Abstract: Aspects of the present disclosure are directed to systems, methods, and computer readable media for executing actions for events associated with use of applications. A computing system can identify free text associated with an application to be evaluated for at least one of a plurality of events associated with a use of the application. The computing system can apply the free text to a machine learning (ML) architecture. The computing system can determine, based on applying the free text to the ML architecture, a value indicating a likelihood of occurrence of an event associated with the use of the application. The computing system can provide to a generative ML model, a model input based on the free text and the value, to obtain data for an electronic document characterizing the event. The computing system can execute an action using the data for the electronic document.

Abstract: Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.

Abstract: Systems and methods for generating synthetic intercorrelated data are disclosed. For example, a system may include at least one memory storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include training a parent model by iteratively performing steps. The steps may include generating, using the parent model, first latent-space data and second latent-space data. The steps may include generating, using a first child model, first synthetic data based on the first latent-space data, and generating, using a second child model, second synthetic data based on the second latent-space data. The steps may include comparing the first synthetic data and second synthetic data to training data. The steps may include adjusting a parameter of the parent model based on the comparison or terminating training of the parent model based on the comparison.

Abstract: A computer system is provided that is designed to handle multi-label classification. The computer system includes multiple processing instances that are arranged in a hierarchal manner and execute differently trained classification models. The classification task of one processing instance and the executed model therein may rely on the results of classification performed by another processing instance. Each of the models may be associated with a different threshold value that is used to binarize the probability output from the classification model.

Abstract: A computer implemented method for detecting regression in a relationship between a performance indicator and AI metrics includes calculating a baseline threshold of regression degradation according to a historical correlation coefficient corresponding to a performance indicator and a set of AI metrics, calculating a current correlation coefficient according to one or more current data records, identifying a correction constant according to the current correlation coefficient and a desired correlation coefficient, generating a function to predict correction constants corresponding to performance indicator data and the set AI metrics, determining a delta correction constant for each AI metric of the set of AI metrics, applying the determined delta correction constant to the set of AI metrics, and identifying a subset of AI metric outliers according to the calculated baseline threshold and the determined delta correction constant.

Abstract: A method for training a neural network. The neural network comprises a first layer which includes a plurality of filters to provide a first layer output comprising a plurality of feature maps. Training of the classifier includes: receiving, by a preceding layer, a first layer input in the first layer, wherein the first layer input is based on the input signal; determining the first layer output based on the first layer input and a plurality of parameters of the first layer; determining a first layer loss value based on the first layer output, wherein the first layer loss value characterizes a degree of dependency between the feature maps, the first layer loss value being obtained in an unsupervised fashion; and training the neural network. The training includes an adaption of the parameters of the first layer, the adaption being based on the first layer loss value.

Abstract: In general, the disclosure describes techniques for characterizing a dynamical system and a neural ordinary differential equation (NODE)-based controller for the dynamical system. An example analysis system is configured to: obtain a set of parameters of a NODE model used to implement the NODE-based controller, the NODE model trained to control the dynamical system; determine, based on the set of parameters, a system property of a combined system comprising the dynamical system and the NODE-based controller, the system property comprising one or more of an accuracy, safety, reliability, reachability, or controllability of the combined system; and output the system property to modify one or more of the dynamical system or the NODE-based controller to meet a required specification for the combined system.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, includes various embodiments for receiving a plurality of characteristics of a target artificial intelligence (AI) network. The various embodiments apply the plurality of characteristics of the target AI network to at least one of a static cost model and a heuristic AI network model. The various embodiments further receive optimized target AI network configuration data from at least one of static cost model and the heuristic AI network model, the optimized target AI network configuration data representative of a subset of the characteristics of the target AI network that minimize a cost function of execution of the target AI network.

Abstract: A method is described for predicting permeability including receiving a 3-D earth model including a volume of interest; generating 2-D property images; receiving 2-D fracture images; training a physics-guided neural network using the 2-D fracture images; and predicting permeability using the physics-guided neural network applied to the 2-D property images. The method is executed by a computer system.

Abstract: A model for a natural language understanding task is generated based on labeled data generated by a labeling model. The model for the natural language understanding task is smaller than the labeling model (i.e., with lower computational and memory requirements than the combined model), but with substantially the same performance as the labeling model. In some cases, the labeling model may be generated based on a large pre-trained model.

