a python framework to build, learn and reason about probabilistic circuits and tensor networks
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Updated
Oct 22, 2024 - Python
a python framework to build, learn and reason about probabilistic circuits and tensor networks
Code in support of the paper Continuous Mixtures of Tractable Probabilistic Models
GraphSPNs: Sum-Product Networks Benefit From Canonical Orderings
How to Turn Your Knowledge Graph Embeddings into Generative Models
Squared Non-monotonic Probabilistic Circuits
Probabilistic Circuits from the Juice library
Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs
Undergraduate honours project exploring learning Gaussian Mixture Models with negative components.
Probabilistic Circuits in Julia
A Python Library for Deep Probabilistic Modeling
🎲 A Kotlin DSL for probabilistic programming.
Code for Deep Structured Mixtures of Gaussian Processes (DSMGPs)
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
Barebone slides introducing sum-product networks.
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