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Tuesday, August 4, 2020 | History

2 edition of Modelling space and networks found in the catalog.

Modelling space and networks

Colloquium of Theoretical and Quantitative Geography (7th 1991 Stockholm)

Modelling space and networks

progress in theoretical and quantitative geography : proceedings from the 7th Colloquium of Theoretical and Quantitative Geography, Stockholm, September 1991

by Colloquium of Theoretical and Quantitative Geography (7th 1991 Stockholm)

  • 142 Want to read
  • 25 Currently reading

Published by Umeå universitet in Umeå .
Written in English

    Subjects:
  • Human geography -- Methodology -- Congresses.,
  • Human geography -- Network analysis -- Congresses.,
  • Human geography -- Statistical methods -- Congresses.,
  • Spatial behavior -- Congresses.

  • Edition Notes

    Includes bibliographical references.

    StatementEinar Holm (ed.)
    GenreCongresses.
    SeriesGERUM Kulturgeografi -- 1997:1
    ContributionsHolm, Einar.
    The Physical Object
    Paginationv, 342 p. :
    Number of Pages342
    ID Numbers
    Open LibraryOL19320180M
    ISBN 109171912886

    In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics. They have applications in image and video recognition. TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 LSTM: A Search Space Odyssey Klaus Greff, Rupesh K. Srivastava, Jan Koutn´ık, Bas R. Steunebrink, J urgen Schmidhuber¨ Abstract—Several variants of the Long Short-Term Memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in In recent Cited by:

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