This is the official code repository for ICML 2021 paper ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables by Alek Dimitriev and Mingyuan Zhou.
If you use the code in this repository please correspondingly cite the paper:
@inproceedings{dimitriev2021arms,
title = {ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables},
author = {Dimitriev, Alek and Zhou, Mingyuan},
booktitle = {ICML: International Conference on Machine Learning},
pages = {2717--2727},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = jul,
publisher = {PMLR},
pdf = {https://arxiv.org/pdf/2105.14141.pdf},
url = {https://proceedings.mlr.press/v139/dimitriev21a.html},
}
Supported datasets: Dynamic MNIST, Fashion MNIST, and Omniglot, with either a linear or nonlinear encoder/decoder pair. Supported gradients: ARM, DisARM, LOORF, RELAX and ARMS (Dirichlet copula) and ARMS_Normal (Gaussian copula).
The figure below can be reproduced by running python toy_problem.py.
To run an experiment you can start with the below template:
python3 -m experiment_launcher \
--dataset=omniglot \
--logdir=../logs_test \
--grad_type=arms \
--encoder_type=nonlinear \
--num_steps=1e6 \
--num_pairs=3 \
--demean_input \
--initialize_with_bias \