推断与神经网络的高阶合
Aurélien Decelle1, Alfonso de Jesús Navas Gómez2, Beatriz Seoane3
1Universidad Complutense de Madrid, Universidad Politécnica de Madrid, Escuela Técnica Superior de Ingenieros Industriales, Calle de José Gutiérrez Abascal 2, Madrid 28006, Spain and Departamento de Física Teórica, 28040 Madrid, Spain.
Physical review letters
|November 30, 2025
概括
这项研究引入了一种新的方法,利用受限制的博尔兹曼机器 (RBM) 从复杂系统中提取更高阶的相互作用. 这种方法有效地模拟了多体合,在生物信息学和神经科学应用中表现优于传统方法.
科学领域:
- 统计物理 统计物理
- 机器学习 机器学习
- 生物信息学是一种生物信息学.
- 神经科学是一个神经科学.
背景情况:
- 最大的方法推断复杂系统中的对交互,但错过了更高阶的效应.
- 现有的机器学习模型捕捉了更高阶的交互,但在计算上是昂贵的.
- 限制波兹曼机器 (RBMs) 为建模相关性提供了一个高效的替代方案.
研究的目的:
- 开发一种方法,从复杂的系统中提取任意顺序的相互作用.
- 为了利用RBM来有效地建模多体合器.
- 为了使生物数据中更高阶结构的准确重建.
主要方法:
- 将受限制的博尔兹曼机器 (RBM) 映射到泛化的波茨模型.
- 使用大N近似来有效地提取多体合器.
- 在生成模型中开发一个用于恢复高阶相互作用的框架.
主要成果:
- 在合成数据上演示的两体和三体相互作用的准确恢复.
- 使用真实序列数据对蛋白质接触图的高保真重建.
- 在接触地图预测中超越最先进的逆波茨模型的性能.
结论:
- RBMs提供了一个计算高效和强大的工具,用于建模更高阶结构.
- 开发的方法允许系统地提取复杂的相互作用.
- 这种方法对需要分析高维分类数据的领域具有重大意义.
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