Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using embedded function with a deep network. One of the methods includes receiving an input comprising a plurality of features, wherein each of the features is of a different feature type; processing each of the features using a respective embedding function to generate one or more numeric values, wherein each of the embedding functions operates independently of each other embedding function, and wherein each of the embedding functions is used for features of a respective feature type; processing the numeric values using a deep network to generate a first alternative representation of the input, wherein the deep network is a machine learning model composed of a plurality of levels of non-linear operations; and processing the first alternative representation of the input using a logistic regression classifier to predict a label for the input.

Abstract: A system that uses one or more artificial intelligence models that predict an effect of a predicted event on a current state of the system. For example, the model may predict how a rate of change in time-series data may be altered throughout the first time period based on the predicted event.

Abstract: A system includes inputs, outputs, and nodes between the inputs and the outputs. The nodes include hidden nodes. Connections between the nodes are determined based on a gradient computable using symmetric solution submatrices.

Abstract: A simulation apparatus includes a machine learning device for learning a change in a machining route in machining of a workpiece. The machine learning device observes data indicating the changed machining route and data indicating a machining condition of the workpiece as a state variable, and also acquires determination data for determining whether or not a cycle time obtained by simulation using the changed machining route is appropriate, and learns by associating the machining condition of the workpiece with the change in the machining route, using the state variable and the determination data.

Abstract: The present disclosure applies trained artificial intelligence (AI) processing adapted to automatically generating transformations of formatted templates. Pre-existing formatted templates (e.g., slide-based presentation templates) are leveraged by the trained AI processing to automatically generate a plurality of high-quality template transformations. In transforming a formatted template, the trained AI processing not only generates feature transformation of objects thereof but may also provide style transformations where attributes associated with a presentation theme may be modified for a formatted template or set of formatted templates. The trained AI processing is novel in that it is tailored for analysis of feature data of a specific type of formatted template.

Abstract: A neural processing device and method for pruning thereof are provided. The neural processing device includes a processing unit configured to perform calculations, an L0 memory configured to store input and output data of the processing unit, wherein the input and output data include a two-dimensional weight matrix and a weight manipulator configured to receive the two-dimensional weight matrix and partition it into preset sizes to thereby generate partitioned matrices, to generate a pruning matrix by pruning the partitioned matrix, and to transmit the pruning matrix to the processing unit.

Abstract: A facility for estimating a value relating to an occurrence is described. The facility receives a first occurrence specifying a first value for each of a plurality of independent variables that include a distinguished independent variable designated to be monotonically linked to a dependent variable. The facility subjects the first independent variable values specified by the received occurrence to a statistical model to obtain a first value of the dependent variable. The facility receives a second occurrence specifying a second value for each of the plurality of independent variables (the second values varying from the first values in a first direction). The facility subjects the second independent variable values to the statistical model to obtain a second value of the dependent variable, the second value of the dependent variable being guaranteed not to vary from the first value of the dependent variable in a second direction opposite the first direction.

Abstract: Detection systems, methods and computer program products comprising a non-transitory tangible storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method for anomaly detection, a detected anomaly being indicative of an undesirable event. A detection system comprises a computer and an anomaly detection engine executable by the computer, the anomaly detection engine configured to perform a method comprising receiving data comprising a plurality m of multidimensional data points (MDDPs), each data point having n features, constructing a dictionary D based on the received data, embedding dictionary D into a lower dimension embedded space and classifying, based in the lower dimension embedded space, a MDDP as an anomaly or as normal.

Abstract: A system is described that performs training operations for a neural network, the system including an analog circuit element functional block with an array of analog circuit elements, and a controller. The controller monitors error values computed using an output from each of one or more initial iterations of a neural network training operation, the one or more initial iterations being performed using neural network data acquired from the memory. When one or more error values are less than a threshold, the controller uses the neural network data from the memory to configure the analog circuit element functional block to perform remaining iterations of the neural network training operation. The controller then causes the analog circuit element functional block to perform the remaining iterations.

Abstract: A 2D array-based neuromorphic processor includes: axon circuits each being configured to receive a first input corresponding to one bit from among bits indicating n-bit activation; first direction lines extending in a first direction from the axon circuits; second direction lines intersecting the first direction lines; synapse circuits disposed at intersections of the first direction lines and the second direction lines, and each being configured to store a second input corresponding to one bit from among bits indicating an m-bit weight and to output operation values of the first input and the second input; and neuron circuits connected to the first or second direction lines, each of the neuron circuits being configured to receive an operation value output from at least one of the synapse circuits, based on time information assigned individually to the synapse circuits, and to perform an arithmetic operation by using the operation values.

Abstract: Disclosed is a neuromorphic integrated circuit including, in some embodiments, a multi-layered neural network disposed in an analog multiplier array of two-quadrant multipliers. Each multiplier of the multipliers is wired to ground and draws a negligible amount of current when input signal values for input signals to transistors of the multiplier are approximately zero, weight values of the transistors of the multiplier are approximately zero, or a combination thereof. Also disclosed is a method of the neuromorphic integrated circuit including, in some embodiments, training the neural network; tracking rates of change for the weight values; determining if and how quickly certain weight values are trending toward zero; and driving those weight values toward zero, thereby encouraging sparsity in the neural network. Sparsity in the neural network combined with the multipliers wired to ground minimizes power consumption of the neuromorphic integrated circuit such that battery power is sufficient for power.

