This repository contains the source code for the experiments presented in the following paper:
Variational Learning is Effective for Large Deep Networks
Y. Shen*, N. Daheim*, B. Cong, P. Nickl, G.M. Marconi, C. Bazan, R. Yokota, I. Gurevych, D. Cremers, M.E. Khan, T. Möllenhoff
Paper: https://arxiv.org/abs/2402.17641
This repository mainly consists of Python source code and Bash scripts to run the experiments. Our source code is based on the PyTorch deep learning framework.
This code base depends on an implementation of the IVON optimizer which is released in a separate repo (https://github.com/team-approx-bayes/ivon) and as a pip installable package ivon-opt.
Use the following command to install the necessary dependencies using pip:
pip install -r requirements.txt
You can inspect the requirements.txt file to understand the dependencies and customize them as needed. By default, we use ivon-opt-0.1.2 which is the version of the IVON optimizer we used in the paper, but using newer (and optimized) versions of IVON should give comparable results.
For ImageNet experiments we use ffcv to speed up the dataloading. If you wish to run ImageNet experiments, you need to additionally install ffcv as follows
conda env update --file addon_ffcv.yml
This repository organizes experimental code into separate folders:
gpt2/: contains the GPT-2 experiments in Section 4.1.1. Follow these instructions to run the experiments;distributed/: contains the ImageNet experiments in Section 4.1.2. Follow these instructions to run the experiments;image_classification/: contains the image classification experiments in Section 4.1.2 that can be run on single GPU, i.e. everything except ImageNet experiments. Follow these instructions to run the experiments;bdl_indomain/: covers the in-domain Bayesian deep learning experiments in Section 4.2.1. Follow these instructions to run the experiments;bdl_ood/: covers the OOD experiments from Section 4.2.2. Follow these instructions to run the experiments;mcsamples/: covers the multi MC sample ablation studies from Section 4.2.3. Follow these instructions to run the experiments;bdl_competiton/: covers the NeurIPS 2021 Bayesian deep learning competition mentioned in Section 4.2.4 and Appendix D.3. Follow these instructions to run the experiments;common/: common utility folder used by the source code in other folders.
If this code base helps your research, please consider citing
@inproceedings{shen2024variational,
title={Variational Learning is Effective for Large Deep Networks},
author={Yuesong Shen and Nico Daheim and Bai Cong and Peter Nickl and Gian Maria Marconi and Clement Bazan and Rio Yokota and Iryna Gurevych and Daniel Cremers and Mohammad Emtiyaz Khan and Thomas Möllenhoff},
booktitle={International Conference on Machine Learning (ICML)},
year={2024},
url={https://arxiv.org/abs/2402.17641}
}