Abstract: Techniques in advanced deep learning provide improvements in one or more of accuracy, performance, energy efficiency, and cost. In a first embodiment, a scaled array of processing elements is implementable with varying dimensions of the processing elements to enable varying price/performance systems. In a second embodiment, an array of clusters communicates via high-speed serial channels. The array and the channels are implemented on a Printed Circuit Board (PCB). Each cluster comprises respective processing and memory elements. Each cluster is implemented via a plurality of 3D-stacked dice, 2.5D-stacked dice, or both in a Ball Grid Array (BGA). A processing portion of the cluster is implemented via one or more Processing Element (PE) dice of the stacked dice. A memory portion of the cluster is implemented via one or more High Bandwidth Memory (HBM) dice of the stacked dice.

Abstract: A computing device selects a trained spatial regression model. A spatial weights matrix defined for observation vectors is selected, where each element of the spatial weights matrix indicates an amount of influence between respective pairs of observation vectors. Each observation vector is spatially referenced. A spatial regression model is selected from spatial regression models, initialized, and trained using the observation vectors and the spatial weights matrix to fit a response variable using regressor variables. Each observation vector includes a response value for the response variable and a regressor value for each regressor variable of the regressor variables. A fit criterion value is computed for the spatial regression model and the spatial regression model selection, initialization, and training are repeated until each spatial regression model is selected. A best spatial regression model is selected and output as the spatial regression model having an extremum value of the fit criterion value.

Abstract: An automatic system and method for the performance of scientific inferencing including the determination of a null hypothesis significance testing on an interactive computer system, the method including the steps of: (a) providing for the input of an input description of a proposed hypothesis test, the input description including a number of relevant input parameters; (b) utilising the computational system for processing the input description into a null hypothesis significance test; (c) executing the null hypothesis significance test on the computational system; and (d) visually displaying the results of the execution.

Abstract: In accordance with various embodiments of the disclosed subject matter, a method and framework configured for modeling a pattern of life (POL) by processing both categorical data and non-categorical data (e.g., numeric, spatial etc.), conducting pattern of life estimation (POLE), and detecting anomalous data in a multi-dimensional data set in a substantially simultaneous manner by comparing statistical PoL results.

Abstract: Systems and methods for natural language processing and classification are provided. In some embodiments, the systems and methods include a communication editor dashboard which receives the message, performs natural language processing to divide the message into component parts. The system displays the message in a first pane with each of the component parts overlaid with a different color, and displaying in a second pane the insights, the confidence scores associated with each insight, the sentiment and the actions. In another embodiment, the systems and methods include combining outputs from multiple machine learned AI models into a unified output. In another embodiment, the systems and methods include responding to simple question using natural language processing.

Abstract: A neural network device may generate an input feature list based on an input feature map, where the input feature list includes an input feature index and an input feature value, generating an output feature index based on the input feature index corresponding to an input feature included in the input feature list and a weight index corresponding to a weight included in a weight list, and generating an output feature value corresponding to the output feature index based on the input feature value corresponding to the input feature and a weight value corresponding to the weight.

Abstract: A processing device includes a processor core and a number of calculation modules that each is configurable to perform any one of operations for a convolutional neuron network system. A first set of the calculation modules are configured to perform convolution operations, a second set of the calculation modules are reconfigured to perform averaging operations, and a third set of the calculation modules are reconfigured to perform dot product operations.

Abstract: The invention shows how to use noise-like perturbations to improve the speed and accuracy of Markov Chain Monte Carlo (MCMC) estimates and large-scale optimization, simulated annealing optimization, and quantum annealing for large-scale optimization.

Abstract: Methods, systems, and apparatus including a special purpose hardware chip for training neural networks are described. The special-purpose hardware chip may include a scalar processor configured to control computational operation of the special-purpose hardware chip. The chip may also include a vector processor configured to have a 2-dimensional array of vector processing units which all execute the same instruction in a single instruction, multiple-data manner and communicate with each other through load and store instructions of the vector processor. The chip may additionally include a matrix multiply unit that is coupled to the vector processor configured to multiply at least one two-dimensional matrix with a second one-dimensional vector or two-dimensional matrix in order to obtain a multiplication result.

Abstract: Embodiments of the present specification provide a method and an apparatus for clustering privacy data of a plurality of parties.

Abstract: In an example, for each feature of one or more features of a target sample data, feature values for one or more pseudo-samples are generated using, localized stratified sampling. The one or more pseudo-samples are fed into the trained machine learned model to obtain their prediction values. A piecewise linear regression model is trained using the one or more pseudo-samples and their prediction values, the piecewise linear regression model having two coefficients for each feature, a first coefficient describing prediction change when a corresponding feature value is increased and a second coefficient describing prediction change when a corresponding feature value is decreased. A top positive feature influencer is identified based on a feature of the one or more features of the target sample having a greatest magnitude of positive first coefficient or greatest magnitude of negative second coefficient.

