English(EN) A Reverse-Gold Theorem: Positive-Data Identification by Substrate-Bounded Contrastive Learners

新的“反黄金定理”可从正样本中识别语言

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-22 04:00

研究人员开发了一个新的定理，称为“反黄金定理”，该定理证明了某些语言仅使用正样本即可识别。该方法利用递归对比算子和通过对比估计训练的、具有容量约束的评分器。该方法实现了PAC条件识别，这意味着它可以在样本复杂度多项式地依赖于关键参数的情况下，将大多数正样本的排名置于负扰动之上。该定理的假设已在可替换的上下文无关语言和子正则类中得到实例化，并对该定理在轻微上下文敏感语言层级中的位置提出了一个开放性猜想。 AI

影响引入了一种新颖的语言识别理论框架，可能影响未来的模型训练技术。

排序理由学术论文，详细介绍机器学习中的一项新理论成果。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 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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arXiv cs.CL TIER_1 English(EN) · Rakesh Sengupta · 2026-10-22 04:00

A Reverse-Gold Theorem: Positive-Data Identification by Substrate-Bounded Contrastive Learners

arXiv:2610.28747v1 Announce Type: cross Abstract: Gold's theorem shows that no learner can identify an arbitrary language in a superfinite class from positive presentations alone. We prove a positive result of a different shape. Fix an innate \emph{recursive contrast operator} $R…

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A Reverse-Gold Theorem: Positive-Data Identification by Substrate-Bounded Contrastive Learners

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