Abstract: Techniques for data integration and labeling are provided. Training real-world signal data is collected for a physical environment, where the training real-world signal data comprises at least one of (i) coordinate information or (ii) a direction to move. Simulated signal data is generated for a first portion of the physical environment, and an aggregate data set is generated comprising the training real-world signal data and the simulated signal data. A machine learning (ML) model is trained using the aggregate data set. A first real-world data point is received, where the first real-world data point does not include coordinate information, and the first real-world data point is labeled based at least in part on coordinate information of the aggregate data set.

Abstract: Techniques disclosed herein relate to learning and applying contextual patient similarities. Multiple template similarity functions (118) may be provided (602). Each template similarity function may compare a respective subset of features of a query entity feature vector with a corresponding subset of features of a candidate entity feature vector. A composite similarity function (120) may be provided (604) as a weighted combination of respective outputs of the template similarity functions. A plurality of labeled entity vectors may be provided (606) as context training data. An approximation function may be applied (608) to approximate a first context label for each respective labeled entity vector. A first context specific composite similarity function may be trained (610) based on the composite similarity function by learning first context weights for the template similarity functions using a first loss function based on output of application of the approximation function to the first context training data.

Abstract: An example method comprises receiving historical sensor data of a first time period, the historical data including sensor data of a renewable energy asset, extracting features, performing a unsupervised anomaly detection technique on the historical sensor data to generate first labels associated with the historical sensor data, performing at least one dimensionality reduction technique to generate second labels, combining the first labels and the second labels to generate combined labels, generating one or more models based on supervised machine learning and the combined labels, receiving current sensor data of a second time period, the current sensor data including sensor data of the renewable energy asset, extracting features, applying the one or more models to the extracted features of the current sensor data to create a prediction of a future fault in the renewable energy asset, and generating a report including the prediction of the future fault in the energy asset.

Abstract: System, apparatus and method may permit users to collaboratively engage in inference on a computer and visualize structure of that inference, and provide a formal verification system for informal argumentation and inference. The system and method may generate and allow for modification of graphical structures that represent sequences of structured rational argumentation; and automatically monitor, compute and represent ratings or scores of nodes within the structure; indicate whether a node is supported by a chain of argumentation that has not been validly rebutted. The graphical structures may be displayed to bring into focus contentious and significant underlying points within an argument, and simulate the effects of alternative resolutions of these contentious points. The graphical displays may provide a transparent verification to other users of the state of what can be demonstrated and refuted, allow discovery of weak or missing points in a logical argument, and allow rational inference by users.

Abstract: A computer system is configured to receive a dataset of image-derived features for a plurality of images, reduce the dimensionality of this dataset, identify clusters within the dimensionally-reduced dataset, and generate a visual representation of the datapoint of the dimensionally-reduced dataset as icons grouped by cluster. User input is received to apply user classification labels to the images for inclusion in a training dataset. A user interface is useable to present information to the user and receive information from the user to facilitate the application of user classification labels.

Abstract: A system for generating a statistical model for fault diagnosis comprising at least one hardware processor, adapted to: extract a plurality of structured values, each associated with at least one of a plurality of semantic entities of a semantic model or at least one of a plurality of semantic relationships of the semantic model, from structured historical information organized in an identified structure and related to at least some of a plurality of historical events, the semantic model represents an ontology of an identified diagnosis domain, each of the plurality of semantic entities relates to at least one of a plurality of domain entities existing in the identified diagnosis domain, and each of the plurality of semantic relationships connects two of the plurality of semantic entities and represents a parent-child relationship therebetween; extract a plurality of unstructured values, each associated with at least one of the plurality of semantic entities.

Abstract: A system coordinates services between users and providers. The system trains a computer model to predict a user state of a user using data about past services. The prediction is based on data associated with a request submitted by a user. Request data can include current data about the user's behavior and information about the service that is independent of the particular user behavior or characteristics. The user behavior may be compared against the user's prior behavior to determine differences in the user behavior for this request and normal behavior of prior requests. The system can alter the parameters of a service based on the prediction about the state of the user requesting the service.

Abstract: There is a need for more effective and efficient predictive data analysis. This need can be addressed by, for example, solutions for performing cross-temporal predictive data analysis. In one example, a method includes determining a time-adjusted encoding for each temporal unit of a group of temporal units, processing each time-adjusted encoding using a cross-temporal encoding machine learning model to generate a cross-temporal encoding of the group of temporal units, and performing one or more prediction-based actions based at least in part on the cross-temporal encoding.

Abstract: Systems, methods, and computer readable media related to: training an encoder model that can be utilized to determine semantic similarity of a natural language textual string to each of one or more additional natural language textual strings (directly and/or indirectly); and/or using a trained encoder model to determine one or more responsive actions to perform in response to a natural language query. The encoder model is a machine learning model, such as a neural network model. In some implementations of training the encoder model, the encoder model is trained as part of a larger network architecture trained based on one or more tasks that are distinct from a “semantic textual similarity” task for which the encoder model can be used.

Abstract: Techniques for detecting and correcting anomalies in computer-based reasoning systems are provided herein. The techniques can include obtaining current context data and determining a contextually-determined action based on the obtained context data and a reasoning model. The reasoning model may have been determined based on one or more sets of training data. The techniques may cause performance of the contextually-determined action and, potentially, receiving an indication that performing the contextually-determined action in the current context resulted in an anomaly. The techniques include determining a portion of the reasoning model that caused the determination of the contextually-determined action based on the obtained context data and causing removal of the portion of the model that caused the determination of the contextually-determined action, to produce a corrected reasoning model.

