Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for presenting a recommendation. In one embodiment, a system is introduced that includes a plurality of models for obtaining a recommendation score. The recommendation score may be obtained using one or more models which can include supervised and unsupervised learning as well as a combination of user information and transactions. In another embodiment, the system is introduced that can provide a total recommendation score and recommendation generated by an ensemble model whose input can include the one or more recommendation scores previously obtained.

Abstract: The present disclosure relates to system(s) and method(s) to generate alerts while monitoring a machine learning model in real time. The system is configured to receive, in response to a first input parameter, a first output parameter generated by a first function of a learning model corresponding to a machine learning model. The system is further configured to receive, in response to a second input parameter, a second output parameter generated by a second functionality of a real-time model corresponding to the machine learning model. Further, the system is configured to compare the first output parameter with the second output parameter and the first input parameter with the second input parameter to generate tuning and rebuilding alerts.

Abstract: A method for determining isothermal phase behavior for reservoir simulation includes generating a training data set using negative flash calculations, training a first machine learning algorithm to identify a supercritical phase and a subcritical phase, training a second machine learning algorithm to identify a number of stable phases in the subcritical phase, and training a third machine learning algorithm to determine a phase split of the subcritical phase that has more than one identified stable phase.

Abstract: Techniques for model reutilization with heterogeneous sensor stacks via sensor data auto-normalization are described. A normalization model can be trained and utilized to normalize sensor data generated by a first type of sensor stack so that it can be used with an existing machine learning model that was trained using data from another type or types of sensor stacks having different characteristics. A sensor data can be generated by the sensor stack and provided as an input to the normalization model to yield normalized sensor data. The normalized sensor data can be provided as input to the existing model to generate accurate results despite the sensor stack having different characteristics than the one(s) used to train the machine learning model.

Abstract: Techniques are provided herein for creating well-balanced computer-based reasoning systems and using those to control systems. The techniques include receiving a request to determine whether to use one or more particular data elements, features, cases, etc. in a computer-based reasoning model (e.g., as data elements, cases or features are being added, or as part of pruning existing features or cases). Conviction measures are determined and inclusivity conditions are tested. The result of comparing the conviction measure can be used to determine whether to include or exclude the feature, case, etc. in the model and/or whether there are anomalies in the model. A controllable system may then be controlled using the computer-based reasoning model.

Abstract: Technology is described for providing machine learning (ML) models. A plurality of candidate ML models that are derived from a primary ML model may be generated in a service provider environment. The primary ML model may be associated with a set of parameters and a candidate ML model in the plurality of candidate ML models may be associated with a subset of the parameters associated with the primary ML model. The plurality of candidate ML models may be run against validation data to evaluate performance criteria for the candidate ML models. A performance representation of the candidate ML models with respect to performance results for the candidate ML models may be provided. An ML model may be selected from the performance representation based on the performance results for the candidate ML models.

Abstract: Systems and processes are disclosed for virtual assistant request recognition using live usage data and data relating to future events. User requests that are received but not recognized can be used to generate candidate request templates. A count can be associated with each candidate request template and can be incremented each time a matching candidate request template is received. When a count reaches a threshold level, the corresponding candidate request template can be used to train a virtual assistant to recognize and respond to similar user requests in the future. In addition, data relating to future events can be mined to extract relevant information that can be used to populate both recognized user request templates and candidate user request templates. Populated user request templates (e.g., whole expected utterances) can then be used to recognize user requests and disambiguate user intent as future events become relevant.

Abstract: Methods and systems for predicting irradiance include learning a classification model using unsupervised learning based on historical irradiance data. The classification model is updated using supervised learning based on an association between known cloudiness states and historical weather data. A cloudiness state is predicted based on forecasted weather data. An irradiance is predicted using a regression model associated with the cloudiness state.

Abstract: Techniques are provided herein for creating well-balanced computer-based reasoning systems and using those to control systems. The techniques include receiving a request to determine whether to use one or more particular data elements, features, cases, etc. in a computer-based reasoning model (e.g., as data elements, cases or features are being added, or as part of pruning existing features or cases). Conviction measures are determined and inclusivity conditions are tested. The result of comparing the conviction measure can be used to determine whether to include or exclude the feature, case, etc. in the model and/or whether there are anomalies in the model. A controllable system may then be controlled using the computer-based reasoning model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training giant neural networks. One of the methods includes obtaining data specifying a partitioning of the neural network into N composite layers that form a sequence of composite layers, wherein each composite layer comprises a distinct plurality of layers from the multiple network layers of the neural network; obtaining data assigning each of the N composite layers to one or more computing devices from a set of N computing devices; partitioning a mini-batch of training examples into a plurality of micro-batches; and training the neural network, comprising: performing a forward pass through the neural network until output activations have been computed for each micro-batch for a final composite layer in the sequence, and performing a backward pass through the neural network until output gradients have been computed for each micro-batch for the first composite layer in the sequence.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output sequences from input sequences. One of the methods includes obtaining an input sequence having a first number of inputs arranged according to an input order; processing each input in the input sequence using an encoder recurrent neural network to generate a respective encoder hidden state for each input in the input sequence; and generating an output sequence having a second number of outputs arranged according to an output order, each output in the output sequence being selected from the inputs in the input sequence, comprising, for each position in the output order: generating a softmax output for the position using the encoder hidden states that is a pointer into the input sequence; and selecting an input from the input sequence as the output at the position using the softmax output.

