用少量故障数据建模罕见事件，提升系统安全可靠性。
Rare event modeling with self-regularized normalizing flows: what can we learn from a single failure?
- 自正则化归一化流框架，从极少故障数据中学习后验分布。
- 在数据稀缺场景下性能超越现有方法，成功分析2022年西南航空危机。
- 适合安全关键系统故障分析、小样本生成建模的研究者使用。
自动驾驶系统在交通与机器人领域的广泛应用带来了越来越多的安全关键型故障。由于故障数据极度稀少——正常操作数据可达数万条，而故障前的可用数据可能仅有几秒——导致故障建模和调试困难。现有生成模型在训练时易因数据不足而过拟合噪声或因先验过强而欠拟合。为此，本文提出CalNF（校准归一化流），一种基于自正则化的后验学习框架，可有效应对极小样本下的故障建模挑战。CalNF在数据受限的故障建模与逆问题任务中达到当前最优性能，并首次实现对2022年西南航空调度危机根本原因的案例研究。
原文摘要 · Abstract (English)
Increased deployment of autonomous systems in fields like transportation and robotics have seen a corresponding increase in safety-critical failures. These failures can be difficult to model and debug due to the relative lack of data: compared to tens of thousands of examples from normal operations, we may have only seconds of data leading up to the failure. This scarcity makes it challenging to train generative models of rare failure events, as existing methods risk either overfitting to noise in the limited failure dataset or underfitting due to an overly strong prior. We address this challenge with CalNF, or calibrated normalizing flows, a self-regularized framework for posterior learning from limited data. CalNF achieves state-of-the-art performance on data-limited failure modeling and inverse problems and enables a first-of-a-kind case study into the root causes of the 2022 Southwest Airlines scheduling crisis.
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