Abstract: The techniques herein include using an input context to determine a suggested action and/or cluster. Explanations may also be determined and returned along with the suggested action. The explanations may include (i) one or more most similar cases to the suggested case (e.g., the case associated with the suggested action) and, optionally, a conviction score for each nearby cases; (ii) action probabilities, (iii) excluding cases and distances, (iv) archetype and/or counterfactual cases for the suggested action; (v) feature residuals; (vi) regional model complexity; (vii) fractional dimensionality; (viii) prediction conviction; (ix) feature prediction contribution; and/or other measures such as the ones discussed herein, including certainty. The explanation data may be used to determine whether to perform a suggested action.

Abstract: A feature data segment may be determined by applying a feature segmentation model to a test data observation. The feature segmentation model may be pre-trained via a plurality of training data observations and may divide the plurality of training data observations into a plurality of feature data segments. A predicted target value may be determined by applying to a test data observation a prediction model pre-trained via a plurality of training data observations. One or more distance metrics representing a respective distance between the test data observation and the feature data segment along one or more dimensions may be determined. The one or more distance metrics may be represented in a user interface. An updated prediction model and an updated feature segmentation model that both incorporate the test data observation and the training data observations may be determined based on user input.

Abstract: A method of training a quantum computer employs quantum algorithms. The method comprises loading, into the quantum computer, a description of a quantum Boltzmann machine, and training the quantum Boltzmann machine according to a protocol, wherein a classification error is used as a metric for the protocol.

Abstract: Systems and methods for predicting account action failures are disclosed. An example method is performed by one or more processors of a system and includes detecting a character sequence being entered for identifying an account to be used in an account action, extracting a set of subsequences from the character sequence, determining, for the character sequence and each subsequence extracted from the character sequence, a feature value for each of a predefined set of features for characterizing sequences, the determining based on a number of characters included in at least one portion of the character sequence, and generating, using the feature values as input to a model trained in conjunction with a machine learning (ML) algorithm, a predictive value suggestive of a likelihood that the detected character sequence, if submitted, will result in the account action failing.

Abstract: Techniques for detecting and correcting anomalies in computer-based reasoning systems are provided herein. The techniques can include obtaining current context data and determining a contextually-determined action based on the obtained context data and a reasoning model. The reasoning model may have been determined based on one or more sets of training data. The techniques may cause performance of the contextually-determined action and, potentially, receiving an indication that performing the contextually-determined action in the current context resulted in an anomaly. The techniques include determining a portion of the reasoning model that caused the determination of the contextually-determined action based on the obtained context data and causing removal of the portion of the model that caused the determination of the contextually-determined action, to produce a corrected reasoning model.

Abstract: A dataset is received that is for processing by a machine learning model. A scoring payload for the dataset and that regards the machine learning model is also received. A set of features of the machine learning model is determined by analyzing the scoring payload. The scoring payload is structured in accordance with the set of features such that the structured scoring payload is ready for analysis for a monitor of the machine learning model.

Abstract: The techniques herein include using an input context to determine a suggested action. One or more explanations may also be determined and returned along with the suggested action. The one or more explanations may include (i) one or more most similar cases to the suggested case (e.g., the case associated with the suggested action) and, optionally, a conviction score for each nearby cases; (ii) action probabilities, (iii) excluding cases and distances, (iv) archetype and/or counterfactual cases for the suggested action; (v) feature residuals; (vi) regional model complexity; (vii) fractional dimensionality; (viii) prediction conviction; (ix) feature prediction contribution; (x) conviction ratio; (xi) contribution ratio; and/or other measures such as the ones discussed herein, including certainty. In some embodiments, the explanation data may be used to determine whether to perform a suggested action.

Abstract: There is a need for more effective and efficient prediction data analysis. This need can be addressed by, for example, solutions for performing first-occurrence multi-disease prediction. In one example, a method includes determining a per-event-type loss value for each event type of a group of event types; determining a cross-event-type loss value based at least in part on each per-event-type loss value; training a multi-event-type prediction model based at least in part on the cross-event type loss value; generating a first-occurrence prediction based at least in part on the multi-event-type prediction model, wherein the first occurrence-prediction comprises a first-occurrence prediction item for each event type of the group of event types; and performing one or more prediction-based actions based at least in part on the first-occurrence prediction.

Abstract: The disclosed embodiments provide a system for streamlining machine learning. During operation, the system determines a resource overhead for a baseline version of a machine learning model that uses a set of features to produce entity rankings and a number of features to be removed to lower the resource overhead to a target resource overhead. Next, the system calculates importance scores for the features, wherein each importance score represents an impact of a corresponding feature on the entity rankings. The system then identifies a first subset of the features to be removed as the number of features with lowest importance scores and trains a simplified version of the machine learning model using a second subset of the features that excludes the first subset of the features. Finally, the system executes the simplified version to produce new entity rankings.

Abstract: The present disclosure relates to a concept for training one or more model parameters of a predictive parking difficulty model for different locations based on collected telemetry data. A ground truth ranking related to subjective parking difficulties at the different locations is obtained based on pairwise comparison of parking difficulties between pairs of the different locations by one or more humans. A prediction loss between a model ranking of the different locations obtained by the predictive parking difficulty model and the ground truth ranking is determined. The one or more model parameters are adjusted to minimize the prediction loss between the model ranking and the ground truth ranking.