Abstract: Techniques are disclosed for normalizing and publishing alerts using a behavioral recognition-based video surveillance system configured with an alert normalization module. Certain embodiments allow a user of the behavioral recognition system to provide the normalization module with a set of relative weights for alert types and a maximum publication value. Using these values, the normalization module evaluates an alert and determines whether its rareness value exceed a threshold. Upon determining that the alert exceeds the threshold, the module normalizes and publishes the alert.

Abstract: A neuromorphic device may include a pre-synaptic neuron, a row line extending from the pre-synaptic neuron in a row direction, a post-synaptic neuron, a column line extending from the post-synaptic neuron in a column direction, and a synapse coupled between the row line and the column line. The synapse may be disposed in an intersection region between the row line and the column line. The synapse may include a first unit synapse and a second unit synapse. The first unit synapse may include a resistive memory device. The second unit synapse may include a phase-changeable memory device.

Abstract: Methods and apparatuses are described for automated predictive analysis of user interactions to determine a modification based upon competing classification models. A server computing device receives first encoded text for prior user interactions and trains a plurality of classification models using the first text. The server determines a prediction cost for each of the models based upon the training. The server receives second encoded text for a current user interaction and executes the trained models using the second text to generate a prediction vector for each model that maximizes user engagement. The server selects one of the models based upon the prediction vectors, identifies a communication feature of the model, generates a user interaction modification, and transmits the user interaction modification to a client computing device.

Abstract: A method and system for identifying workplace risk factors is provided. The method includes monitoring via execution of multiple geographically distributed sensor devices, workplace injury based events associated with individuals at a multisite distributed workplace environment. Current injury data describing the workplace injury based events is stored and predicted future workplace injury based events associated with future workplace injury based events with respect to a predicted plurality of individuals at the multisite distributed workplace environment are determined. Injury risk mitigating actions associated with prevention of said predicted future workplace injury based events are generated and an associated cost optimized reduction plan for prioritized implementation of the injury risk mitigating actions is generated.

Abstract: Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a pattern program using a genetic algorithm. The genetic algorithm operates on example data strings that represent the data categories to be recognized or extracted through named entity recognition. In the initialization stage, the initial pattern programs are generated based on example data strings that represent the data categories to be recognized or extracted through named entity recognition. Starting from the initial pattern programs, genetic operations are iteratively conducted to generate generations of offspring pattern programs. In each round of the genetic operation, offspring pattern programs are generated through the crossover operation and the mutation operation.

Abstract: Embodiments of the present invention provide a method for detecting a temporal change of name associated with performance data. The method comprises receiving at least one candidate name replacement pair comprising a pair of names. The method further comprises, in a training stage, for each known name replacement pair included in the performance data, determining a window of time covering a most recent appearance of a first name of the known name replacement pair. The window of time is determined based on quantitative features of a time series model comprising performance data for the first name and a second name of the known name replacement pair. The method further comprises, in the training stage, training a machine learning classifier based on quantitative features computed using a portion of the performance data for the first name and the second name, where the portion is within the window of time determined.

Abstract: The present disclosure relates generally to mechanisms for the estimation of location privacy risk, comprising: building one or more trajectory models from auxiliary information (e.g., one or more maps, one or more routes); capturing common behavioral patterns (e.g., shortest route(s),/fastest route(s)); identifying, given unlinked trajectories for a plurality of users, most likely linkages using the trajectory model(s); eliminating one or more unlikely linkages based on deviation from the shortest route(s) and/or the fastest route(s); measuring privacy as the percentage of linkages correctly identified; and outputting the measured privacy.

Abstract: A method for characterizing a user associated with a vehicle including collecting a movement dataset sampled at least at one of a location sensor and a motion sensor associated with the vehicle, during a time period associated with movement of the vehicle; extracting a set of movement features associated with movement of at least one of the user and the vehicle during the time period; and determining one or more user characteristics describing the user based on the set of movement features, wherein the one or more user characteristics include a classification of the user as at least one of a passenger and a driver for the time period associated with movement of the vehicle.

Abstract: Apparatus and method for a scalable, free running neuromorphic processor. For example, one embodiment of a neuromorphic processing apparatus comprises: a plurality of neurons; an interconnection network to communicatively couple at least a subset of the plurality of neurons; a spike controller to stochastically generate a trigger signal, the trigger signal to cause a selected neuron to perform a thresholding operation to determine whether to issue a spike signal.