Neural Algorithmic Reasoning for Transformers: The TransNAR Framework

Graph neural networks (GNNs), referred to as neural algorithmic reasoners (NARs), have shown effectiveness in robustly solving algorithmic tasks of varying input sizes, both in and out of distribution. However, NARs are still relatively narrow forms of AI as they require rigidly structured input formatting and cannot be directly applied to problems posed in noisy […]

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