• DocumentCode
    352971
  • Title

    Neural processing of complex continual input streams

  • Author

    Gers, Felix A. ; Schmidhuber, Jurgen

  • Author_Institution
    IDSIA, Lugano, Switzerland
  • Volume
    4
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    557
  • Abstract
    Long short-term memory (LSTM) can learn algorithms for temporal pattern processing not learnable by alternative recurrent neural networks or other methods such as hidden Markov models and symbolic grammar learning. Here, we present tasks involving arithmetic operations on continual input streams that even LSTM cannot solve. However, an LSTM variant based on “forget gates,” has superior arithmetic capabilities and does solve the tasks
  • Keywords
    content-addressable storage; learning (artificial intelligence); recurrent neural nets; complex continual input streams; forget gates; learning; long short-term memory; recurrent neural networks; Arithmetic; Error correction; Genetic programming; Hidden Markov models; Learning systems; Protection; Recurrent neural networks; State-space methods;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
  • Conference_Location
    Como
  • ISSN
    1098-7576
  • Print_ISBN
    0-7695-0619-4
  • Type

    conf

  • DOI
    10.1109/IJCNN.2000.860830
  • Filename
    860830
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=352971