Abstract: A composite vector is received. A first candidate component vector is generated and evaluated. The first candidate component vector is selected, based on the evaluating, as an accurate component vector. The first candidate component vector is unbundled from the composite vector. The unbundling results in a first reduced vector.

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.

Abstract: A computerized neuro-vector-symbolic architecture, that: receives image data associated with an artificial intelligence (AI) task; processes the image data using a frontend that comprises an artificial neural network (ANN) and a vector-symbolic architecture (VSA); and processes an output of the frontend using a backend that comprises a symbolic logical reasoning engine, to solve the AI task. The AI task, for example, may be an abstract visual reasoning task.

Abstract: Embodiments are disclosed for a method. The method includes determining a granularity of hypervectors. The method also includes receiving an input hypervector representing a data structure. Additionally, the method includes performing an iterative process to factorize the input hypervector into individual hypervectors representing the cognitive concepts. The iterative process includes, for each concept: determining an unbound version of a hypervector representing the concept by a blockwise unbinding operation between the input hypervector and estimate hypervectors of other concepts. The iterative process further includes determining a similarity vector indicating a similarity of the unbound version of the hypervector with each candidate code hypervector of the concept. Additionally, the iterative process includes generating an estimate of a hypervector representing the concept by a linear combination of the candidate code hypervectors, and weights of the similarity vector.

Abstract: Embodiments are disclosed for a method. The method includes bundling a set of M code hypervectors, each of dimension D, where M>1. The bundling includes receiving an M-dimensional vector comprising weights for weighting the set of code hypervectors. The bundling further includes mapping the M-dimensional vector to an S-dimensional vector, sk, such that each element of the S-dimensional vector, sk, indicates one of the set of code hypervectors, where S=D/L and L?1. Additionally, the bundling includes building a hypervector such that an ith element of the built hypervector is an ith element of the code hypervector indicated in an ith element of the S-dimensional vector, sk.

Abstract: Predefined concepts are represented by codebooks. Each codebook includes candidate code hypervectors that represent items of a respective concept of the predefined concepts. A neuromorphic memory device with a crossbar array structure includes row lines and column lines stores a value of respective code hypervectors of an codebook. An input hypervector is stored in an input buffer. A plurality of estimate buffers are each associated with a different subset of row lines and a different codebook and initially store estimated hypervectors. An unbound hypervector is computed using the input hypervector and all the estimated hypervectors. An attention vector is computed that indicates a similarity of the unbound hypervector with one estimated hypervector. A linear combination of the one estimated hypervector, weighted by the attention vector, is computed and is stored in the estimate buffer that is associated with the one estimated hypervector.

Abstract: A computer-implemented method for performing a classification of an input signal by a neural network includes: computing, by a feature extraction unit of the neural network, a D-dimensional query vector, wherein D is an integer; generating, by a classification unit of the neural network, a set of C fixed D-dimensional quasi-orthogonal bipolar vectors as a fixed classification matrix, wherein C is an integer corresponding to a number of classes of the classification unit; and performing a classification of a query vector based, at least in part, on the fixed classification matrix.

Abstract: A computer-implemented method for performing a classification of an input signal utilizing a neural network includes: computing, by a feature extraction unit of the neural network, a query vector; and performing, by a classification unit, a factorization of the query vector to a plurality of codebook vectors of a plurality of codebooks to determine a corresponding class of a number of classes. A set of combinations of vector products of the plurality of codebook vectors of the plurality of codebooks establishes a number of classes of the classification unit.

Abstract: A computer-implemented method for factorizing hypervectors in a resonator network includes: receiving an input hypervector representing a data structure; performing an iterative process for each concept in a set of concepts associated with the data structure in order to factorize the input hypervector into a plurality of individual hypervectors representing the set of concepts, wherein the iterative process includes: generating a first estimate of an individual hypervector representing a concept in the set of concepts; generating a similarity vector indicating a similarity of the estimate of the individual hypervector with each candidate attribute hypervector of a plurality of candidate attribute hypervectors representing an attribute associated with the concept; and generating a second estimate of the individual hypervector based, at least in part, on a linear combination of the plurality of candidate attribute hypervectors and performing a non-linear function on the linear combination of the plurality of ca