LMMNN

Integrating Random Effects in Deep Neural Networks

This is the working directory for our JMLR 2023 and NeurIPS 2021 papaers.

For full implementation details see the papers and supplemental material.

For running the simulations use the simulate.py file, like so:

python simulate.py --conf conf_files/conf_random_intercepts.yaml --out res.csv

The --conf attribute accepts a yaml file such as conf_random_intercepts.yaml which you can change.

To run various real data experiments see the jupyter notebooks in the notebooks folder. We cannot unfortunately attach the actual datasets, see papers for details.

For using LMMNN with your own data use the NLL loss layer as shown in notebooks and simulation.