Methods › Graphs › Graph Models › IPA-GNN
Instruction Pointer Attention Graph Neural Network
IPA-GNN
Introduced by David Bieber et al. in Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Instruction Pointer Attention Graph Neural Network, or IPA-GNN, is a learning-interpreter neural network (LNN) based on GNNs for learning to execute programmes. It achieves improved systematic generalization on the task of learning to execute programs using control flow graphs. The model arises by considering RNNs operating on program traces with branch decisions as latent variables. The IPA-GNN can be seen either as a continuous relaxation of the RNN model or as a GNN variant more tailored to execution.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks 23 Oct 2020 · 1 repository · arXiv:2010.12621
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Code Completion | 1 |
| Learning to Execute | 1 |
| Program Repair | 1 |
| Program Synthesis | 1 |
| Systematic Generalization | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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