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Repulsive Deep Ensembles are Bayesian

This repo contains the code of the paper Repulsive deep ensembles are Bayesian. In the following some usage examples can be found

Sampling from synthetic distributions experiments

The experiment for the synthetic distributions can be found in 'notebooks/WGD_synthetic.ipynb'

1d regression experiments

The 1d toy regression problem can be explored. Example run:

$ python3 experiments/exp_regr.py --epochs 5000 --lr 1e-2 --n_particles 100 --size_hidden 10 --num_hidden 2 --method SGD --prior_variance 1 --annealing_steps 1000  --batch_size 32 --dataset toy_reg --ann_sch None 

2d classification experiments

The 2d classification problem can be explored. Example run:

$ python3 experiments/exp_2d_class.py --epochs 10000 --lr 1e-2 --n_particles 100 --size_hidden 10 --num_hidden 2 --method SVGD --prior_variance 1 --annealing_steps 1000 --batch_size 128 --dataset twod_gaussian --ann_sch None 

Citation

If you use our code or consider our ideas in your research project, please consider citing our paper.

@article{d2021repulsive,
  title={Repulsive Deep Ensembles are Bayesian},
  author={D'Angelo, Francesco and Fortuin, Vincent},
  journal={arXiv preprint arXiv:2106.11642},
  year={2021}
}