Abstract: A platform that integrates and collates the data points from students, employers, schools, and industry into an ecosystem which allows for customers (students, employers, schools, and industry) to model ‘what-if’ scenarios based on their industry parameters. By using a design algorithm based on automated reasoning, game theory, and knowledge mining, within a neural network, the platform can predict, model, and build the journey. The decision modeling neural learning platform may be used to augment or replace the need for guidance counselors in schools, along with assisting industry and immigration liaisons.

Abstract: According to an embodiment, an information processing apparatus includes a computing unit and a compressing unit. The computing unit is configured to execute computation of an input layer, a hidden layer, and an output layer of a neural network. The compressing unit is configured to irreversibly compress output data of at least a part of the input layer, the hidden layer, and the output layer and output the compressed data.

Abstract: Provided is a spike neural network circuit. The spike neural network circuit includes an axon configured to generate an input spike signal, a synapse including a first transistor for outputting a current according to a weight and a second transistor connected to the first transistor and outputting the current according to an input spike signal, a neuron configured to compare a value according to the current output from the synapse with a reference value and generate an output spike signal based on a comparison result, and a radiation source attached to a substrate on which the synapse is formed, configured to output radiation particles to the synapse, and configured to increase magnitudes of threshold voltages of the first and second transistors of the synapse.

Abstract: A system including: at least one processor; and at least one memory having stored thereon computer program code that, when executed by the at least one processor, controls the system to: receive a data model identification and a dataset; in response to determining that the data model does not contain a hierarchical structure, perform expectation propagation on the dataset to approximate the data model with a hierarchical structure; divide the dataset into a plurality of channels; for each of the plurality of channels: divide the data into a plurality of microbatches; process each microbatch of the plurality of microbatches through parallel iterators; and process the output of the parallel iterators through single-instruction multiple-data (SIMD) layers; and asynchronously merge results of the SIMD layers.

Abstract: A method for adjusting a convolutional neural network includes following operations. The convolutional neural network includes convolution layers in a sequential order. Receptive field widths of the convolution layers in a first model of the convolutional neural network are determined. Channel widths of the convolution layers in the first model are reduced into reduced channel widths according to the receptive field widths of the convolution layers and an input image width. A structure of a second model of the convolutional neural network is formed according to the reduced channel widths. The second model of the convolutional neural network is trained according the structure of the second model.

Abstract: Advanced noise and signal management techniques for RPU arrays during ANN training are provided. In one aspect of the invention, a method for ANN training includes: providing an array of RPU devices with pre-normalizers and post-normalizers; computing and pre-normalizing a mean and standard deviation of all elements of an input vector x to the array that belong to the set group of each of the pre-normalizers; and computing and post-normalizing the mean ? and the standard deviation ? of all elements of an output vector y that belong to the set group of each of the post-normalizers.

Abstract: The present disclosure relates to a method of generating spikes by a neuron of a spiking neural network. The method comprises generating at each time, wherein the spike generation encodes at each time instant at least two variable values at the neuron. Synaptic weights may be optimized for a spike train generated by a given presynaptic neuron of a spiking neural network, wherein the spike train being indicative of features of at least one timescale.

Abstract: A method which includes steps of providing a state space model of behaviour of a physical system, the model including covariances for state transition and measurement errors, providing a data based regression model for prediction of state variables of the physical system, observing a state vector comprising state variables of the physical system, determining a prediction vector of state variables based on the state vector, using the regression model, and combining information from the state space model with predictions from the regression model through a Bayesian filter, is provided.

Abstract: Broadly speaking, embodiments of the present techniques provide a reconfigurable hardware-based artificial neural network, wherein weights for each neural network node of the artificial neural network are obtained via training performed external to the neural network.

Abstract: The present disclosure relates generally to a risk-based fraud identification and risk analysis system. For example, the system may receive application data from a first borrower user, determine a segment associated with the application data, apply application data to one or more machine learning (ML) models, and receive a score based at least in part upon output of the ML model.

Abstract: A method is presented for predicting values of multiple input items. The method includes allowing a user to select a first set of variables and input first values therein and predicting second values for a second set of variables, the second values predicted in real-time as the first values are being inputted by the user. A tree-based prediction model is used to predict the second values. The tree-based prediction model is a regression tree or a decision tree.

