Abstract: Method and apparatus for deep learning. A first input and a second input are accessed. A first embedding for the first input is generated using a binding network. A second embedding for the second input is generated using the binding network. The first and second embeddings are aggregated to generate a combined embedding. A transformation function is applied to the combined embedding to generate a transformed combined embedding. The transformed combined embedding is processed, using an unbinding network, to extract a first transformed embedding for the first input and a second transformed embedding for the second input. An inference function is applied to the first transformed embedding to generate a first output. The inference function is applied to the second transformed embedding to generate a second output.

Abstract: An embodiment establishes a neural network that comprises a plurality of layers. The embodiment receives a plurality of input data sequences into a layer of the neural network, the plurality of input data sequences comprises a first input data sequence and a second input data sequence. The embodiment superposes the first input data sequence and the second input data sequence, thereby creating a superposed embedding. The embodiment transforms the superposed embedding by applying a function to the superposed embedding, thereby creating a transformed superposed embedding. The embodiment infers a first output data element corresponding to the first input data sequence and a second output data element corresponding to the second input data sequence via application of an unbinding operation on the transformed superposed embedding.

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: 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: The present disclosure relates to training a classifier. The classifier includes a controller and an explicit memory. The training may include iteratively receiving one or more second training datasets, each comprising second data samples of a set of one or more associated novel classes, adding to the explicit memory one or more second output vectors indicative of the set of one or more associated novel classes, in response to providing the one or more second training datasets to the classifier, retraining the classifier using the one or more second training datasets and the first training dataset by minimizing a distance between the one or more second output vectors and the one or more prototype vectors, determining a set of updated prototype vectors indicative of first training dataset and the one or more second training datasets, and updating the explicit memory with the set of updated prototype vectors.

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 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 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 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

Abstract: Management systems, methods and mediums are provided for displaying graphics using a programmable symbol animation pre-processor. One method includes identifying a symbol associated with a building graphic, identifying a symbol property to be animated, and determining whether the symbol property is associated with a script. When it is, identifying a plurality of different data points referenced in the script where each data point corresponds to the same device or a respective device in the building. The method identifies a respective value for each identified data point as received from a management system operably connected to each of the plurality of devices, identifies an operation in the script that corresponds to an evaluation of the values of the identified data points, generates a first evaluation result based on the operation, and displays a graphical representation of the symbol based on the first evaluation result and in association with the building graphic.

Abstract: Management systems, methods and mediums are provided. A method includes displaying a building layout graphic, receiving a request to generate a coverage area graphic associated with a monitoring device, generating the coverage area graphic, and displaying the coverage area graphic relative to the building layout graphic. The method also identifies a device having a data point to be monitored by the data processing system, and determines whether the identified device is in the coverage area represented by the coverage area graphic. In response to determining that the identified device is in the coverage area, the method further includes storing a related item identifier associated with the one or more monitoring devices in association with an object corresponding to the identified device. The method receives a selection for the identified device and, in response, displays one or more related item identifiers associated with the one or more monitoring devices.

Abstract: Management systems, methods and mediums are provided for a building automation graphical interface command configuration. One method includes receiving a selection of a graphical element. The method includes receiving a selection of a command control type. The method includes associating the command control type with the graphical element to produce a command control. The method includes receiving at least one property for the command control. At least one property includes at least a parameter name that identifies a target of the command control. The method includes storing the command control. The method includes in response to a user selection or modification of the command control, processing a command to modify the target of the command control.

Abstract: Management systems, methods and mediums are provided for displaying graphics using a programmable symbol animation pre-processor. One method includes identifying a symbol associated with a building graphic, identifying a symbol property to be animated, and determining whether the symbol property is associated with a script. When it is, identifying a plurality of different data points referenced in the script where each data point corresponds to the same device or a respective device in the building. The method identifies a respective value for each identified data point as received from a management system operably connected to each of the plurality of devices, identifies an operation in the script that corresponds to an evaluation of the values of the identified data points, generates a first evaluation result based on the operation, and displays a graphical representation of the symbol based on the first evaluation result and in association with the building graphic.

Abstract: Management systems, methods and mediums are provided. A method includes displaying a building layout graphic, receiving a request to generate a coverage area graphic associated with a monitoring device, generating the coverage area graphic, and displaying the coverage area graphic relative to the building layout graphic. The method also identifies a device having a data point to be monitored by the data processing system, and determines whether the identified device is in the coverage area represented by the coverage area graphic. In response to determining that the identified device is in the coverage area, the method further includes storing a related item identifier associated with the one or more monitoring devices in association with an object corresponding to the identified device. The method receives a selection for the identified device and, in response, displays one or more related item identifiers associated with the one or more monitoring devices.

Abstract: Automation systems, methods, and mediums. A method includes identifying a value for a data point associated with a device in a building. The value is received from a management system operably connected to the device. The method includes mapping the value for the data point to a graphical representation of the value for the data point. The method includes generating a display comprising a graphic for the building and a symbol representing the device. The method includes displaying the graphical representation of the value for the data point in association with the symbol representing the device. Additionally, the method includes modifying the graphical representation of the value based on a change in the value in response to identifying the change in the value from the management system.