most citedDeeProb-kit: a Python Library for Deep Probabilistic Modelling

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cs.LG2024

What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)?

Lorenzo Loconte, Antonio Mari, Gennaro Gala +5

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

cs.LG2024

Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits

Gennaro Gala, Cassio de Campos, Antonio Vergari +1

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

cs.LG2023

Probabilistic Integral Circuits

Gennaro Gala, Cassio de Campos, Robert Peharz +2

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 contrast,…

cs.LG20232 cited

Bayesian Structure Scores for Probabilistic Circuits

Yang Yang, Gennaro Gala, Robert Peharz

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

cs.LG20221 cited

DeeProb-kit: a Python Library for Deep Probabilistic Modelling

Lorenzo Loconte, Gennaro Gala

DeeProb-kit is a unified library written in Python consisting of a collection of deep probabilistic models (DPMs) that are tractable and exact representations for the modelled prob…