Abstract: A method and system are provided. The method includes obtaining, by a hardware processor, candidate data representing a plurality of candidates. The method further includes calculating, by the hardware processor, for each of the candidates, a temporal next state of a Recurrent Neural Network (RNN) by inputting a corresponding one of the candidates to the RNN at a current state. The method also includes merging, by the hardware processor, the temporal next state for each of the candidates to obtain the temporal next state of the RNN.

Abstract: A method of automating a fraud classification process includes generating or updating fraud classification rules, at least by training a machine learning program using fraud classifications of a plurality of financial accounts and financial transaction data associated with those accounts. The method also includes retrieving first financial transaction data associated with a first financial account, and selecting, by applying the fraud classification rules to the first financial transaction data, a first fraud classification. The first fraud classification may be selected from among a plurality of predetermined fraud classifications. The method also includes causing an indication of the first fraud classification to be displayed to one or more people via one or more respective computing device user interfaces, the indication further specifying at least the first financial account.

Abstract: A machine learning hardware accelerator architecture and associated techniques are disclosed. The architecture features multiple memory banks of very wide SRAM that may be concurrently accessed by a large number of parallel operational units. Each operational unit supports an instruction set specific to machine learning, including optimizations for performing tensor operations and convolutions. Optimized addressing, an optimized shift reader and variations on a multicast network that permutes and copies data and associates with an operational unit that support those operations are also disclosed.

Abstract: A motion processing analysis method, system, and computer program product include selecting a first spatial graticule size, determining that a physical entity is associated with a first spatial graticule during a first timeframe and that the physical entity is associated with a second spatial graticule during a second timeframe, as a result of determining that the physical entity is associated with the first spatial graticule during the first timeframe and that the physical entity is associated with the second spatial graticule during the second timeframe, adding to a sum, determining that the sum is beyond an acceptable range, and as a result of determining that the sum beyond the acceptable range, selecting a second spatial graticule size.

Abstract: Methods, systems, and apparatuses for adapting a predictive model for a manufacturing process. One method includes receiving, with an electronic processor, a plurality of data points for a plurality of manufactured parts and the predictive model. The predictive model outputs a label for a manufactured part provided by the manufacturing process indicating whether the manufactured part is accepted or rejected. The method also includes estimating, with the electronic processor, a drift for each of the plurality of data points and generating, with the electronic processor, an adapted version of the predictive model based on the predictive model and the drift for each of the plurality of data points. In addition, the method includes outputting, with the electronic processor, a label for each of the plurality of manufactured parts using the adapted version of the predictive model.

Abstract: The present disclosure includes methods and systems for generating captions for digital images. In particular, the disclosed systems and methods can train an image encoder neural network and a sentence decoder neural network to generate a caption from an input digital image. For instance, in one or more embodiments, the disclosed systems and methods train an image encoder neural network (e.g., a character-level convolutional neural network) utilizing a semantic similarity constraint, training images, and training captions. Moreover, the disclosed systems and methods can train a sentence decoder neural network (e.g., a character-level recurrent neural network) utilizing training sentences and an adversarial classifier.

Abstract: A convolutional neural network is provided comprising artificial neurons arranged in layers, each comprising output matrices. An output matrix comprises output neurons and is connected to an input matrix, comprising input neurons, by synapses associated with a convolution matrix comprising weight coefficients associated with the output neurons of an output matrix. Each synapse consists of a set of memristive devices storing a weight coefficient of the convolution matrix. In response to a change of the output value of an input neuron, the neural network dynamically associates each set of memristive devices with an output neuron connected to the input neuron. The neural network comprises accumulator(s) for each output neuron; to accumulate the values of the weight coefficients stored in the sets of memristive devices dynamically associated with the output neuron, the output value of the output neuron being determined from the value accumulated in the accumulator(s).

Abstract: Technologies for optimization of machine learning training include a computing device to train a machine learning network with a training algorithm that is configured with configuration parameters. The computing device may perform many training instances in parallel. The computing device captures a time series of partial accuracy values from the training. Each partial accuracy value is indicative of machine learning network accuracy at an associated training iteration. The computing device inputs the configuration parameters to a feed-forward neural network to generate a representation and inputs the representation to a recurrent neural network. The computing device trains the feed-forward neural network and the recurrent neural network against the partial accuracy values. The computing device optimizes the feed-forward neural network and the recurrent neural network to determine optimized configuration parameters.

Abstract: A machine learning device acquires from a numerical controller information relating to machining when the machining is performed, and further acquires an actual delay time due to servo control and due to machine movement which are caused in the machining when the machining is performed. Then, the device performs supervised learning using the acquired machining-related information as input data, and using the acquired actual delay time due to servo control and due to machine movement as supervised data, and constructs a learning model, thereby predicting the machine delay time caused in a machine with high precision.

Abstract: Traffic condition forecasting techniques are provided that use matrix compression and deep neural networks. An illustrative method comprises obtaining a compressed origination-destination matrix indicating a cost to travel between pairs of a plurality of nodes, wherein the compressed origination-destination matrix is compressed using a locality-aware compression technique that maintains only non-empty data; obtaining a trained deep neural network trained using the compressed origination-destination matrix and past observations of traffic conditions at various times; and applying traffic conditions between two nodes in the compressed origination-destination matrix at a time, t, to the trained deep neural network to obtain predicted traffic conditions between the two nodes at a time, t+?. A tensor can be generated indicating an evolution of traffic conditions over a time span using a stacked Origination-Destination matrix comprising a plurality of past observations representing the time span.

Abstract: System and techniques for identifying novel information are described herein. A classified experience may be obtained. The classified experience may include a set of attributes. Memory counts of members of the set of attributes for a user may be obtained. A novelty score for the classified experience may be computed by comparing the set of attributes to the memory counts. The classified experience may be presented to the user when the novelty score meets a qualification criterion.