BUFF: Boosted Decision Tree based Ultra-Fast Flow matching

Autor:	Jiang, Cheng, Qian, Sitian, Qu, Huilin
Rok vydání:	2024
Předmět:	Physics - Instrumentation and Detectors Computer Science - Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology Physics - Data Analysis Statistics and Probability
Druh dokumentu:	Working Paper
Popis:	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
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.18219 Zobrazit plný text záznamu View this record from Arxiv