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自动差异化并不是植物遗传梯度计算的万能药
Mathieu Fourment1, Christiaan J Swanepoel2,3, Jared G Galloway4
1Australian Institute for Microbiology and Infection, University of Technology Sydney, Ultimo, NSW, Australia.
Genome biology and evolution
|June 2, 2023
概括
自动差异化比专门的方法要慢,因为它对遗传学概率梯度有很大影响. 使用基因和机器学习图书馆的综合方法提供了最佳的性能和灵活性.
科学领域:
- 计算统计学 计算统计学
- 机器学习 机器学习
- 人类遗传学 是一个学科.
背景情况:
- 概率模型概率的梯度对于计算统计和机器学习至关重要.
- 像TensorFlow和PyTorch这样的库中的自动差异化 (AD) 提供了一般的梯度计算.
- 与专业代码相比,AD在特定的遗传学任务中的效率尚未得到充分证实.
研究的目的:
- 为了比较自动区分的性能与植物遗传学特定的梯度实现.
- 为了评估梯度计算速度和可扩展性在类遗传概率函数的背景下.
- 评估梯度计算对遗传学变异推理程序的影响.
主要方法:
- 实施并比较了六种不同的梯度计算方法,用于基因概率函数.
- 独立地和在变化推理框架内对实施方案进行评估.
- 根据计算速度和树大小的可扩展性来评估性能.
主要成果:
- 自动差异化与树的大小差不多是线性的.
- 对于树概率和比率转换的专业梯度计算比AD快得多.
- 由于AD的通用性,这些特定的遗传学操作的性能会变慢.
结论:
- 建议采用混合方法,将专业的族系图书馆与一般的机器学习图书馆相结合.
- 这种混合策略平衡了计算速度与复杂的遗传学建模所需的灵活性.
- 未来的进步应该利用专业和通用工具的优势.
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