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通过最佳运输和投影追踪来生成时间依赖密度的建模
Jonah Botvinick-Greenhouse1, Yunan Yang2, Romit Maulik3,4
1Center for Applied Mathematics, Cornell University, Ithaca, New York 14850, USA.
Chaos (Woodbury, N.Y.)
|October 13, 2023
概括
我们开发了一种计算效率高的深度学习方法,用于模拟时间密度. 这种方法使用最佳的传输和传输线条,为高维数据提供可扩展和可并行替代方案.
科学领域:
- 计算数学是指计算数学.
- 机器学习 机器学习
- 数据科学是数据科学.
背景情况:
- 对于时间密度的深度学习生成模型面临着计算挑战.
- 现有的方法通常需要广泛的超参数调整,并与高维数据作斗争.
研究的目的:
- 为时间密度的生成建模提出一个计算效率高,可扩展的替代方案.
- 引入一种需要最小的超参数调整的方法,适用于高维问题.
主要方法:
- 使用基于投影的最佳运输解决方案来连接连续的样本.
- 运用运输线条来插曲不断变化的概率密度.
- 利用数值线性代数进行分析和控制,避免非凸式优化.
主要成果:
- 证明了计算效率,特别是在高采样频率下,最佳地图接近身份.
- 由于独立的最佳地图计算,展示了高的并行性.
- 实现了与各种维度的最先进的时间条件规范流动相对应的竞争性性能.
结论:
- 拟议的方法为时间密度的生成建模提供了一个具有成本效益和可扩展的解决方案.
- 它在数值线性代数的基础上增强了分析处理能力和控制能力.
- 该方法在各种合成和现实世界数据集中是强大的和具有竞争力的.
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