تقرير
BUFF: Boosted Decision Tree based Ultra-Fast Flow matching
| العنوان: | BUFF: Boosted Decision Tree based Ultra-Fast Flow matching |
|---|---|
| المؤلفون: | Jiang, Cheng, Qian, Sitian, Qu, Huilin |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science High Energy Physics - Experiment High Energy Physics - Phenomenology Physics (Other) |
| مصطلحات موضوعية: | Physics - Instrumentation and Detectors, Computer Science - Machine Learning, High Energy Physics - Experiment, High Energy Physics - Phenomenology, Physics - Data Analysis, Statistics and Probability |
| الوصف: | Tabular data stands out as one of the most frequently encountered types in high energy physics. Unlike commonly homogeneous data such as pixelated images, simulating high-dimensional tabular data and accurately capturing their correlations are often quite challenging, even with the most advanced architectures. Based on the findings that tree-based models surpass the performance of deep learning models for tasks specific to tabular data, we adopt the very recent generative modeling class named conditional flow matching and employ different techniques to integrate the usage of Gradient Boosted Trees. The performances are evaluated for various tasks on different analysis level with several public datasets. We demonstrate the training and inference time of most high-level simulation tasks can achieve speedup by orders of magnitude. The application can be extended to low-level feature simulation and conditioned generations with competitive performance. Comment: 9 pages, 10 figures, 1 additional figure in appendix |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.18219 |
| رقم الأكسشن: | edsarx.2404.18219 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |