English(EN) Efficient Online Continual Foundation Model Fine-Tuning for Predictive Process Monitoring

新框架支持预测性流程监控的基础模型持续微调

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-31 04:00

研究人员推出 COMPASS，一个新颖的框架，专为预测性流程监控 (PPM) 的基础模型 (FM) 在线持续微调而设计。该方法解决了现有从头开始训练特定任务网络的方法中固有的冷启动问题。COMPASS 采用损失平台漂移检测来识别事件流中的任务边界，并维护统一的知识子空间，在各种漂移场景下均优于最先进的非 FM 竞争对手。 AI

影响通过允许基础模型持续学习并适应不断变化的数据分布，这项研究可以为动态环境中更强大、更具适应性的 AI 系统提供支持。

排序理由该集群包含一篇详细介绍基础模型微调新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Sjoerd van Straten, Marwan Hassani · 2026-08-31 04:00

面向预测性流程监控的高效在线持续基础模型微调

arXiv:2608.28237v1 Announce Type: new Abstract: Predictive Process Monitoring (PPM) models are increasingly deployed in dynamic environments where concept drift causes the underlying process distribution to shift over time. While recent work has moved toward online continual lear…

本文如何被排名

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面向预测性流程监控的高效在线持续基础模型微调

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