Sum Product Flow: An Easy and Extensible Library for Sum-Product Networks
A structured list of resources about Sum-Product Networks (SPNs)
Implementation of DeepDB: Learn from Data, not from Queries!
Probabilistic programming system for fast and exact symbolic inference
A Python Library for Deep Probabilistic Modeling
Sum-product networks in Julia.
Sum-Product Network learning routines in python
🔆 A Python implementation of a sum-product network with gaussian processes leafs model (SPNGP, arXiv:1809.04400) 📃
An implementation of EinsumNetworks in PyTorch.
PyTorch implementation for "Probabilistic Circuits for Variational Inference in Discrete Graphical Models", NeurIPS 2020
PyTorch implementation for "HyperSPNs: Compact and Expressive Probabilistic Circuits", NeurIPS 2021
Sum-Product Networks (SPNs) for Robust Automatic Speaker Identification.
Code for Deep Structured Mixtures of Gaussian Processes (DSMGPs)
Probabilistic Circuits in Julia
Code and supplemental material for "Sum-Product Autoencoding: Encoding and Decoding Representations using Sum-Product Networks"
DeepNotebooks is an automated statistical analysis tool build on top of SPNs. They are currently being developed by Claas Völcker at the ML group at TU Darmstadt.
Barebone slides introducing sum-product networks.
Safe Semi-Supervised Learning of Sum-Product Networks
Personal fork of the official EinsumNetworks implementation with a few enhancements.
Optimisation of Overparametrized Sum-Product Networks
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