(Reproduction)Sum-product network implementation and its application to image completion.
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Updated
May 30, 2019 - C++
(Reproduction)Sum-product network implementation and its application to image completion.
Survey and presentation about Sum-Product Networks (SPNs)
Optimisation of Overparametrized Sum-Product Networks
Personal fork of the official EinsumNetworks implementation with a few enhancements.
Simple implementation (in Go) of algorithm to convert SPNs into BNs with ADDs.
Barebone slides introducing sum-product networks.
Tractable Machine Learning in Cosmological Structure Formation.
The first Scala-based library for Sum-Product Networks
PyTorch implementation for "Probabilistic Circuits for Variational Inference in Discrete Graphical Models", NeurIPS 2020
GoDrive is an application of autonomous driving through image classification using sum-product networks.
Safe Semi-Supervised Learning of Sum-Product Networks
PyTorch implementation for "HyperSPNs: Compact and Expressive Probabilistic Circuits", NeurIPS 2021
Code and supplemental material for "Sum-Product Autoencoding: Encoding and Decoding Representations using Sum-Product Networks"
replication of this paper: https://www.nrl.navy.mil/itd/aic/content/evaluation-sum-product-networks-image-classification-tasks
Probabilistic Circuits in Julia
Sum-Product Networks (SPNs) for Robust Automatic Speaker Identification.
🔆 A Python implementation of a sum-product network with gaussian processes leafs model (SPNGP, arXiv:1809.04400) 📃
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.
Sum-product networks in Julia.
Sum-Product Network learning routines in python
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