English(EN) Provable Accuracy Collapse in Embedding-Based Representations under Dimensionality Mismatch

新研究证明嵌入维度不匹配导致准确性崩溃

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-06 04:00

研究人员证明了基于嵌入的机器学习表示中一个基本的信息论限制。他们的发现表明，如果嵌入维度选择不接近真实数据维度，准确性可能会突然崩溃。即使在标准的对比学习设置中，这种现象也会发生，其中监督仅限于距离比较，导致性能显著下降。 AI

影响强调了嵌入维度的理论限制，表明仔细选择对于模型性能至关重要。

排序理由这是一篇发表在arXiv上的研究论文，详细介绍了机器学习的理论发现。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Dionysis Arvanitakis, Vaggos Chatziafratis, Yiyuan Luo · 2026-05-06 04:00

Embedding表示在维度不匹配下可证的准确性崩溃

arXiv:2605.03346v1 Announce Type: cross Abstract: Embedding-based representations in Euclidean space $\mathbb{R}^d$ are a cornerstone of modern machine learning, where a major goal is to use the \emph{smallest dimension} that faithfully captures data relations. In this work, we p…