Abstract: A computer-implemented method according to one embodiment includes identifying a textual document, determining chemical data within the textual document, performing an analysis of the chemical data to identify a chemical pathway, and calculating a probability score for the chemical pathway.

Abstract: A processor, that may include at least one neural network that comprises at least one leaky spiking neuron; wherein the at least one leaky spiking neuron is configured to directly receive an input pulse density modulation (PDM) signal from a sensor; wherein the input PDM signal represents a detected signal that was detected by the sensor; and wherein the at least one neural network is configured to process the input PDM signal to provide an indication about the detected input signal.

Abstract: An autonomous vehicle includes one or more sensors that measures the distance to and/or other properties of a target by illuminating the target with light. The sensor(s) provides sensor data indicative of one or more surface manifolds. Point data is generated with respect to the surface manifold(s). AHaH (Anti-Hebbian and Hebbian) based mechanism performs an AHaH-based feature extraction operation on the point data for compression and processing thereof.

Abstract: A non-transitory computer readable storage media having computer-executable instructions, when executed by a processor, performs a method for evaluating a reach of a social media influencer. The methods provides for receiving a plurality of influencers at a server, wherein a data set is associated with each of the plurality of influencers; parsing the data set into quantitative data readable by a machine learning algorithm at the server; receiving, inputting, or both, a type of product or service at the server; classifying the type of product or service into at least one class of goods or services; training a node using the machine learning algorithm using the date set an input; and executing the machine learning algorithm to determine a score of each influencer for each class of goods or services. Systems for evaluating the reach of a social media influencer as it relates to advertisers and content is also disclosed herein.

Abstract: A method and system of analyzing a symbolic sequence is provided. Metadata of a symbolic sequence is received from a computing device of an owner. A set of R random sequences are generated based on the received metadata and sent to the computing device of the owner of the symbolic sequence for computation of a feature matrix based on the set of R random sequences and the symbolic sequence. The feature matrix is received from the computing device of the owner. Upon determining that an inner product of the feature matrix is below a threshold accuracy, the iterative process returns to generating R random sequences. Upon determining that the inner product of the feature matrix is at or above the threshold accuracy, the feature matrix is categorized based on machine learning. The categorized global feature matrix is sent to be displayed on a user interface of the computing device of the owner.

Abstract: An implementation of the disclosure provides an apparatus comprising a memory to store classification data of a machine learning model; and a processing device, operatively coupled to the memory, to detect characteristics of an object. The characteristics comprise at least a spatial position of the object. Sensor information is received from a node device. The sensor information is indicative of at least one of: a spatial position, a speed or a direction of movement of the node device. Using the sensor information, it is determined whether the node device is within a threshold with respect to the object in accordance with the detected characteristics. Responsive to determining that the node device is within the threshold, a notification related to at least part of the classification data and the one or more characteristics is provided to the node device. The classification data is representative of a classification of the object.

Abstract: Non-mechanistic, differential-equation-free approaches for predicting a particular structure-activity responses of a system to a given molecular structure input are provided in the form of systems, methods, and devices. These approaches are generally directed to a non-compartmental method of predicting a time-dependent structure-activity response of a component of a system to an input into the system.

Abstract: Devices and methods for modeling streaming data are disclosed. A method includes: receiving, by a computing device, a local graph model; determining, by the computing device, a subgraph in the local graph model; acquiring, by the computing device, an external graph model; determining, by the computing device, a plurality of alternative subgraphs in the external graph model; determining, by the computing device, a score for each of the plurality of alternative subgraphs; selecting, by the computing device, an alternative subgraph having a highest score among the plurality of alternative subgraphs; and ensembling, by the computing device, the local graph model and the alternative subgraph having the highest score.

Abstract: Embodiments of the present disclosure provide a method and a device for predicting a box office trend of a film, a device and a storage medium. The method includes acquiring in real time a plurality of dynamic factor data of each of various films to be shown, in which, the dynamic factor data represents a factor that influences box office of the film; after a film in the various films is shown, incrementally updating a pre-trained box office prediction model by using box office data and the plurality of dynamic factor data of the film; and according to a preset period, predicting a box office trend of a target film to be predicted in the various films by using a box office prediction model incrementally updated in each preset period and the plurality of dynamic factor data of the target film, to obtain a plurality of prediction results.