Abstract: Systems and methods are described for analyzing information relating to technical events associated with client devices in an organization (e.g., hardware and/or software malfunctions or performance inefficiencies originating at a client device or elsewhere in an organizational computing system). Particularly, machine learning techniques may use one or more trained artificial neural networks to classify technical events. Classifications of technical events may include, for example, causes of technical events, identifications of other affected devices, and/or steps for resolving technical events. Additionally, systems and methods are described for analyzing information relating to organizational ideas conceived of by client device users within the organization.

Abstract: A Bayesian neural network including an input layer, and, an output layer, and, possibly, one or more hidden layer(s). Each neuron of a layer is connected at its input with a plurality of synapses, the synapses of the plurality being implemented as a RRAM array constituted of cells, each column of the array being associated with a synapse and each row of the array being associated with an instance of the set of synaptic coefficients, the cells of a row of the RRAM being programmed during a SET operation with respective programming current intensities, the programming intensity of a cell being derived from the median value of a Gaussian component obtained by GMM decomposition into Gaussian components of the marginal posterior probability of the corresponding synaptic coefficient, once the BNN model has been trained on a training dataset.

Abstract: A system for horizontal scaling of retraining machine learning models across operational domains is provided. The system may reduce computational overhead associated model retraining. The system may include an artificial intelligence (“AI”) engine that determines target machine learning models that need to be retrained in response to changed training data. The AI engine may assign daemons to the target models. The daemons may gather retraining requirements such as source code and training data. The daemons may schedule the target models for retraining on a CPU or a GPU based model training system.

Abstract: A method for compressing a neural network model is disclosed. The method for compressing a neural network model includes receiving, at a processor of the electronic apparatus, an original model including a plurality of layers each including a plurality of filters, a compression ratio to be applied to the original model, and a metric for determining an importance of the plurality of filters, determining the importance of the plurality of filters using the metric, normalizing the importance of the plurality of filters layer by layer, and compressing the original model by removing at least one filter among the plurality of filters based on the normalized importance and the compression ratio.

Abstract: A model is trained through a hybrid machine learning process. In the hybrid machine landing process, an automatic machine learning process is performed on a dataset to generate a model for making a prediction. The automatic machine learning process uses a pipeline to train the model and makes decisions in the steps of the pipeline. After the model is trained through the automatic machine learning process, a representation of the pipeline is generated and presented to a user in a user interface. The user interface allows the user to modify at least some decision made in the automatic machine learning process. One or more modifications are received from the user through the user interface and are used to refine the trained model. The refined model is deployed to make the prediction based on new data.

Abstract: A metrology module includes an estimation model that is configured to provide an estimation of independent overlay with tool induced shift on received wafers based on only one azimuth angle spectra. The estimation model can use at least one machine learning algorithm. The estimation model can be derived by the machine learning algorithm applied to calculated training data based on a first training sample set from initial metrology measurements and an additional tool induced shift training sample.

Abstract: Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.

Abstract: A method and computer program product for obtaining values are run using a neural network according to a machine learning algorithm. One embodiment may comprise accessing one or more datafiles of input data, where the input data is representable in a d-dimensional space, with d>1. The method may explore N distinct paths of the input data in the d-dimensional space, where N?1, and collects data along the N distinct paths explored to respectively form N sequences of M objects each, with M?2. For one or more sequences of the N sequences formed, values obtained from the M objects of each sequence may be coupled into one or more input nodes of a neural network, which is then run according to the machine learning algorithm to obtain L output values from, L?1.

Abstract: An agricultural data collection framework is provided in a system and method for tracking and managing livestock, and for analyzing animal conditions such as health, growth, nutrition, and behavior. The framework uses ultra-high frequency interrogation of RFID tags to collect individual animal data across multiple geographical locations, and incorporates artificial intelligence techniques to develop machine learning base models for statistical process controls around each animal for evaluating the animal condition. The framework provides a determination of normality at an individual animal basis or for a specific location, and generates alerts, predictions, and a targeted processing or application schedule for prioritizing and delivering resources when intervention is needed.

Abstract: Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.