Methods, apparatus, systems and articles of manufacture are disclosed to map workloads. An example apparatus includes a constraint definer to define performance characteristic targets of the neural network, an action determiner to apply a first resource configuration to candidate resources corresponding to the neural network, a reward determiner to calculate a results metric based on (a) resource performance metrics and (b) the performance characteristic targets, and a layer map generator to generate a resource mapping file, the mapping file including respective resource assignments for respective corresponding layers of the neural network, the resource assignments selected based on the results metric.

A boom sprayer includes any number of components to treat plants as the boom sprayer travels through a plant field. The components take actions to treat plants or facilitate treating plants. The boom sprayer includes any number of sensors to measure the state of the boom sprayer as the boom sprayer treats plants. The boom sprayer includes a control system to generate actions for the components to treat plants in the field. The control system includes an agent executing a model that functions to improve the performance of the boom sprayer treating plants. Performance improvement can be measured by the sensors of the boom sprayer. The model is an artificial neural network that receives measurements as inputs and generates actions that improve performance as outputs. The artificial neural network is trained using actor-critic reinforcement learning techniques.

Systems and methods to generate artificial intelligence models with synthetic data are disclosed. An example system includes a deep neural network (DNN) generator to generate a first DNN model using first real data. The example system includes a synthetic data generator to generate first synthetic data from the first real data, the first synthetic data to be used by the DNN generator to generate a second DNN model. The example system includes an evaluator to evaluate performance of the first and second DNN models to determine whether to generate second synthetic data. The example system includes a synthetic data aggregator to aggregate third synthetic data and fourth synthetic data from a plurality of sites to form a synthetic data set. The example system includes an artificial intelligence model deployment processor to deploy an artificial intelligence model trained and tested using the synthetic data set.

This application relates to computing circuitry, and in particular to analogue computing circuitry suitable for neuromorphic computing. An analogue computation unit for processing data is supplied with a first voltage from a voltage regulator which is operable in a sequence of phases to cyclically regulate the first voltage. A controller is configured to control operation of the voltage regulator and/or the analogue computation unit, such that the analogue computation unit processes data during a plurality of compute periods that avoid times at which the voltage regulator undergoes a phase transition which is one of a predefined set of phase transitions between defined phases in said sequence of phases. This avoids performing computation operations during a phase transition of the voltage regulator that could result in a transient or disturbance in the first voltage, which could adversely affect the computing.

A processing system including at least one processor may obtain operational data from a radio access network (RAN), format the operational data into state information and reward information for a reinforcement learning agent (RLA), processing the state information and the reward information via the RLA, where the RLA comprises a plurality of sub-agents, each comprising a respective neural network, each of the neural networks encoding a respective policy for selecting at least one setting of at least one parameter of the RAN to increase a respective predicted reward in accordance with the state information, and where each neural network is updated in accordance with the reward information. The processing system may further determine settings for parameters of the RAN via the RLA, where the RLA determines the settings in accordance with selections for the settings via the plurality of sub-agents, and apply the plurality of settings to the RAN.

A data processing method includes the following steps: generating a machine-learning parameter and obtaining a storage parameter code, wherein the storage parameter code corresponds to a storage space; receiving the machine-learning parameter and the storage parameter code, and storing the machine-learning parameter in the storage space according to the storage parameter code, and generating an event notification when the machine-learning parameter is modified; and generating a loading request according to the event notification, and the loading request is used to request the modified machine-learning parameter, wherein after the loading request is generated, the modified machine-learning parameter is downloaded from the storage space corresponding to the storage parameter code.

Computer-implemented techniques for learning composite machine learned models are disclosed. Benefits to implementors of the disclosed techniques include allowing non-machine learning experts to use the techniques for learning a composite machine learned model based on a learning dataset, reducing or eliminating the explorative trial and error process of manually tuning architectural parameters and hyperparameters, and reducing the computing resource requirements and model learning time for learning composite machine learned models. The techniques improve the operation of distributed learning computing systems by reducing or eliminating straggler effects and by reducing or minimizing synchronization latency when executing a composite model search algorithm for learning a composite machine learned model.

A method system, and computer program product for improving data reduction using aggregate machine learning systems comprising receiving, by an aggregating machine learning system from one or more machine learning systems associated with a set of one or more storage arrays, a first set of output parameters indicative of performance metrics for the set of the one or more storage arrays, aggregating, by the aggregating machine learning system, the first set of output parameters, resulting in a second set of output parameters, and sending, from the aggregating machine learning system, at least one member of the second set of output parameters as an input to at least one of the one or more machine learning systems associated with the set of the one or more storage arrays.

A computer-implemented method for inferring a 3D structure of a genome is provided. The method includes providing genome interaction data and operating an autoencoder including a structured sequence of n autoencoder units, each of which including an encoder unit and a decoder unit, each of which is implemented as a recurrent neural network unit. The method includes additionally training the autoencoder by feeding all vectors of genome interaction data to the encoder units. Thereby, the training of the auto-encoder units is performed stepwise by using inner state of respective previous autoencoder units in the cascaded sequence of autoencoder units and performing backpropagation within each of the plurality of autoencoder units after all autoencoder units have processed their respective input values, and using the output values of the encoder units for deriving a 3D model for a visualization of the genome.

The present disclosure generally relates to apparatus, software and methods for detecting and classifying anomalous features in one-dimensional data. The apparatus, software and methods disclosed herein use a YOLO-type algorithm on one-dimensional data. For example, the data can be any one-dimensional data or time series, such as but not limited to be power over time data, signal to noise ratio (SNR) over time data, modulation error ratio (MER) data, full band capture data, radio frequency data, temperature data, stock data, or production data. Each type of data may be susceptible to repeating phenomena that produce recognizable anomalous features. In some embodiments, the features can be characterized or labeled as known phenomena and used to train a machine learning model via supervised learning to recognize those features in a new data series.