Abstract: Methods and systems for performing concept-based adversarial generation with steerable and diverse semantics. One system includes an electronic processor configured to access an input image. The electronic processor is also configured to perform concept-based semantic image generation based on the input image. The electronic processor is also configured to perform concept-based semantic adversarial learning using a set of semantic latent spaces generated as part of performing the concept-based semantic image generation. The electronic processor is also configured to generate an adversarial image based on the concept-based semantic adversarial learning. The electronic processor is also configured to test a target model using the adversarial image.

Abstract: Methods and systems for performing function testing for moveable objects. One system includes an electronic processor configured to access a driving scene including a moveable object. The electronic processor is also configured to perform spatial representation learning on the driving scene. The electronic processor is also configured to generate an adversarial example based on the learned spatial representation. The electronic processor is also configured to retrain the deep learning model using the adversarial example and the driving scene.

Abstract: A visual analytics workflow and system are disclosed for assessing, understanding, and improving deep neural networks. The visual analytics workflow advantageously enables interpretation and improvement of the performance of a neural network model, for example an image-based objection detection and classification model, with minimal human-in-the-loop interaction. A data representation component extracts semantic features of input image data, such as colors, brightness, background, rotation, etc. of the images or objects in the images. The input image data are passed through the neural network to obtain prediction results, such as object detection and classification results. An interactive visualization component transforms the prediction results and semantic features into interactive and human-friendly visualizations, in which graphical elements encoding the prediction results are visually arranged depending on the extracted semantic features of input image data.

Abstract: Mechanisms are provided to implement a brand personality inference engine. The mechanisms receive crowdsource information and extract features associated with a brand from the crowdsource information. The crowdsource information comprises natural language content submitted by a plurality of providers to a crowdsource information source. The mechanisms analyze features associated with the brand in accordance with a brand personality model configured to predict a brand personality for the brand based on the features associated with the brand. The mechanisms generate an inferred brand personality data structure, representing a perceived brand personality of providers providing the crowdsource information, and output an output indicating aspects of the perceived brand personality based on the inferred brand personality data structure.

Abstract: A system includes a camera configured to obtain image information from objects. The system also includes a processor in communication with the camera and programmed to receive an input data including the image information, encode the input via an encoder, obtain a latent variable defining an attribute of the input data, generate a sequential reconstruction of the input data utilizing at least the latent variable and an adversarial noise, obtain a residual between the input data and the sequential reconstruction utilizing a comparison of at least the input and the reconstruction to learn a mean shift in latent space, and output a mean shift indicating a test result of the input compared to the adversarial noise based on the comparison.

Abstract: A system includes a camera configured to obtain image information from objects. The system also includes a processor in communication with the camera and programmed to receive an input data including the image information, encode the input via an encoder, obtain a latent variable defining an attribute of the input data, generate a sequential reconstruction of the input data utilizing at least the latent variable and an adversarial noise, obtain a residual between the input data and the sequential reconstruction utilizing a comparison of at least the input and the reconstruction to learn a mean shift in latent space, and output a mean shift indicating a test result of the input compared to the adversarial noise based on the comparison.

Abstract: A method includes extracting, by an analysis computer, a plurality of first datasets from a plurality of graph snapshots using a structural self-attention module. The analysis computer can then extract at least a second dataset from the plurality of first datasets using a temporal self-attention module across the plurality of graph snapshots. The analysis computer can then perform graph context prediction with at least the second dataset.

Abstract: A system and method for scoring an interaction using an analytical model and authorization decisions is disclosed. The method includes receiving from an access device an authorization request message for an interaction between a user and a resource provider. An analytical model comprising a neural network with at least one long short-term memory determines a score based on data in the authorization request message. The analytical model was formed using interaction data from prior authorization request messages, and authorization response messages from an authorizing computer. The authorization request message and the score is transmitted to the authorizing computer and an authorization response message, including an indication of whether the interaction was approved or declined, is received. Then the analytical model is updated based upon data in the authorization request message and the indication in the authorization response message to form an updated analytical model.

Abstract: A computer system can perform a semi-supervised machine learning processes to cluster a plurality of entities within a population based on their features and associated labels. The computer system can generate visualization data representing the clusters of entities and associated labels for displaying on a user interface. A user can review the clustering of entities and use the user interface to add or modify the labels associated with a particular entity or set of entities. The computer system can use the user's feedback to update the labels and then re-determine the clustering of entities using the semi-supervised machine learning process with the updated labels as input. As such, the computer system can use the user's feedback to improve the accuracy of the machine learning model without requiring a larger amount of labeled input data.

Abstract: Visual analytics tool for updating object detection models in autonomous driving applications. In one embodiment, an object detection model analysis system including a computer and an interface device. The interface device includes a display device. The computer includes an electronic processor that is configured to extract object information from image data with a first object detection model, extract characteristics of objects from metadata associated with image data, generate a summary of the object information and the characteristics, generate coordinated visualizations based on the summary and the characteristics, generate a recommendation graphical user interface element based on the coordinated visualizations and a first one or more user inputs, and update the first object detection model based at least in part on a classification of one or more individual objects as an actual weakness in the first object detection model to generate a second object detection model for autonomous driving.

