This repository contains code for the paper:
HyperSPNs: Compact and Expressive Probabilistic Circuits
"HyperSPNs: Compact and Expressive Probabilistic Circuits"
Andy Shih, Dorsa Sadigh, Stefano Ermon
In Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS), 2021
@inproceedings{ShihSEneurips21,
author = {Andy Shih and Dorsa Sadigh and Stefano Ermon},
title = {HyperSPNs: Compact and Expressive Probabilistic Circuits},
booktitle = {Advances in Neural Information Processing Systems 34 (NeurIPS)},
month = {december},
year = {2021},
keywords = {conference}
}
conda env create -f environment.yml
Optionally, for EinsumNetworks:
cd EinsumNetworks
pip3 install -r requirements.txt
The Twenty Datasets benchmark is from here.
The Amazon Baby Registries benchmark is from here. The dataset was converted from the set format into the binary format.
The Einsum Network repository is from here.
Experiments can be launched with the helper bash files
runid=0
bash bashfiles/run_hyperspn.bash ${runid} 5
bash bashfiles/run_hyperspn.bash ${runid} 10
bash bashfiles/run_hyperspn.bash ${runid} 20
bash bashfiles/run_spn.bash ${runid} 1e-3
bash bashfiles/run_spn.bash ${runid} 1e-4
bash bashfiles/run_spn.bash ${runid} 1e-5
cd EinsumNetworks/src/
python train_svhn_mixture.py --run=0
python train_svhn_mixture.py --nn --run=0