移性，其性能优于现有的自监督基线，甚至在仅使用少量标签数据的情况下也能媲美完全监督的训练。 AI

影响该方法可以实现对真实粒子碰撞数据更准确、更高效的分析，从而推动高能物理学研究。

排序理由该集群描述了一篇详细介绍一种新颖的特定科学领域机器学习方法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Zichun Hao, Raghav Kansal, Abhijith Gandrakota, Chang Sun, Haoyang Li, Zihan Zhao, Ngadiuba Jennifer, Javier Duarte, Maria Spiropulu · 2026-10-15 04:00

RINO：无标签重整化群蒸馏

arXiv:2610.15919v1 Announce Type: cross Abstract: Supervised machine learning in high-energy physics (HEP) requires training on labeled simulations of particle collisions, which are known to be unfaithful to the underlying physics. This leads to significant inaccuracies and biase…

本文如何被排名

报道来源 [1]

RINO：无标签重整化群蒸馏

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