When humans observe a physical system, they can easily locate objects, understand their
interactions, and anticipate future behavior, even in settings with complicated and previously …

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 …

Computing expected predictions of discriminative models is a fundamental task in machine
learning that appears in many interesting applications such as fairness, handling missing …

We present a comprehensive survey of the advancements and techniques in the field of
tractable probabilistic generative modeling, primarily focusing on Probabilistic Circuits …

Abstract The recent Attend-Infer-Repeat (AIR) framework marks a milestone in structured
probabilistic modeling, as it tackles the challenging problem of unsupervised scene …

Probabilistic graphical models are a central tool in AI, however, they are generally not as
expressive as deep neural models, and inference is notoriously hard and slow. In contrast …

Making sense of a dataset in an automatic and unsupervised fashion is a challenging
problem in statistics and AI. Classical approaches for exploratory data analysis are usually …

This paper establishes a rigorous connection between circuit representations and tensor
factorizations, two seemingly distinct yet fundamentally related areas. By connecting these …

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

The need for consistent treatment of uncertainty has recently triggered increased interest in
probabilistic deep learning methods. However, most current approaches have severe …