Deep learning (DL), as one of the most active machine learning research fields, has
achieved great success in numerous scientific and technological disciplines, including …

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

Probabilistic circuits (PCs) are models that allow exact and tractable probabilistic inference.
In contrast to neural networks, they are often assumed to be well-calibrated and robust to out …

We consider the problem of increasing the expressivity of probabilistic circuits by
augmenting them with the successful generative models of normalizing flows. To this effect …

Probabilistic circuits are a class of probabilistic generative models that allow us to compute
different types of probabilistic queries in polynomial time. Unlike many of the mainstream …

Probabilistic sentential decision diagrams are logic circuits where the inputs of disjunctive
gates are annotated by probability values. They allow for a compact representation of joint …

和积网络(SPNs) 是一种基于有根有向无环图的深度概率图模型. 除了叶节点外,
其余节点由求和节点或求积节点组成. 和积网络与概率图模型密切相关, 但是 …

Abstract Sum-Product Networks (SPNs) and their credal counterparts are machine learning
models that combine good representational power with tractable inference. Yet they often …

Portfolio optimization is a crucial part of many investment approaches and is arguably
employed by almost all traders in a way or another. We introduce novel approaches for …