This repository is the official implementation of Probabilistic Integral Circuits.
@InProceedings{gala24pic,
title={Probabilistic Integral Circuits},
author={Gala, Gennaro and de Campos, Cassio and Peharz, Robert and Vergari, Antonio and Quaeghebeur, Erik},
booktitle={Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
year={2024}
}
MNIST-famility datasets: all MNIST-famility datasets are available in torchvision, and automatically downloaded if needed.
PTB288: download it here, and place it in data/ptbchar_288
Binary datasets (DEBD): download them here, and place them in data/debd
UCI datasets: download non pre-processed datasets here or pre-processed datasets here, and place them in data/UCI
python train_pic.py -ds mnist -bs 256 -nip 128 -int trapezoidal
python train_pic.py -ds fashion_mnist -bs 256 -nip 128 -int trapezoidal
python train_pic.py -ds emnist -split mnist -bs 256 -nip 128 -int trapezoidal
python train_pic.py -ds emnist -split letters -bs 256 -nip 128 -int trapezoidal
python train_pic.py -ds emnist -split balanced -bs 256 -nip 128 -int trapezoidal
python train_pic.py -ds emnist -split byclass -bs 256 -nip 128 -int trapezoidal
python train_hclt.py -ds mnist -bs 256 -hd 128
python train_hclt.py -ds fashion_mnist -bs 256 -hd 128
python train_hclt.py -ds emnist -split mnist -bs 256 -hd 128
python train_hclt.py -ds emnist -split letters -bs 256 -hd 128
python train_hclt.py -ds emnist -split balanced -bs 256 -hd 128
python train_hclt.py -ds emnist -split byclass -bs 256 -hd 128