Abstract: An approach for bundling a set of hypervectors may be provided herein. The approach may involve encoding a data structure into a plurality of hypervectors. The approach may further involve calculating the element-wise sum of a set of hypervectors to generate a sum hypervector. A plurality of blocks may be produced from the sum hypervectors. The block elements of the sum hypervector may be selected based on a selection criterion. A selection criterion may include a threshold value or simply be the largest element per block. Additionally, the approach may involve setting the non-selected elements of the sum hypervector to zero.

Abstract: An approach for factorizing hypervectors using a resonator network may be provided herein. The approach may involve providing alternative implementations of a step for each step of the iterative process of the resonator network. An input hypervector representing a data structure may be received, by a resonator network. The approach may further involve selecting a step from the provided implementation of each step of the iterative process. The iterative process may be executed based on the selected implementations, thereby factorizing the input hypervector.

Abstract: The disclosure includes a computer-implemented method of factorizing a vector by utilizing resonator network modules. Such modules include an unbinding module, as well as search-in-superposition modules. The method includes the following steps. A product vector is fed to the unbinding module to obtain unbound vectors. The latter represent estimates of codevectors of the product vector. A first operation is performed on the unbound vectors to obtain quasi-orthogonal vectors. The first operation is reversible. The quasi-orthogonal vectors are fed to the search-in-superposition modules, which rely on a single codebook. In this way, transformed vectors are obtained, utilizing a single codebook. A second operation is performed on the transformed vectors. The second operation is an inverse operation of the first operation, which makes it possible to obtain refined estimates of the codevectors.