Learning Aggregation Functions - Code

This repository contains the python code for reproducing the experiments described in the paper Learning Aggregation Functions, appearing in the IJCAI 2021 proceedings (https://arxiv.org/abs/2012.08482) by Giovanni Pellegrini, Alessandro Tibo, Paolo Frasconi, Andrea Passerini, and Manfred Jaeger.

The current implemention relies on PyTorch. However, a TensorFlow version of the LAF layer is implemented in laf/model_tf.py.

Python Requirments

• numpy
• pytorch (version >= 2.0)
• tqdm
• torch-scatter (see instructions on https://github.com/rusty1s/pytorch_scatter)
• torchvision

Run the Experiments for MNIST

$ cd mnist
$ ./run.sh

Supplementary Material

Please read the supplementary.pdf file if you are interested in learning more about the supplementary material of our IJCAI 2021 paper.

Citation

@misc{pellegrini2020learning,
  title={Learning Aggregation Functions}, 
  author={Giovanni Pellegrini and Alessandro Tibo and Paolo Frasconi and Andrea Passerini and Manfred Jaeger},
  year={2020},
  eprint={2012.08482},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}