Yilun Du, Shuang Li, Joshua B. Tenenbaum, Igor Mordatch
A link to our paper can be found on arXiv.
Official codebase for [Learning Iterative Reasoning through Energy Minimization](). Contains scripts to reproduce experiments.
Please install the listed requirements.txt file.
pip install -r requirements.txt
We provide code for running continuous graph reasoning experiments in graph_train.py
and code for running continuous matrix experiments in train.py
.
To run continuous matrix addition experiments you utilize the following command:
python train.py --exp=addition_experiment --train --num_steps=10 --dataset=addition --train --cuda --infinite
To evaluate the final performance of the model after training, you may use the command:
python train.py --exp=addition_experiment --num_steps=10 --dataset=addition --cuda --infinite --resume_iter=10000
and the following command for the OOD test set:
python train.py --exp=addition_experiment --num_steps=10 --dataset=addition --cuda --infinite --resume_iter=10000 --ood
We may substitute the flag --dataset with other keywords such as inverse or lowrank (as well as additional ones defined in dataset.py).
To run discrete graph reasoning experiments you may utilize the following command:
python graph_train.py --exp=identity_experiment --train --num_steps=10 --dataset=identity --train --cuda --infinite
We may substitute the flag --dataset with other datasets such as shortestpath or connected (as well as additional ones defined in graph_dataset.py).
To evaluate the model, you may then utilize the following command:
python graph_train.py --exp=identity_experiment --num_steps=10 --dataset=identity --cuda --infinite --resume_iter=10000
Please cite our paper as:
@article{du2022irem,
title={Learning Iterative Reasoning through Energy Minimization},
author={Yilun Du and Shuang Li and Joshua B. Tenenbaum and Igor Mordatch},
booktitle={Proceedings of the 39th International Conference on Machine
Learning (ICML-22)},
year={2022}
}
Note: this is not an official Google product.
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