Graph Models
Instruction Pointer Attention Graph Neural Network
Introduced by Bieber et al. in Learning to Execute Programs with Instruction Pointer Attention Graph Neural NetworksInstruction 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.
Source: Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Learning to Execute | 1 | 25.00% |
| Program Repair | 1 | 25.00% |
| Program Synthesis | 1 | 25.00% |
| Systematic Generalization | 1 | 25.00% |
Usage Over Time
Components
| Component | Type |
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|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
