Abstract: The present disclosure relates to merging database tables. Systems and methods may involve performing a comparison between the first set of records and the second set of records and identifying a plurality of record pairs based on the comparison. Each record pair may comprise a record in the first set of records and a record in the second set of records. In addition, A feature signature may be generated for each record pair by comparing field values in each record pair. The feature signature may be classified to identify at least one related record pair. A merged database table may be generated such that it comprises the at least one related record pair and comprises a set of unique records among selected from the first set of records and the second set of records.

Abstract: Systems and methods for model evaluation. A model is evaluated by performing a decomposition process for a model output, relative to a baseline input data set.

Abstract: Systems and methods for model evaluation. A protected class model that satisfies an accuracy threshold is built by using: data sets for use by a modeling system being evaluated, and protected class membership information for each data set. A target for the protected class model is a protected class membership variable indicating membership in a protected class. Each predictor of the protected class model is a predictor of an evaluated model used by the modeling system. A target of the evaluated model is different from the target of the protected class model. Each predictor is a set of one or more variables of the data sets. For each predictor of the protected class model, a protected class model impact ranking value and a modeling system impact ranking value are determined.

Abstract: Systems and methods for model evaluation. A protected class model that satisfies an accuracy threshold is built by using: data sets for use by a modeling system being evaluated, and protected class membership information for each data set. A target for the protected class model is a protected class membership variable indicating membership in a protected class. Each predictor of the protected class model is a predictor of an evaluated model used by the modeling system. A target of the evaluated model is different from the target of the protected class model. Each predictor is a set of one or more variables of the data sets. For each predictor of the protected class model, a protected class model impact ranking value and a modeling system impact ranking value are determined.

Abstract: Systems and methods for generating and processing modeling workflows are disclosed. In some examples, a reference distribution of scores generated by a model is determined. The reference distribution of scores is recorded in a structured database. One or more unexpected scores are detected during execution of the model. To detect the one or more unexpected scores, a production distribution of scores is compared with the reference distribution of scores recorded in the structured database. The production distribution of scores is generated by the model for a production input data set. An alert is then provided to an external system, when an alert condition is determined to be satisfied based on the comparison. The alert indicates detection of the one or more unexpected scores.

Abstract: Systems and methods enable most valuable players (MVPs) of a gameplay session to play a bonus gameplay session where other players (non-MVP players) participate as player-spectators in online video games and, through a collective voting mechanism or through active interaction, determine the occurrence of certain events or contents of the bonus gameplay in real time. In this manner, non-MVP players are able to directly affect the course of the bonus gameplay.

Abstract: Systems and methods enable most valuable players (MVPs) of a gameplay session to play a bonus gameplay session where other players (non-MVP players) participate as player-spectators in online video games and, through a collective voting mechanism or through active interaction, determine the occurrence of certain events or contents of the bonus gameplay in real time. In this manner, non-MVP players are able to directly affect the course of the bonus gameplay.

Abstract: Systems and methods enable most valuable players (MVPs) of a gameplay session to play a bonus gameplay session where other players (non-MVP players) participate as player-spectators in online video games and, through a collective voting mechanism or through active interaction, determine the occurrence of certain events or contents of the bonus gameplay in real time. In this manner, non-MVP players are able to directly affect the course of the bonus gameplay.

Abstract: Systems and methods enable most valuable players (MVPs) of a gameplay session to play a bonus gameplay session where other players (non-MVP players) participate as player-spectators in online video games and, through a collective voting mechanism or through active interaction, determine the occurrence of certain events or contents of the bonus gameplay in real time. In this manner, non-MVP players are able to directly affect the course of the bonus gameplay.

Abstract: Systems and methods enable most valuable players (MVPs) of a gameplay session to play a bonus gameplay session where other players (non-MVP players) participate as player-spectators in online video games and, through a collective voting mechanism or through active interaction, determine the occurrence of certain events or contents of the bonus gameplay in real time. In this manner, non-MVP players are able to directly affect the course of the bonus gameplay.

Abstract: A system includes an image labeler to receive one or more images and associate one or more labels with each of the one or more images, a neural network generator to build a neural network from the one or more images and the one or more labels, a neural network compressor to compress the neural network into a compressed neural network, and a software development kit on a client device to receive and embed the compressed neural network, receive an input from the client device, interface with the compressed neural network to generate a result from the input and send the result to the client device to be displayed on a machine display of the client device.