We design a predictive layer for structured-output prediction (SOP) that can be plugged into
any neural network guaranteeing its predictions are consistent with a set of predefined …

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

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

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

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

Juice is an open-source Julia package providing tools for logic and probabilistic reasoning
and learning based on logic circuits (LCs) and probabilistic circuits (PCs). It provides a …

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 …

While probabilistic graphical models are a central tool for reasoning under uncertainty in AI,
they are in general not as expressive as deep neural models, and inference is notoriously …

Probabilistic circuits (PCs) are a family of generative models which allows for the
computation of exact likelihoods and marginals of its probability distributions. PCs are both …

Probabilistic circuits (PCs) is a unifying representation for probabilistic models that support
tractable inference. Numerous applications of PCs like controllable text generation depend …