使用施罗丁格桥的稳定生成建模
Georg A Gottwald1, Fengyi Li2, Youssef Marzouk2
1School of Mathematics and Statistics, University of Sydney, Sydney, New South Wales, Australia.
概括
这项研究引入了一个新的生成模型,使用施罗丁格桥梁和朗格温动力学来从未知分布中取样. 该方法提高了样本生成的稳定性,并确保样本保持在训练数据范围内.
科学领域:
- 计算统计学 计算统计学
- 机器学习 机器学习
- 应用数学 应用数学 应用数学
背景情况:
- 生成模型和贝叶斯推理越来越依赖于从复杂的未知分布中采样.
- 现有的方法面临样本稳定性和计算效率方面的挑战.
研究的目的:
- 提出一种结合施罗丁格桥梁和朗格温动态的新型生成模型,以改进样本生成.
- 解决从刚性随机微分方程采样中的稳定性问题.
- 扩大条件抽样和贝叶斯推理的框架.
主要方法:
- 在可逆参考过程中使用施罗丁格桥梁来近似条件转换概率.
- 实现一个离散时间可逆的Langevin采样器.
- 使用分步方案,在训练数据的凸体内保持样本属性.
主要成果:
- 提出的方法有效地规避了与时间渐变的刚性随机微分方程相关的稳定性问题.
- 生成的样本保证保持在训练样本的凸体内.
- 在合成数据,子网尺度参数化和动态系统轨迹生成方面表现出良好的性能.
结论:
- 合并的施罗丁格桥梁和朗格文动态方法为生成建模和贝叶斯推理提供了稳定有效的方法.
- 该框架的灵活性允许扩展到条件抽样和复杂的反向问题.
- 这项工作有助于生成建模和贝叶斯推理之间的协同作用,用于逆问题.
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