用预训练模型加速科学事件分析，无需重新训练。
On Foundation Models for Temporal Point Processes to Accelerate Scientific Discovery
- 构建通用事件序列基础模型，通过海量模拟数据学习模式。
- 新数据仅需少量样本即可即时分析，支持快速微调提升精度。
- 适合医疗、地震等需要快速响应事件分析的科研领域。
许多科学领域，如医学和地震学，依赖对时间序列事件的分析来理解复杂系统。传统机器学习模型需为每个新数据集从头构建和训练，过程缓慢且成本高昂。本文提出一种新方法：训练一个强大的单一模型，使其在数百万条模拟事件序列上学习事件演化的普遍规律，形成‘基础模型’。该模型可直接用于分析新科学数据，无需重新训练，仅需查看少量数据样本即可完成分析，同时支持快速微调以获得更高精度。这一方法显著提升了事件分析的可及性，推动科学发现进程。
原文摘要 · Abstract (English)
Many scientific fields, from medicine to seismology, rely on analyzing sequences of events over time to understand complex systems. Traditionally, machine learning models must be built and trained from scratch for each new dataset, which is a slow and costly process. We introduce a new approach: a single, powerful model that learns the underlying patterns of event data in context. We trained this "foundation model" on millions of simulated event sequences, teaching it a general-purpose understanding of how events can unfold. As a result, our model can analyze new scientific data instantly, without retraining, simply by looking at a few examples from the dataset. It can also be quickly fine-tuned for even higher accuracy. This approach makes sophisticated event analysis more accessible and accelerates the pace of scientific discovery.
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