Automatic Integration for Fast and Interpretable Neural Point Processes
Dependencies: make
, conda-lock
make create_environment
conda activate autonpp
make download prefix=data
make download prefix=models
Specify the parameters in configs/test_autoint_1d_dataset.yaml
and then run
make run
The loss curves and example intensity predictions are saved to figs/
.
With real-world datasets, the ground truth intensity is a placeholder and can be safely ignored.
The logs are saved to logs/
.
The models are saved to models/
.
To use the trained models, set retrain: false
.
@article{zhou2023automatic,
title={Automatic Integration for Fast and Interpretable Neural Point Processes},
author={Zhou, Zihao and Yu, Rose},
journal={Learning for Dynamics and Control (L4DC)},
year={2023}
}