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arXiv · 2402.06445

The Deep Equilibrium Algorithmic Reasoner

Dobrik Georgiev·Pietro Liò·Davide Buffelli
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Abstract

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.

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Dobrik Georgiev, Pietro Liò, Davide Buffelli. 2024-02-09. The Deep Equilibrium Algorithmic Reasoner. https://arxiv.org/abs/2402.06445

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