论文标题
i-flow：高维整合和与标准化流的采样
i-flow: High-dimensional Integration and Sampling with Normalizing Flows
论文作者
论文摘要
在许多科学领域，都需要高维整合。已经开发了数值方法来评估这些复杂的积分。我们介绍了I-Flow的代码I-Flow，这是一个使用归一化流的Python软件包，该软件包执行高维数值集成。正常化的流是机器学习的，两种分布之间的射合映射。 i-flow也可以根据高维度的复杂分布来采样随机点。我们将i-Flow与其他用于高维数值集成的算法进行比较，并表明I-Flow优于高维相关积分的表现。 i-flow代码可在gitlab上在https://gitlab.com/i-flow/i-flow上公开获得。
In many fields of science, high-dimensional integration is required. Numerical methods have been developed to evaluate these complex integrals. We introduce the code i-flow, a python package that performs high-dimensional numerical integration utilizing normalizing flows. Normalizing flows are machine-learned, bijective mappings between two distributions. i-flow can also be used to sample random points according to complicated distributions in high dimensions. We compare i-flow to other algorithms for high-dimensional numerical integration and show that i-flow outperforms them for high dimensional correlated integrals. The i-flow code is publicly available on gitlab at https://gitlab.com/i-flow/i-flow.
