Probabilistic models are at the very core of modern machine learning (ML) and artificial
intelligence (AI). Indeed, probability theory provides a principled and almost universally …

Sum–product networks (SPNs) in deep probabilistic models have made great progress in
computer vision, robotics, neuro-symbolic artificial intelligence, natural language …

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 …

We present the Sum-Product Probabilistic Language (SPPL), a new probabilistic
programming language that automatically delivers exact solutions to a broad range of …

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

We focus on the problem of unsupervised cell outlier detection and repair inmixed-type
tabular data. Traditional methods are concerned only with detecting which rows in the …

Sum-product networks (SPNs) are flexible density estimators and have received significant
attention due to their attractive inference properties. While parameter learning in SPNs is …

In many real-world scenarios it is crucial to be able to reliably and efficiently reason under
uncertainty while capturing complex relationships in data. Probabilistic circuits (PCs), a …

Probabilistic circuits (PCs) are a prominent representation of probability distributions with
tractable inference. While parameter learning in PCs is rigorously studied, structure learning …

When collaborating with an AI system, we need to assess when to trust its
recommendations. If we mistakenly trust it in regions where it is likely to err, catastrophic …