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arXiv AI By Dmitri Rachkovskij, Evgeny Osipov, Olexander Volkov, Denis Kleyko, Vaclav Snasel

Derandomizing Dense Binary Hypervector Codebooks for Quantized Scalars

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The paper introduces a transition-based derandomization framework for dense binary hypervector codebooks used in hyperdimensional computing. It targets two similarity families—exponential and linear decay with scalar separation—and separates the similarity law, derandomization variant, and generator construction. The authors formalize variants that constrain initial Hamming weight, update-count variability, and update balance, deriving exact finite-dimensional expressions for bias, variance, and RMS error, and validate the theory with simulations to guide practical codebook design.

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