• DocumentCode
    2495744
  • Title

    Augmented Efficient BackProp for backpropagation learning in deep autoassociative neural networks

  • Author

    Embrechts, Mark J. ; Hargis, Blake J. ; Linton, Jonathan D.

  • Author_Institution
    Dept. of Ind. & Syst. Eng., Rensselaer Polytech. Inst., Troy, NY, USA
  • fYear
    2010
  • fDate
    18-23 July 2010
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    We introduce Augmented Efficient BackProp as a strategy for applying the backpropagation algorithm to deep autoencoders, i.e., autoassociators with many hidden layers, without relying on a weight initialization using restricted Boltzmann machines. This training method is an extension of Efficient BackProp, first proposed by LeCun et al. [1], and is benchmarked on three different types of application datasets.
  • Keywords
    backpropagation; content-addressable storage; learning (artificial intelligence); neural nets; augmented efficient backprop; backpropagation learning; deep autoassociative neural networks; deep autoencoders; restricted Boltzmann machines; Artificial neural networks; Backpropagation algorithms; Measurement; Neurons; Petroleum; Principal component analysis; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2010 International Joint Conference on
  • Conference_Location
    Barcelona
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-6916-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.2010.5596828
  • Filename
    5596828
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2495744