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On Neural Associative Memory Structures: Storage and Retrieval of Sequences in a Chain of Tournaments

Abolpour Mofrad, Asieh; Abolpour Mofrad, Samaneh; Yazidi, Anis; Parker, Matthew Geoffrey
Journal article, Peer reviewed
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https://hdl.handle.net/11250/2992618
Utgivelsesdato
2021
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  • Department of Informatics [740]
  • Registrations from Cristin [5237]
Originalversjon
Neural Computation. 2021, 33 (9), 2550-2577.   10.1162/neco_a_01417
Sammendrag
Associative memories enjoy many interesting properties in terms of error correction capabilities, robustness to noise, storage capacity, and retrieval performance, and their usage spans over a large set of applications. In this letter, we investigate and extend tournament-based neural networks, originally proposed by Jiang, Gripon, Berrou, and Rabbat (2016), a novel sequence storage associative memory architecture with high memory efficiency and accurate sequence retrieval. We propose a more general method for learning the sequences, which we call feedback tournament-based neural networks. The retrieval process is also extended to both directions: forward and backward—in other words, any large-enough segment of a sequence can produce the whole sequence. Furthermore, two retrieval algorithms, cache-winner and explore-winner, are introduced to increase the retrieval performance. Through simulation results, we shed light on the strengths and weaknesses of each algorithm.
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MIT Press
Tidsskrift
Neural Computation
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Copyright 2021 Massachusetts Institute of Technology

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