probabilistic-flow-circuits

This repository contains the code for the research paper Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference, which was presented at UAI 2023.

Setup Instructions

Create a new conda or virtual environment. Activate the environment and install the required packages

conda activate ENV_NAME   # or source venv/bin/activate
pip install -r requirements.txt 

How to run

You can use trainer.py to run the experiments. You can specify the model, dataset and hyperparameters using config.py or by passing them as command line arguments to trainer.py . For example,

To train EinsumNet+LRS on the 3D dataset KNOTTED, you may run:

python trainer.py --dataset KNOTTED --model LinearSplineEinsumFlow   --config '{"log_freq":10, "lr":0.001, "epochs":200}' --graph random_binary_tree

Similarly, to train EinsumNet on the 3D dataset KNOTTED, you may run:

python trainer.py --dataset KNOTTED --model EinsumNet   --config '{"log_freq":10, "lr":0.001, "epochs":200}' --graph random_binary_tree

Citation

If you find probabilistic flow circuits useful in your research, kindly cite the following paper:

@inproceedings{
    sidheekh2023probabilistic,
    title={Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference},
    author={Sahil Sidheekh, Kristian Kersting, Sriraam Natarajan},
    booktitle={The 39th Conference on Uncertainty in Artificial Intelligence},
    year={2023},
    url={https://openreview.net/forum?id=1oE7YizXHf}
}

License

This project is distributed under MIT license.

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