Abstract: A distributed system may implement identifying correlated workloads for resource allocation. Resource data for resources hosted at resource hosts in a distributed system may be analyzed to determine behavioral similarities. Historical behavior data or resource configuration data, for instance, may be compared between resources. Behaviors between resources may be identified as correlated according to the determined behavioral similarities. An allocation of one or more resource hosts in the distributed system may be made for a resource based on the behaviors identified as correlated. For instance, resources may be migrated from a current resource host to another resource host, new resources may be placed at a resource host, or resources may be reconfigured into different resources. Machine learning techniques may be implemented to refine techniques for identifying correlated behaviors.

Abstract: This present disclosure relates to systems and methods for providing an Adaptive Analytical Behavioral and Health Assistant. These systems and methods may include collecting one or more of patient behavior information, clinical information, or personal information; learning one or more patterns that cause an event based on the collected information and one or more pattern recognition algorithms; identifying one or more interventions to prevent the event from occurring or to facilitate the event based on the learned patterns; preparing a plan based on the collected information and the identified interventions; and/or presenting the plan to a user or executing the plan.

Abstract: Computer systems and computer-implemented methods train and/or operate, once trained, a machine-learning system that comprises a plurality of generator-detector pairs. The machine-learning computer system comprises a set of processor cores and computer memory that stores software. When executed by the set of processor cores, the software causes the set of processor cores to implement a plurality of generator-detector pairs, in which: (i) each generator-detector pair comprises a machine-learning data generator and a machine-learning data detector; and (ii) each generator-detector pair is for a corresponding cluster of data examples respectively, such that, for each generator-detector pair, the generator is for generating data examples in the corresponding cluster and the detector is for detecting whether data examples are within the corresponding cluster.

Abstract: Computer-implemented systems and methods soft-tie learned parameters of a neural network(s). The soft-tying comprises: applying a common label to the first and second learned parameters; and as part of the training, and in response to the first and second learned parameters having the common label, applying a regularization penalty to a loss function for the first learned parameter upon a determination that the first learned parameter is different than the second learned parameter. The learned parameters can be connection weights, node biases, and/or parametric model statistics. The application of the regularization penalty can be influenced by a soft-tying hyperparameter.

Abstract: An artificial neural network device that utilizes one or more non-volatile memory arrays as the synapses. The synapses are configured to receive inputs and to generate therefrom outputs. Neurons are configured to receive the outputs. The synapses include a plurality of memory cells, wherein each of the memory cells includes spaced apart source and drain regions formed in a semiconductor substrate with a channel region extending there between, a floating gate disposed over and insulated from a first portion of the channel region and a non-floating gate disposed over and insulated from a second portion of the channel region. Each of the plurality of memory cells is configured to store a weight value corresponding to a number of electrons on the floating gate. The plurality of memory cells are configured to multiply the inputs by the stored weight values to generate the outputs.

Abstract: Semantic information that describes data sets is inferred based upon a semantic analysis performed on data sets retained within a data repository. The semantic analysis can include a determination of formats associated with fields of the data sets and a comparison of values of the fields against reference data sets having predetermined semantic types. Correlations are inferred between data sets based upon respective semantic information. The correlations are incorporated into visualizations displayed in connection with a graphical user interface.

Abstract: Techniques that facilitate time series analysis using machine learning are provided. In one example, a system includes a matrix generation component, a matrix factorization component and a machine learning component. The matrix generation component converts at least a first stream of time series data and a second stream of time series data (e.g., raw time series data) into a data matrix (e.g., a partially-observed similarity matrix) that comprises void data and numerical data associated with the first stream of time series data and the second stream of time series data. The matrix factorization component factorizes the data matrix into a first factorization data matrix and a second factorization data matrix. The machine learning component processes a machine learning model based on first matrix data associated with the first factorization data matrix and second matrix data associated with the second factorization data matrix.

Abstract: A machine learning model can be trained to infer the probability of the presence of categories of a software bug in a source code file. A bug tracker can provide information concerning the category to which a software bug belongs. The bug data supplied to a machine learning model for inferring the presence of particular categories of bugs can be filtered to exclude a specified category or categories of bugs. Information including but not limited to organizational boundaries can be inferred from the category of bugs present in a body of source code. The inferred organization boundaries can be used to generate team-specific machine learning models.

Abstract: Techniques that facilitate time series analysis using machine learning are provided. In one example, a system includes a matrix generation component, a matrix factorization component and a machine learning component. The matrix generation component converts at least a first stream of time series data and a second stream of time series data (e.g., raw time series data) into a data matrix (e.g., a partially-observed similarity matrix) that comprises void data and numerical data associated with the first stream of time series data and the second stream of time series data. The matrix factorization component factorizes the data matrix into a first factorization data matrix and a second factorization data matrix. The machine learning component processes a machine learning model based on first matrix data associated with the first factorization data matrix and second matrix data associated with the second factorization data matrix.

