# ifl-tpp

**Repository Path**: z2007c/ifl-tpp

## Basic Information

- **Project Name**: ifl-tpp
- **Description**: Implementation of "Intensity-Free Learning of Temporal Point Processes"
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-10-04
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Intensity-Free Learning of Temporal Point Processes

Pytorch implementation of the paper ["Intensity-Free Learning of Temporal Point Processes"](https://openreview.net/forum?id=HygOjhEYDH), Oleksandr Shchur, Marin Biloš and Stephan Günnemann, ICLR 2020.

## Usage
In order to run the code, you need to install the `dpp` library that contains all the algorithms described in the paper
```bash
cd code
python setup.py install
```

A Jupyter notebook [`code/interactive.ipynb`](https://github.com/shchur/ifl-tpp/blob/master/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`](https://github.com/shchur/ifl-tpp/blob/master/code/train.py).

Another notebook [`code/generate_embeddings.ipynb`](https://github.com/shchur/ifl-tpp/blob/master/code/generate_embeddings.ipynb) shows how to learn sequence embeddings for different synthetic datasets.


## Requirements
```
numpy=1.16.4
pytorch=1.2.0
scikit-learn=0.21.2
scipy=1.3.1
```


## Cite
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},
}
```