GraphSPNs: Sum-Product Networks Benefit From Canonical Orderings

Materials for the AAAI'25 tutorial "From Tensor Factorizations to Circuits (and Back)"

Undergraduate honours project exploring learning Gaussian Mixture Models with negative components.

Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs

C++ implementation of parameter learning algorithms for Sum-Product Networks, aka Probabilistic Circuits

Website for the AAAI'25 Workshop on "Connectin Low-Rank Representations in AI"

Barebone slides introducing sum-product networks.

Probabilistic Circuits in Julia

Code for Deep Structured Mixtures of Gaussian Processes (DSMGPs)

Code in support of the paper Continuous Mixtures of Tractable Probabilistic Models

🎲 A Kotlin DSL for probabilistic programming.

PyTorch implementation for "HyperSPNs: Compact and Expressive Probabilistic Circuits", NeurIPS 2021

PyTorch implementation for "Probabilistic Circuits for Variational Inference in Discrete Graphical Models", NeurIPS 2020

Squared Non-monotonic Probabilistic Circuits

How to Turn Your Knowledge Graph Embeddings into Generative Models

A Python Library for Deep Probabilistic Modeling

a python framework to build, learn and reason about probabilistic circuits and tensor networks

Probabilistic Circuits from the Juice library