Sparse Measure is a companion toy example for our tutorial "Hands-on Bayesian Neural Networks - A Tutorial for Deep Learning Users". It aims at showing how different learning strategies can be implemented for a BNN.

The code depends on:

• numpy (tested with version 1.19.2),
• scipy (tested with version 1.3.1),
• pytorch (tested with version 1.8.1),
• matplotlib (tested with version 3.1.1),

and one library from the base python distribution: argparse.

It has been tested with python 3.6.9.

The project is split into multiple files:

• dataset.py implement the primitives to generate the dataset.

• experiment.py contain a small script to run the experiment which generated figure 16 in the paper

• viModel.py implement the variational inference layers and model we are using.

• viExperiment.py is the script running the actual experiment. It can be called with the -h option to get a contextual help message:

  python viExperiment.py -h

If you use our code in your project please cite our tutorial:

@ARTICLE{9756596,
author={Jospin, Laurent Valentin and Laga, Hamid and Boussaid, Farid and Buntine, Wray and Bennamoun, Mohammed},
journal={IEEE Computational Intelligence Magazine}, 
title={Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users}, 
year={2022},
volume={17},
number={2},
pages={29-48},
doi={10.1109/MCI.2022.3155327}
}