论文标题
Deeprob-kit：用于深概率建模的Python库
DeeProb-kit: a Python Library for Deep Probabilistic Modelling
论文作者
论文摘要
Deeprob-kit是一个用python编写的统一库，由深层概率模型（DPM）组成，这些模型（dpms）是易于处理的概率且精确的表示形式的。单个库中DPM的代表性选择的可用性使得以直接的方式将它们组合在一起，这是当今深度学习研究中的常见实践。此外，它还包括有效实施的学习技术，推理例程，统计算法，并提供了高质量的完全证明的API。 Deeprob-kit的发展将有助于社区加速对DPM的研究，并标准化其评估，并更好地根据其表达性理解它们的关系。
DeeProb-kit is a unified library written in Python consisting of a collection of deep probabilistic models (DPMs) that are tractable and exact representations for the modelled probability distributions. The availability of a representative selection of DPMs in a single library makes it possible to combine them in a straightforward manner, a common practice in deep learning research nowadays. In addition, it includes efficiently implemented learning techniques, inference routines, statistical algorithms, and provides high-quality fully-documented APIs. The development of DeeProb-kit will help the community to accelerate research on DPMs as well as to standardise their evaluation and better understand how they are related based on their expressivity.
