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English(EN) Linearized subspace refinement framework to expose hidden accuracy in trained neural networks

新框架揭示已训练神经网络的隐藏精度

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为线性子空间细化（LSR）的新型训练后框架，旨在提高已训练神经网络的精度，特别是在科学机器学习任务中。LSR通过分析已训练网络的局部线性化模型并在低维子空间内计算优化校正来工作。该方法通过解决标准基于梯度的训练中常见的精度平台问题，在函数逼近和算子学习等各种应用中显著降低了误差。 AI

影响 该框架通过揭示先前无法达到的性能水平，有望在科学应用中实现更精确、更可靠的神经网络。

排序理由 关于神经网络细化新颖框架的已发表研究论文。[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 →

新框架揭示已训练神经网络的隐藏精度

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关于神经网络细化新颖框架的已发表研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Wenbo Cao, Weiwei Zhang ·

    线性子空间细化框架揭示训练神经网络中隐藏的准确性

    arXiv:2601.13989v2 Announce Type: replace Abstract: Neural networks trained by gradient-based methods often exhibit optimization-induced accuracy plateaus in scientific machine learning tasks. We present Linearized Subspace Refinement (LSR), an architecture-agnostic post-training…

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