Semantic probabilistic layers for neuro-symbolic learning
We design a predictive layer for structured-output prediction (SOP) that can be plugged into
any neural network guaranteeing its predictions are consistent with a set of predefined …
any neural network guaranteeing its predictions are consistent with a set of predefined …
Understanding the distillation process from deep generative models to tractable 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 …
tractable probabilistic models that support efficient computation of various inference tasks …
Sparse probabilistic circuits via pruning and growing
Probabilistic circuits (PCs) are a tractable representation of probability distributions allowing
for exact and efficient computation of likelihoods and marginals. There has been significant …
for exact and efficient computation of likelihoods and marginals. There has been significant …
Building Expressive and Tractable Probabilistic Generative Models: A Review
S Sidheekh, S Natarajan - arXiv preprint arXiv:2402.00759, 2024 - arxiv.org
We present a comprehensive survey of the advancements and techniques in the field of
tractable probabilistic generative modeling, primarily focusing on Probabilistic Circuits …
tractable probabilistic generative modeling, primarily focusing on Probabilistic Circuits …
Scaling up probabilistic circuits by latent variable distillation
Probabilistic Circuits (PCs) are a unified framework for tractable probabilistic models that
support efficient computation of various probabilistic queries (eg, marginal probabilities) …
support efficient computation of various probabilistic queries (eg, marginal probabilities) …
Characteristic Circuits
In many real-world scenarios it is crucial to be able to reliably and efficiently reason under
uncertainty while capturing complex relationships in data. Probabilistic circuits (PCs), a …
uncertainty while capturing complex relationships in data. Probabilistic circuits (PCs), a …
Bayesian structure scores for probabilistic circuits
Probabilistic circuits (PCs) are a prominent representation of probability distributions with
tractable inference. While parameter learning in PCs is rigorously studied, structure learning …
tractable inference. While parameter learning in PCs is rigorously studied, structure learning …
Multi-head variational graph autoencoder constrained by sum-product networks
Variational graph autoencoder (VGAE) is a promising deep probabilistic model in graph
representation learning. However, most existing VGAEs adopt the mean-field assumption …
representation learning. However, most existing VGAEs adopt the mean-field assumption …
SPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid Domains
Causal inference in hybrid domains, characterized by a mixture of discrete and continuous
variables, presents a formidable challenge. We take a step towards this direction and …
variables, presents a formidable challenge. We take a step towards this direction and …
Probabilistic Neural Circuits
PZ Dos Martires - Proceedings of the AAAI Conference on Artificial …, 2024 - ojs.aaai.org
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 …
for discussing probabilistic models that support tractable queries and are yet expressive …