论文标题
总产品网络：一项调查
Sum-product networks: A survey
论文作者
论文摘要
总和网络（SPN）是基于根的无环向图的概率模型，其中终端节点代表单变量概率分布，而非末端节点代表凸组合（加权总和）和概率函数的产物。它们与概率图形模型密切相关，尤其是具有多个特定于上下文独立性的贝叶斯网络。他们的主要优点是从数据（即可以按时间成比例执行多个推理任务的模型）构建可访问模型的可能性。它们与神经网络有些相似，可以解决相同类型的问题，例如图像处理和自然语言理解。本文提供了SPN的调查，包括其定义，从数据推理和学习的主要算法，主要应用程序，对软件库的简要审查以及与相关模型的比较
A sum-product network (SPN) is a probabilistic model, based on a rooted acyclic directed graph, in which terminal nodes represent univariate probability distributions and non-terminal nodes represent convex combinations (weighted sums) and products of probability functions. They are closely related to probabilistic graphical models, in particular to Bayesian networks with multiple context-specific independencies. Their main advantage is the possibility of building tractable models from data, i.e., models that can perform several inference tasks in time proportional to the number of links in the graph. They are somewhat similar to neural networks and can address the same kinds of problems, such as image processing and natural language understanding. This paper offers a survey of SPNs, including their definition, the main algorithms for inference and learning from data, the main applications, a brief review of software libraries, and a comparison with related models
