Probabilistic integral circuits
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 …
allow modelling expressive mixtures with an uncountable number of components. In …
What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)?
This paper establishes a rigorous connection between circuit representations and tensor
factorizations, two seemingly distinct yet fundamentally related areas. By connecting these …
factorizations, two seemingly distinct yet fundamentally related areas. By connecting these …
KLay: Accelerating Neurosymbolic AI
A popular approach to neurosymbolic AI involves mapping logic formulas to arithmetic
circuits (computation graphs consisting of sums and products) and passing the outputs of a …
circuits (computation graphs consisting of sums and products) and passing the outputs of a …
On the relationship between monotone and squared probabilistic circuits
Probabilistic circuits are a unifying representation of functions as computation graphs of
weighted sums and products. Their primary application is in probabilistic modeling, where …
weighted sums and products. Their primary application is in probabilistic modeling, where …
Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits
Probabilistic integral circuits (PICs) have been recently introduced as probabilistic models
enjoying the key ingredient behind expressive generative models: continuous latent …
enjoying the key ingredient behind expressive generative models: continuous latent …