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Updated: Sep 11, 2025

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Analysis of SEC-SAXS data via EFA deconvolution and Scatter
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traceax:用于随机跟踪估计的基于JAX的框架
Abdullah Al Nahid1, Linda Serafin2, Nicholas Mancuso3,2,4
1Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, USA.
bioRxiv : the preprint server for biology
|August 12, 2025
概括
随机跟踪估计为机器学习和统计学中的大矩阵提供了一个内存高效的解决方案. 新的 traceax 框架允许使用 Python 进行可扩展,准确的 trace 估计,从而降低计算成本.
科学领域:
- 计算数学是指计算数学.
- 机器学习是机器学习.
- 统计推断的统计推断.
背景情况:
- 矩阵跟踪计算是必不可少的,但往往会阻碍记忆.
- 随机跟踪估计提供了使用随机方法的可行替代方案.
- 现有的方法可能缺乏可扩展性或集成能力.
研究的目的:
- 介绍traceax,这是一个Python框架,用于可扩展的随机跟踪估计.
- 与直接计算相比,证明traceax的效率和准确性.
- 促进先进的痕迹估计器融入推理管道.
主要方法:
- 利用线性操作员表示来实现高效的矩阵处理.
- 实施最先进的随机跟踪估计器.
- 利用自动区分和硬件加速来提高性能.
主要成果:
- 模拟证实了轨迹轴估计器的高准确性.
- 运行时间和内存使用量显著减少.
- 成功实施一个随机遗传性估计器作为概念证明.
结论:
- traceax为随机跟踪估计提供了一种多功能和可扩展的工具.
- 该框架支持高效集成到现有的机器学习和统计管道中.
- 能够对大规模问题进行先进的痕迹估计.
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