GHR uses hierarchical recurrence on pooled graph abstractions to improve long-range dependency capture and out-of-range generalization while using far fewer parameters than existing models.
Neural execution of graph algorithms
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Graph Hierarchical Recurrence for Long-Range Generalization
GHR uses hierarchical recurrence on pooled graph abstractions to improve long-range dependency capture and out-of-range generalization while using far fewer parameters than existing models.