Despite the success of autoregressive large language models in text generation, it remains
a major challenge to generate text that satisfies complex constraints: sampling from the …

Abstract Probabilistic Circuits (PCs) are a general and unified computational framework for
tractable probabilistic models that support efficient computation of various inference tasks …

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 …

Probabilistic circuits (PCs) are a tractable representation of probability distributions allowing
for exact and efficient computation of likelihoods and marginals. There has been significant …

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

Probabilistic Circuits (PCs) are a unified framework for tractable probabilistic models that
support efficient computation of various probabilistic queries (eg, marginal probabilities) …

Abstract Probabilistic Circuits (PCs) are a promising avenue for probabilistic modeling. They
combine advantages of probabilistic graphical models (PGMs) with those of neural networks …

Understanding the dependencies among features of a dataset is at the core of most
unsupervised learning tasks. However, a majority of generative modeling approaches are …

Conditional inference on arbitrary subsets of variables is a core problem in probabilistic
inference with important applications such as masked language modeling and image …

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 …