On Neural Associative Memory Structures: Storage and Retrieval of Sequences in a Chain of Tournaments
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2021Metadata
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Mofrad, A. A., Mofrad, S. A., Yazidi, A., & Parker, M. G. (2021). On neural associative memory structures: Storage and retrieval of sequences in a chain of tournaments. Neural Computation, 33(9), 2550-2577. 10.1162/neco_a_01417Abstract
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.