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A Study of Retrieval Algorithms of Sparse Messages in Networks of Neural Cliques

Authors:
Ala Aboudib
Vincent Gripon
Xiaoran Jiang

Keywords: associative memory; sparse coding; parsimony; iterative retrieval; threshold control

Abstract:
Associative memories are data structures addressed using part of the content rather than an index. They offer good fault reliability and biological plausibility. Among different families of associative memories, sparse ones are known to offer the best efficiency (ratio of the amount of bits stored to that of bits used by the network itself). Their retrieval process performance has been shown to benefit from the use of iterations. In this paper, we introduce several rules to enhance the performance of the retrieval process in recently proposed sparse associative memories based on binary neural networks. We show that these rules provide better performance than existing techniques. We also analyze the required number of iterations and derive corresponding curves.

Pages: 140 to 146

Copyright: Copyright (c) IARIA, 2014

Publication date: May 25, 2014

Published in: conference

ISSN: 2308-4197

ISBN: 978-1-61208-340-7

Location: Venice, Italy

Dates: from May 25, 2014 to May 29, 2014