Abstract: The present disclosure provides an artificial neural network communicatively-coupled to at least one computer having one or more processors, including a plurality of neurons arranged in layers. The artificial neural network is arranged to receive a new neuron into a layer of the artificial neural network during training; the new neuron is added to the neural network when no other neuron in that layer for a selected output can learn a relationship associated with an input vector of a data set being learnt. The new neuron is updated with both the relationship which could not be learnt by any other neuron in that layer and a modified data set from a last trained neuron in that layer that contributes to the selected output of the neural network. Methods and computer-readable media are also disclosed.

Abstract: Methods, systems, and apparatus for solving optimization tasks. In one aspect, a system includes one or more classical processors and one or more quantum computing resources, wherein the one or more classical processors and one or more quantum computing resources are configured to perform operations comprising receiving input data comprising data specifying a computational task to be solved; processing the received input data using a first quantum computing resource to generate data representing a reduced computational task, wherein the reduced computational task has lower dimensionality that the computational task; and processing the data representing the reduced computational task to obtain a solution to the computational task.

Abstract: A method, system and computer-usable medium are disclosed for automating the generation of an incorrect answer to a question suitable for a multiple choice exam. An input corpus of human-readable text associated with a subject domain is provided to a question generation system, where it is processed to generate a set of question-answer (QA) pairs. The set of QA pairs is then processed with the corpus of input text to extract a set of input keywords and concepts. A concept dependency graph is then used to perform disambiguation operations on the set of input keywords and concepts, and the reference keywords and concepts it contains, to generate a set of distractor words. The resulting set of distractor words is then processed with the set of QA pairs to generate a set of multiple choice question-answers that include various distractor answers.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for an adaptive oracle-trained learning framework for automatically building and maintaining models that are developed using machine learning algorithms. In embodiments, the framework leverages at least one oracle (e.g., a crowd) for automatic generation of high-quality training data to use in deriving a model. Once a model is trained, the framework monitors the performance of the model and, in embodiments, leverages active learning and the oracle to generate feedback about the changing data for modifying training data sets while maintaining data quality to enable incremental adaptation of the model.

Abstract: A mechanism is provided for text classifier training. The mechanism receives a training set of text and class specification pairs to be used as a ground truth for training a text classifier machine learning model for a text classifier. Each text and class specification pair comprises a text and a corresponding class specification. A domain terms selector component identifies at least one domain term in the texts of the training set. A domain terms replacer component replaces the at least one identified domain term in the texts of the training set with a corresponding replacement term to form a revised set of text and class specification pairs. A text classifier trainer component trains the text classifier machine learning model using the revised set to form a trained text classifier machine learning model.

Abstract: Embodiments for fault diagnosis and analysis of refrigeration condenser systems by a processor. An energy usage anomaly is detected in a condenser by comparing an energy usage profile of the condenser against a knowledge domain of energy usage standards and energy usage standards anomalies.

Abstract: One factor in limiting the speed of conventional implementations of mixture models is that the algorithm involves many decisions where different operations are fetched and performed depending on the outcome of the decisions. These decisions cause flushing of the pipeline, and thus prevent the realization of a highly parallel pipeline in a processor. Without parallelism, the throughput of the pipeline in the processor, i.e., the ability to process many samples of the digital input at a time, is limited. To alleviate this issue, implementation of the mixture model is reformulated, among other things, by embedding decisions into the process flow as multiplicative factors. The resulting implementation alleviates the need to use if-else statements for the decisions and reduces the number of times the pipeline has to be flushed. The implementation enables a pipeline with a higher degree of parallelism and thereby increases throughput and speed of the implementation.

Abstract: The present disclosure provides methods for applying artificial neural networks to flow cytometry data generated from biological samples to diagnose and characterize cancer in a subject. The disclosure also provides methods of training, testing, and validating artificial neural networks.

Abstract: Quantum neural nets, which utilize quantum effects to model complex data sets, represent a major focus of quantum machine learning and quantum computing in general. In this application, example methods of training a quantum Boltzmann machine are described. Also, examples for using quantum Boltzmann machines to enable a form of quantum state tomography that provides both a description and a generative model for the input quantum state are described. Classical Boltzmann machines are incapable of this. Finally, small non-stoquastic quantum Boltzmann machines are compared to traditional Boltzmann machines for generative tasks, and evidence presented that quantum models outperform their classical counterparts for classical data sets.

Abstract: A predictive analytics system and method in the setting of multi-class classification are disclosed, for identifying systematic changes in an evaluation dataset processed by a fraud-detection model by examining the time series histories of an ensemble of entities such as accounts. The ensemble of entities is examined and processed both individually and in aggregate, via a set of features determined previously using a distinct training dataset. The specific set of features in question may be calculated from the entity's time series history, and may or may not be used by the model to perform the classification. Certain properties of the detected changes are measured and used to improve the efficacy of the predictive model.

Abstract: A machine learning device of a controller observes, as state variables that express a current state of an environment, feeding amount data indicating a feeding amount per unit cycle of a tool and vibration amount data indicating a vibration amount of a cutting part of the tool when the cutting part of the tool passes through the workpiece. In addition, the machine learning device acquires determination data indicating a propriety determination result of the vibration amount of the cutting part of the tool when the cutting part of the tool passes through the workpiece. Then, the machine learning device learns the feeding amount per unit cycle of the tool when the cutting part of the tool passes through the workpiece in association with the vibration amount data, using the state variables and the determination data.