Learning task-dependent distributed representations by backpropagation through structure

@article{Goller1996LearningTD,
  title={Learning task-dependent distributed representations by backpropagation through structure},
  author={Christoph Goller and Andreas K{\"u}chler},
  journal={Proceedings of International Conference on Neural Networks (ICNN'96)},
  year={1996},
  volume={1},
  pages={347-352 vol.1}
}
  • C. GollerA. Küchler
  • Published 3 June 1996
  • Computer Science
  • Proceedings of International Conference on Neural Networks (ICNN'96)
While neural networks are very successfully applied to the processing of fixed-length vectors and variable-length sequences, the current state of the art does not allow the efficient processing of structured objects of arbitrary shape (like logical terms, trees or graphs. [] Key Method The major difference of our approach compared to others is that the structure-representations are exclusively tuned for the intended inference task.

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