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Found 5 papers, 4 papers with code
Probabilistic Integral Circuits
In contrast, probabilistic circuits (PCs) are hierarchical discrete mixtures represented as computational graphs composed of input, sum and product units.
Bayesian Structure Scores for Probabilistic Circuits
Probabilistic circuits (PCs) are a prominent representation of probability distributions with tractable inference.
E(n)-equivariant Graph Neural Cellular Automata
Cellular automata (CAs) are computational models exhibiting rich dynamics emerging from the local interaction of cells arranged in a regular lattice.
DeeProb-kit: a Python Library for Deep Probabilistic Modelling
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 probability distributions.
Continuous Mixtures of Tractable Probabilistic Models
Meanwhile, tractable probabilistic models such as probabilistic circuits (PCs) can be understood as hierarchical discrete mixture models, and thus are capable of performing exact inference efficiently but often show subpar performance in comparison to continuous latent-space models.