Abstract: An Online Machine Learning System (OMLS) including an Online Preprocessing Engine (OPrE) configured to (a) receive streaming data including an instance comprising a vector of inputs, the vector of inputs comprising a plurality of continuous or categorical features; (b) discretize features; (c) impute missing feature values; (d) normalize features; and (e) detect drift or change in features; an Online Feature Engineering Engine (OFEE) configured to produce features; and an Online Robust Feature Selection Engine (ORFSE) configured to evaluate and select features; an Online Machine Learning Engine (OMLE) configured to incorporate and utilize one or more machine learning algorithms or models utilizing features to generate a result, and capable of incorporating and utilizing multiple different machine learning algorithms or models, wherein each of the OMLE, the OPrE, the OFEE, and the ORFSE are continuously communicatively coupled to each other, and wherein the OMLS is configured to perform continuous online machi

Abstract: This disclosure relates to method and system for providing an explanation for a prediction generated by an artificial neural network (ANN) model for a given input data. The method may include receiving the given input data and the prediction generated by the ANN model. The ANN model may be built and trained for a target application. The method may further include determining a plurality of relevant portions of the given input data. For each of the plurality of relevant portions, the method may further include fetching a portional prediction and a portional prediction score generated by the ANN model, and determining a degree of influence score based on the portional prediction score and a comparison between the portional prediction and the prediction. The method may further include providing the explanation for the prediction based on the degree of influence score of each of the plurality of relevant portions.

Abstract: A data annotation apparatus for machine learning is provided, which includes a stimulus generation portion, a biometrics reading portion, and a data integration portion. The stimulus generation portion is configured to generate, and present to an agent, at least one stimulus based on a first data from a first machine learning dataset. The biometrics reading portion is configured to measure at least one response of the agent to the at least one stimulus, and to generate biometrics data based on the at least one response. The data integration portion is configured to integrate the biometrics data, data of the at least one stimulus, and data of the first machine learning dataset to thereby obtain a second machine learning dataset. The data annotation apparatus can result in an improved data labeling and an enhanced machine learning.

Abstract: In at least one embodiment, a trust rating system and method provide a precise and accurate, structured (yet adaptable and flexible), quantifying way of expressing historical trustworthiness so the user or decision maker can make more informed decisions on the data or information being evaluated.

Abstract: A semiconductor channel based neuromorphic synapse device 1 including a trap-rich layer may be provided that includes: a first to a third semiconductor regions which are formed on a substrate and are sequentially arranged; a word line which is electrically connected to the first semiconductor region; a trap-rich layer which surrounds the second semiconductor region; and a bit line which is electrically connected to the third semiconductor region. When a pulse with positive (+) voltage is applied to the word line, a concentration of electrons emitted from the trap-rich layer to the second semiconductor region increases and a resistance of the second semiconductor region decreases. When a pulse with negative (?) voltage is applied to the word line, a concentration of electrons trapped in the trap-rich layer from the second semiconductor region increases and the resistance of the second semiconductor region increases.

Abstract: A method is presented for predicting values of multiple input items. The method includes allowing a user to select a first set of variables and input first values therein and predicting second values for a second set of variables, the second values predicted in real-time as the first values are being inputted by the user. A tree-based prediction model is used to predict the second values. The tree-based prediction model is a regression tree or a decision tree.

Abstract: A computer-implemented method for simultaneous metric learning and variable selection in non-linear regression is presented. The computer-implemented method includes introducing a dataset and a target variable, creating a univariate neighborhood probability map for each reference point of the dataset, and determining a pairwise distance between each reference point and other points within the dataset. The computer-implemented method further includes computing a Hessian matrix of a quadratic programming (QP) problem, performing optimization of the QP problem, re-weighing data derived from the optimization of the QP problem, and performing non-linear regression on the re-weighed data.

Abstract: Disclosed is a neuromorphic-processing systems including, in some embodiments, a special-purpose host processor operable as a stand-alone host processor; a neuromorphic co-processor including an artificial neural network; and a communications interface between the host processor and the co-processor configured to transmit information therebetween. The co-processor is configured to enhance special-purpose processing of the host processor with the artificial neural network. Also disclosed is a method of a neuromorphic-processing system having the special-purpose host processor and the neuromorphic co-processor including, in some embodiments, enhancing the special-purpose processing of the host processor with the artificial neural network of the co-processor. In some embodiments, the host processor is a hearing-aid processor.

Abstract: A computer-implemented method is provided for neuromorphic computing in a Dynamic Random Access Memory (DRAM). The method includes representing one or more neurons by memory rows in the DRAM. Each bit in any of the memory rows represents a single synapse. The method further includes responsive to activating a given one of the neurons, reinforcing an associated synaptic state of a corresponding one of the memory rows representing the given one of the neurons. The method also includes responsive to inhibiting the given one of the neurons, degrading the associated synaptic state of the corresponding one of the memory rows representing the given one of the neurons.