Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks

@article{Schmidhuber1992LearningTC,
  title={Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks},
  author={J{\"u}rgen Schmidhuber},
  journal={Neural Computation},
  year={1992},
  volume={4},
  pages={131-139}
}
  • J. Schmidhuber
  • Published 3 January 1992
  • Computer Science
  • Neural Computation
Previous algorithms for supervised sequence learning are based on dynamic recurrent networks. This paper describes an alternative class of gradient-based systems consisting of two feedforward nets that learn to deal with temporal sequences using fast weights: The first net learns to produce context-dependent weight changes for the second net whose weights may vary very quickly. The method offers the potential for STM storage efficiency: A single weight (instead of a full-fledged unit) may be… 

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