Gated Graph Neural Networks and graph-sequence outputs

What is recorded here?

Replaces contraction-constrained graph recurrence with GRU-style finite unrolling and backpropagation, and carries node annotations across graph-based output steps.

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Scientific scope

The method uses a finite sequence of recurrent graph updates, rather than requiring convergence to a fixed point. More unrolled steps require more memory; successful benchmark predictions do not prove arbitrary program correctness.

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Gated Graph Sequence Neural Networks

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People and systems

Yujia Li; Richard Zemel; Marc Brockschmidt; Daniel Tarlow

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English catalogue narrative