This paper introduces Bayesian Flow Networks (BFNs), a new class of generative model in
which the parameters of a set of independent distributions are modified with Bayesian …

Diffusion models for continuous data gained widespread adoption owing to their high quality
generation and control mechanisms. However, controllable diffusion on discrete data faces …

We introduce marginalization models (MaMs), a new family of generative models for high-
dimensional discrete data. They offer scalable and flexible generative modeling with …

Probabilistic circuits (PCs) have gained prominence in recent years as a versatile framework
for discussing probabilistic models that support tractable queries and are yet expressive …

Latent diffusion models have enabled continuous-state diffusion models to handle a variety
of datasets, including categorical data. However, most methods rely on fixed pretrained …

We show how to use parallelization to speed up sampling from an arbitrary distribution µ on
a product space [q] n, given oracle access to counting queries: ℙ X∼ µ [XS= σ S] for any …

Devising deep latent variable models for multi-modal data has been a long-standing theme
in machine learning research. Multi-modal Variational Autoencoders (VAEs) have been a …

Diffusion models for discrete data have gained increasing interest lately. Recent methods
use an autoregressive formulation, but where the generation order is random. In this work …

Masked (or absorbing) diffusion is actively explored as an alternative to autoregressive
models for generative modeling of discrete data. However, existing work in this area has …

Probabilistic circuits (PCs) have gained prominence in recent years as a versatile framework
for discussing probabilistic models that support tractable queries and are yet expressive …