• DocumentCode
    2955488
  • Title

    Nonlinear ICA through low-complexity autoencoders

  • Author

    Hochreiter, Sepp ; Schmidhuber, Jürgen

  • Author_Institution
    Fakultat fur Inf., Tech. Univ. Munchen, Germany
  • Volume
    5
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    53
  • Abstract
    We train autoencoders by flat minimum search (FMS), a regularizer algorithm for finding low-complexity networks describable by few bits of information. As a by-product, this encourages nonlinear independent component analysis (ICA) and sparse codes of the input data
  • Keywords
    computational complexity; neural nets; principal component analysis; sparse matrices; flat minimum search; independent component analysis; low-complexity autoencoders; low-complexity networks; nonlinear ICA; regularizer algorithm; sparse codes; Decoding; Equations; Flexible manufacturing systems; Independent component analysis; Principal component analysis; Source separation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 1999. ISCAS '99. Proceedings of the 1999 IEEE International Symposium on
  • Conference_Location
    Orlando, FL
  • Print_ISBN
    0-7803-5471-0
  • Type

    conf

  • DOI
    10.1109/ISCAS.1999.777509
  • Filename
    777509
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2955488