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 …

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 general and unified computational framework for
tractable probabilistic models that support efficient computation of various inference tasks …

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

This paper establishes a rigorous connection between circuit representations and tensor
factorizations, two seemingly distinct yet fundamentally related areas. By connecting these …

Continuous latent variables (LVs) are a key ingredient of many generative models, as they
allow modelling expressive mixtures with an uncountable number of components. In …

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

Diffusion models are the current state of the art for generating photorealistic images.
Controlling the sampling process for constrained image generation tasks such as inpainting …

Tensorizing probabilistic circuits (PCs)-structured computational graphs capable of
efficiently and accurately performing various probabilistic reasoning tasks-is the go-to way to …

Probabilistic integral circuits (PICs) have been recently introduced as probabilistic models
enjoying the key ingredient behind expressive generative models: continuous latent …