Pytorch implementation of the paper "Intensity-Free Learning of Temporal Point Processes", Oleksandr Shchur, Marin Biloš and Stephan Günnemann, ICLR 2020.
In order to run the code, you need to install the dpp
library that contains all the algorithms described in the paper
cd code
python setup.py install
A Jupyter notebook code/interactive.ipynb
contains the code for training models on the datasets used in the paper.
The same code can also be run as a Python script code/train.py
.
Another notebook code/generate_embeddings.ipynb
shows how to learn sequence embeddings for different synthetic datasets.
numpy=1.16.4
pytorch=1.2.0
scikit-learn=0.21.2
scipy=1.3.1
Please cite our paper if you use the code or datasets in your own work
@article{
shchur2020intensity,
title={Intensity-Free Learning of Temporal Point Processes},
author={Oleksandr Shchur and Marin Bilo\v{s} and Stephan G\"{u}nnemann},
journal={International Conference on Learning Representations (ICLR)},
year={2020},
}