Abstract: A computer system can perform a semi-supervised machine learning processes to cluster a plurality of entities within a population based on their features and associated labels. The computer system can generate visualization data representing the clusters of entities and associated labels for displaying on a user interface. A user can review the clustering of entities and use the user interface to add or modify the labels associated with a particular entity or set of entities. The computer system can use the user's feedback to update the labels and then re-determine the clustering of entities using the semi-supervised machine learning process with the updated labels as input. As such, the computer system can use the user's feedback to improve the accuracy of the machine learning model without requiring a larger amount of labeled input data.

Abstract: Provided are systems for analyzing a relational database using embedding learning that may include at least one processor programmed or configured to generate one or more entity-relation matrices from a relational database and perform, for each entity-relation matrix of the one or more entity-relation matrices, an embedding learning process on an embedding associated with an entity. When performing the embedding learning process on the embedding associated with the entity, the at least one processor is programmed or configured to generate an updated embedding associated with the entity. Computer implemented methods and computer-program products are also provided.

Abstract: An approach is provided that provides a tone optimization recommendation. The approach obtains a current tone inferred from digital content and a desired tone inference for a target audience. A tone optimization recommendation to reduce a difference between the current tone and the desired tone is determined using a processor. A memory is modified to save the tone optimization recommendation. The tone optimization recommendation is provided.

Abstract: A system and method for scoring an interaction using an analytical model and authorization decisions is disclosed. The method includes receiving from an access device an authorization request message for an interaction between a user and a resource provider. An analytical model comprising a neural network with at least one long short-term memory determines a score based on data in the authorization request message. The analytical model was formed using interaction data from prior authorization request messages, and authorization response messages from an authorizing computer. The authorization request message and the score is transmitted to the authorizing computer and an authorization response message, including an indication of whether the interaction was approved or declined, is received. Then the analytical model is updated based upon data in the authorization request message and the indication in the authorization response message to form an updated analytical model.

Abstract: A system, method, and computer program product for incorporating knowledge from more complex models in simpler models. A method may include obtaining first training data associated with a first set of features and second training data associated with a second set of features different than the first set of features; training a first model based on the first training data and the second training data; and training a second model, using a loss function that depends on an output of an intermediate layer of the first model and an output of the second model, based on the second training data.

Abstract: Mechanisms are provided to implement a brand comparison engine. The mechanisms receive a request to compare brand personalities of a first specified brand and a second specified brand and obtain a first brand personality scale associated with the first specified brand and a second brand personality scale associated with the second specified brand. The mechanisms calculate at least one gap value indicating a difference between at least one personality trait in the first brand personality scale and a corresponding at least one personality trait in the second brand personality scale. The mechanisms also output an output indicating an aspect of the at least one gap based on the calculation.

Abstract: A method for local approximation of a predictive model may include receiving unclassified data associated with a plurality of unclassified data items. The unclassified data may be classified based on a first predictive model to generate classified data. A first data item may be selected from the classified data. A plurality of generated data items associated with the first data item may be generated using a generative model. The plurality of generated data items may be classified based on the first predictive model to generate classified generated data. A second predictive model may be trained with the classified generated data. A system and computer program product are also disclosed.

Abstract: Mechanisms are provided to implement a brand comparison engine. The mechanisms receive a request to compare brand personalities of a first specified brand and a second specified brand and obtain a first brand personality scale associated with the first specified brand and a second brand personality scale associated with the second specified brand. The mechanisms calculate at least one gap value indicating a difference between at least one personality trait in the first brand personality scale and a corresponding at least one personality trait in the second brand personality scale. The mechanisms also output an output indicating an aspect of the at least one gap based on the calculation.

Abstract: Provided is a method for implementing reinforcement learning by a neural network. The method may include performing, for each epoch of a first predetermined number of epochs, a second predetermined number of training iterations and a third predetermined number of testing iterations using a first neural network. The first neural network may include a first set of parameters, the training iterations may include a first set of hyperparameters, and the testing iterations may include a second set of hyperparameters. The testing iterations may be divided into segments, and each segment may include a fourth predetermined number of testing iterations. A first pattern may be determined based on at least one of the segments. At least one of the first set of hyperparameters or the second set of hyperparameters may be adjusted based on the pattern. A system and computer program product are also disclosed.

Abstract: Mechanisms are provided to implement a brand personality perception gap assessment engine. The mechanisms receive an inferred brand personality for a specified brand. The mechanisms further receive an intended brand personality for the specified brand. The mechanisms calculate at least one gap between the inferred brand personality and the intended brand personality. The mechanisms output an output indicating aspects of the at least one gap.