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 …

Circuit representations are becoming the lingua franca to express and reason about
tractable generative and discriminative models. In this paper, we show how complex …

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

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

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 …

Probabilistic Circuits (PCs) are a general framework for tractable deep generative models,
which support exact and efficient probabilistic inference on their learned distributions …

Despite extensive progress on image generation, common deep generative model
architectures are not easily applied to lossless compression. For example, VAEs suffer from …

Understanding the behavior of learned classifiers is an important task, and various black-
box explanations, logical reasoning approaches, and model-specific methods have been …

Computation in several real-world applications such as probabilistic machine learning,
sparse linear algebra, and robotic navigation can be modeled as irregular directed acyclic …