用海森矩阵分析注意力模型的薄弱区域和参数依赖，提升故障定位能力
Can Hessian-Based Insights Support Fault Diagnosis in Attention-based Models?
- 通过曲率分析识别注意力机制中的脆弱区域
- 利用参数交互分析发现关键故障源，效果优于单纯梯度分析
- 适合研究模型稳定性和调试复杂神经网络的工程师
随着基于注意力的深度学习模型规模与复杂性增加，其故障诊断愈发困难。本文通过实证研究评估海森矩阵分析在注意力模型故障诊断中的潜力。具体地，利用海森矩阵导出的洞察，通过曲率分析识别脆弱区域，通过参数交互分析揭示参数依赖关系。在三种不同模型（HAN、3D-CNN、DistilBERT）上的实验表明，海森矩阵指标能更有效地定位不稳定性并精确定位故障来源，优于仅依赖梯度的方法。结果表明，这些指标有望显著提升复杂神经架构的故障诊断能力，推动软件调试实践改进。
原文摘要 · Abstract (English)
As attention-based deep learning models scale in size and complexity, diagnosing their faults becomes increasingly challenging. In this work, we conduct an empirical study to evaluate the potential of Hessian-based analysis for diagnosing faults in attention-based models. Specifically, we use Hessian-derived insights to identify fragile regions (via curvature analysis) and parameter interdependencies (via parameter interaction analysis) within attention mechanisms. Through experiments on three diverse models (HAN, 3D-CNN, DistilBERT), we show that Hessian-based metrics can localize instability and pinpoint fault sources more effectively than gradients alone. Our empirical findings suggest that these metrics could significantly improve fault diagnosis in complex neural architectures, potentially improving software debugging practices.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。