The Deep Equilibrium Algorithmic Reasoner
<|reference_start|>The Deep Equilibrium Algorithmic Reasoner: Recent work on neural algorithmic reasoning has demonstrated that graph neural networks (GNNs) could learn to execute classical algorithms. Doing so, however, has always used a recurrent architecture, where each iteration of the GNN aligns with an algorithm's iteration. Since an algorithm's solution is often an equilibrium, we conjecture and empirically validate that one can train a network to solve algorithmic problems by directly finding the equilibrium. Note that this does not require matching each GNN iteration with a step of the algorithm.<|reference_end|>
@article{georgiev2024the,
    title={The Deep Equilibrium Algorithmic Reasoner},
    author={Dobrik Georgiev, Pietro Li`o, Davide Buffelli},
    journal={arXiv preprint arXiv:2402.06445},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06445},
    primaryClass={cs.LG}
}