Abstract: Systems and methods may use one or more artificial intelligence models that predict an effect of a predicted event on a current state of the system. The systems and methods may use one or more artificial intelligence models that predict an effect and/or occurrence of a predicted event based on the current state of the system. In order to generate responses that are both timely and pertinent (e.g., in a dynamic fashion), the system must determine, both quickly (i.e., in real-time or near real-time) and accurately, the predicted event.

Abstract: Disclosed by the disclosure is a convolutional neural network on-chip learning system based on non-volatile memory, comprising: an input module, a convolutional neural network module, an output module and a weight update module. The on-chip learning of the convolutional neural network module implements the synaptic function by using the characteristic of the memristor, and the convolutional kernel value or synaptic weight value is stored in a memristor unit; the input module converts the input signal into the voltage signal; the convolutional neural network module converts the input voltage signal layer-by-layer, and transmits the result to the output module to obtain the output of the network; and the weight update module adjusts the conductance value of the memristor in the convolutional neural network module according to the result of the output module to update the network convolutional kernel value or synaptic weight value.

Abstract: Systems and methods to identify document transitions between adjacent documents within document bundles are disclosed. Exemplary implementations may train a model: obtain training information including a first training bundle and corresponding document separation markers; determine page-specific feature information pertaining to individual pages of the first training bundle; determine, based on the obtained page-specific feature information, page-specific feature values for individual features of the individual pages of the first training bundle; generate, for the individual pages of the first training bundle, a page-specific feature vector; train the model, using the training document bundles, to determine whether the first page and the second page are part of different document. Systems and methods may utilize the trained model to identify document transitions between adjacent documents within document bundles.

Abstract: Embodiments are directed to a method for accelerating machine learning using a plurality of graphics processing units (GPUs), involving receiving data for a graph to generate a plurality of random samples, and distributing the random samples across a plurality of GPUs. The method may comprise determining a plurality of communities from the random samples using unsupervised learning performed by each GPU. A plurality of sample groups may be generated from the communities and may be distributed across the GPUs, wherein each GPU merges communities in each sample group by converging to an optimal degree of similarity. In addition, the method may also comprise generating from the merged communities a plurality of subgraphs, dividing each sub-graph into a plurality of overlapping clusters, distributing the plurality of overlapping clusters across the plurality of GPUs, and scoring each cluster in the plurality of overlapping clusters to train an AI model.

Abstract: Methods for receiving a request to process, on a hardware circuit, a neural network comprising a first convolutional neural network layer having a stride greater than one, and in response, generating instructions that cause the hardware circuit to, during processing of an input tensor, generate a layer output tensor equivalent to an output of the first convolutional neural network layer by processing the input tensor using a second convolutional neural network layer having a stride equal to one but that is otherwise equivalent to the first convolutional neural network layer to generate a first tensor, zeroing out elements of the first tensor that would not have been generated if the second convolutional neural network layer had the stride of the first convolutional neural network layer to generate a second tensor, and performing max pooling on the second tensor to generate the layer output tensor.

Abstract: A computer-implemented method controls input of at least a portion of a first training data set into a first machine learning algorithm. The first training data set includes data quantifying damage to a first compressor and data quantifying a first operating parameter of the first compressor. The first machine learning algorithm is executed, and data quantifying the first operating parameter is received as an output of the first machine learning algorithm. The first machine learning algorithm is trained using the received data output from the first machine learning algorithm and data quantifying the first operating parameter of the first compressor. The trained first machine learning algorithm is configured to enable determination of operability of a second compressor of a gas turbine engine.

Abstract: A log circuit for piecewise linear approximation is disclosed. The log circuit identifies an input associated with a logarithm operation to be performed using piecewise linear approximation. The log circuit then identifies a range that the input falls within from various ranges associated with piecewise linear approximation (PLA) equations for the logarithm operation, where the identified range corresponds to one of the PLA equations. The log circuit computes a result of the corresponding PLA equation based on the respective operands of the equation. The log circuit then returns an output associated with the logarithm operation, which is based at least partially on the result of the PLA equation.

Abstract: A neural network mapping method and a neural network mapping apparatus are provided. The method includes: mapping a calculation task for a preset feature map of each network layer in a plurality of network layers in a convolutional neural network to at least one processing element of a chip; acquiring the number of phases needed by a plurality of processing elements in the chip for completing the calculation tasks, and performing a first stage of balancing on the number of phases of the plurality of processing elements; and based on the number of the phases of the plurality of processing elements obtained after the first stage of balancing, mapping the calculation task for the preset feature map of each network layer in the plurality of network layers in the convolutional neural network to at least one processing element of the chip subjected to the first stage of balancing.

Abstract: A method, system and computer readable medium for generating a cognitive insight comprising: receiving training data, the training data being based upon interactions between a user and a cognitive learning and inference system; performing a ranked insight machine learning operation on the training data; generating a cognitive profile based upon the information generated by performing the ranked insight machine learning operations; and, generating a cognitive insight based upon the cognitive profile generated using the plurality of machine learning operations.