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  • 1.
    Kleyko, Denis
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. University of California, USA.
    Bybee, Connor
    University of California, USA.
    Huang, P-C
    University of California, USA.
    Kymn, Christopher
    University of California, USA.
    Olshausen, Bruno
    University of California, USA.
    Frady, E Paxon
    Intel Labs, USA.
    Sommer, Friedrich
    University of California, USA; Intel Labs, USA.
    Efficient Decoding of Compositional Structure in Holistic Representations2023In: Neural Computation, ISSN 0899-7667, E-ISSN 1530-888X, Vol. 35, no 7, p. 1159-1186Article in journal (Refereed)
    Abstract [en]

    We investigate the task of retrieving information from compositional distributed representations formed by hyperdimensional computing/vector symbolic architectures and present novel techniques that achieve new information rate bounds. First, we provide an overview of the decoding techniques that can be used to approach the retrieval task. The techniques are categorized into four groups. We then evaluate the considered techniques in several settings that involve, for example, inclusion of external noise and storage elements with reduced precision. In particular, we find that the decoding techniques from the sparse coding and compressed sensing literature (rarely used for hyperdimensional computing/vector symbolic architectures) are also well suited for decoding information from the compositional distributed representations. Combining these decoding techniques with interference cancellation ideas from communications improves previously reported bounds (Hersche et al., 2021) of the information rate of the distributed representations from 1.20 to 1.40 bits per dimension for smaller codebooks and from 0.60 to 1.26 bits per dimension for larger codebooks. 

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