Corpus ID: 220424574

Learning Graph Structure With A Finite-State Automaton Layer

@article{Johnson2020LearningGS,
  title={Learning Graph Structure With A Finite-State Automaton Layer},
  author={D. Johnson and H. Larochelle and Daniel Tarlow},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.04929}
}
Graph-based neural network models are producing strong results in a number of domains, in part because graphs provide flexibility to encode domain knowledge in the form of relational structure (edges) between nodes in the graph. In practice, edges are used both to represent intrinsic structure (e.g., abstract syntax trees of programs) and more abstract relations that aid reasoning for a downstream task (e.g., results of relevant program analyses). In this work, we study the problem of learning… Expand

References

SHOWING 1-10 OF 50 REFERENCES
Gated Graph Sequence Neural Networks
Graph Transformer Networks
Learning to Represent Programs with Graphs
The Graph Neural Network Model
Learning Transferable Graph Exploration
Global Relational Models of Source Code
End to end learning and optimization on graphs
Neural Relational Inference for Interacting Systems
Graph Recurrent Networks With Attributed Random Walks
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