Corpus ID: 59158826

Typed Graph Networks

@article{Avelar2019TypedGN,
  title={Typed Graph Networks},
  author={Pedro H. C. Avelar and H. Lemos and Marcelo O. R. Prates and M. Gori and L. Lamb},
  journal={ArXiv},
  year={2019},
  volume={abs/1901.07984}
}
  • Pedro H. C. Avelar, H. Lemos, +2 authors L. Lamb
  • Published 2019
  • Mathematics, Computer Science
  • ArXiv
  • Recently, the deep learning community has given growing attention to neural architectures engineered to learn problems in relational domains. Convolutional Neural Networks employ parameter sharing over the image domain, tying the weights of neural connections on a grid topology and thus enforcing the learning of a number of convolutional kernels. By instantiating trainable neural modules and assembling them in varied configurations (apart from grids), one can enforce parameter sharing over… CONTINUE READING
    3 Citations
    Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems
    • 15
    • PDF
    Learning to solve NP-complete problems
    • 5
    • PDF
    Exploiting Contextual Information with Deep Neural Networks

    References

    SHOWING 1-10 OF 26 REFERENCES
    Multitask Learning on Graph Neural Networks - Learning Multiple Graph Centrality Measures with a Unified Network
    • 2
    • PDF
    Gated Graph Sequence Neural Networks
    • 1,156
    • PDF
    Relational inductive biases, deep learning, and graph networks
    • 806
    • Highly Influential
    • PDF
    Recurrent Relational Networks
    • 55
    • PDF
    The Graph Neural Network Model
    • 1,587
    • PDF
    Recurrent Relational Networks for Complex Relational Reasoning
    • 24
    A new model for learning in graph domains
    • 491
    A simple neural network module for relational reasoning
    • 822
    • PDF
    Neural Message Passing for Quantum Chemistry
    • 1,469
    • PDF
    Learning to Solve NP-Complete Problems - A Graph Neural Network for the Decision TSP
    • 39
    • PDF