normflows：一个用于归一化流的PyTorch包
normflows: A PyTorch Package for Normalizing Flows
AI总结：
本文介绍了normflows，一个基于PyTorch的Python包，用于构建和集成归一化流模型，支持多种常见架构，便于在机器学习中应用。
摘要：
归一化流通过一种富有表现力且易于处理的密度来对概率分布进行建模。它们通过一系列被称为层的可逆函数，将一个简单的基础分布（如高斯分布）进行转换。这些层通常使用神经网络来变得极具表现力。流在机器学习中无处不在，已被应用于图像生成、文本建模、变分推断、近似玻尔兹曼分布以及许多其他问题。在此，我们提出了normflows，一个用于归一化流的Python包。它允许从一套基础分布、流层和神经网络中构建归一化流模型。该包在流行的深度学习框架PyTorch中实现，这简化了将流集成到更大的机器学习模型或流程中的过程。它支持大多数常见的归一化流架构，如Real NVP、Glow、Masked Autoregressive Flows、Neural Spline Flows、Residual Flows等。该包可以通过pip轻松安装，代码在GitHub上公开可用。
英文摘要：
Normalizing flows model probability distributions through an expressive tractable density. They transform a simple base distribution, such as a Gaussian, through a sequence of invertible functions, which are referred to as layers. These layers typically use neural networks to become very expressive. Flows are ubiquitous in machine learning and have been applied to image generation, text modeling, variational inference, approximating Boltzmann distributions, and many other problems. Here, we present normflows, a Python package for normalizing flows. It allows to build normalizing flow models from a suite of base distributions, flow layers, and neural networks. The package is implemented in the popular deep learning framework PyTorch, which simplifies the integration of flows in larger machine learning models or pipelines. It supports most of the common normalizing flow architectures, such as Real NVP, Glow, Masked Autoregressive Flows, Neural Spline Flows, Residual Flows, and many more. The package can be easily installed via pip and the code is publicly available on GitHub.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
