A sum-product network (SPN) is a probabilistic model, based on a rooted acyclic directed
graph, in which terminal nodes represent probability distributions and non-terminal nodes …

The past decade has seen a significant interest in learning tractable probabilistic
representations. Arithmetic circuits (ACs) were among the first proposed tractable …

Abstract Sum-Product Networks (SPNs) are deep tractable probabilistic models by which
several kinds of inference queries can be answered exactly and in a tractable time. They …

We give conditions under which convolutional neural networks (CNNs) define valid sum-
product networks (SPNs). One subclass, called convolutional SPNs (CSPNs), can be …

Probabilistic circuits (PCs) are a class of tractable probabilistic models that allow efficient,
often linear-time, inference of queries such as marginals and most probable explanations …

Dashboards are vital in modern business intelligence tools, providing non-technical users
with an interface to access comprehensive business data. With the rise of cloud technology …

Sum-product networks (SPNs) are a class of probabilistic graphical models that allow
tractable marginal inference. However, the maximum a posteriori (MAP) inference in SPNs is …

Tractable Boolean and arithmetic circuits have been studied extensively in AI for over two
decades now. These circuits were initially proposed as “compiled objects,” meant to facilitate …

Sum-product networks are a relatively new and increasingly popular family of probabilistic
graphical models that allow for marginal inference with polynomial effort. They have been …

Probabilistic circuits (PCs) is a unifying representation for probabilistic models that support
tractable inference. Numerous applications of PCs like controllable text generation depend …