Abstract: Systems and methods for detecting and correcting defective products include capturing at least one image of a product with at least one image sensor to generate an original image of the product. An encoder encodes portions of an image extracted from the original image to generate feature space vectors. A decoder decodes the feature space vectors to reconstruct the portions of the image into reconstructed portions by predicting defect-free structural features in each of the portions according to hidden layers trained to predict defect-free products. Each of the reconstructed portions are merged into a reconstructed image of a defect-free representation of the product. The reconstructed image is communicated to a contrastor to detect anomalies indicating defects in the product.

Abstract: Systems and methods for diagnosing a patient condition include a medical imaging device for generating an anatomical image. A reconstructor reconstructs the anatomical image by reconstructing portions of the anatomical image to be a healthy representation of the portions and merging the portions into the anatomical image to generate a reconstructed image. A contrastor contrasts the anatomical image with the reconstructed image to generate an anomaly map indicating locations of difference between the anatomical image and the reconstructed image. An anomaly tagging device tags the locations of difference as anomalies corresponding to anatomical abnormalities in the anatomical image, and a display displays the anatomical image with tags corresponding to the anatomical abnormalities.

Abstract: Systems and methods for assigning labels to an object are provided. A set of labels is assigned to at least one object. A label filter is applied to the set of labels and to the object and labels that are irrelevant to the object are identified by the label filter. The irrelevant labels are then eliminated from further consideration and a subset of labels are created. Labels from the subset of labels that are relevant to the object are then determined and assigned to the object. The elimination of the irrelevant labels increases the speed of the labeling process. A determination of which of the objects are relevant to a user may then be performed to create user-oriented content.

Abstract: Systems and methods for assigning labels to an object are provided. The method includes: receiving a set of labels and at least one object to be assigned labels; applying a label filter to the set of labels and to each of the at least one objects; identifying which labels in the set of labels are irrelevant; eliminating labels in the set of labels that are irrelevant to each of the at least one objects from further consideration; creating a subset of labels; determining which labels in the subset of labels are relevant to the object; assigning, to each of the at least one objects, the labels that are relevant to the object; determining which of the objects are relevant to a user to create user-oriented content, the user-oriented content consisting of at least one object determined to be relevant to a user; and displaying the user-orientated content to the user.

Abstract: Methods and systems for displaying dependencies within data and illustrating differences between a plurality of data sets are disclosed. In accordance with one such method, a plurality of data sets are received for the generation of a plurality of dependency networks in accordance with a graphical modeling scheme. The method further includes receiving a selection of a value of a parameter that adjusts a number of differences between the dependency networks in accordance with the graphical modeling scheme. In addition, at least one version of the dependency networks is generated based on the selected value of the parameter. Further, the one or more versions of the dependency networks is output to permit a user to analyze distinctions between the dependency networks.

Abstract: Disclosed are methods and structures of Multiple Kernel learning framed as a standard binary classification problem with additional constraints that ensure the positive definiteness of the learned kernel. Advantageously, the disclosed methods and structures permit the use of binary classification technologies to develop better performing, and more scalable Multiple Kernel Learning methods that are conceptually simpler.

Abstract: Methods and systems for displaying dependencies within data and illustrating differences between a plurality of data sets are disclosed. In accordance with one such method, a plurality of data sets are received for the generation of a plurality of dependency networks in accordance with a graphical modeling scheme. The method further includes receiving a selection of a value of a parameter that adjusts a number of differences between the dependency networks in accordance with the graphical modeling scheme. In addition, at least one version of the dependency networks is generated based on the selected value of the parameter. Further, the one or more versions of the dependency networks is output to permit a user to analyze distinctions between the dependency networks.

Abstract: Methods and systems for displaying dependencies within data and illustrating differences between a plurality of data sets are disclosed. In accordance with one such method, a plurality of data sets are received for the generation of a plurality of dependency networks in accordance with a graphical modeling scheme. The method further includes receiving a selection of a value of a parameter that adjusts a number of differences between the dependency networks in accordance with the graphical modeling scheme. In addition, at least one version of the dependency networks is generated based on the selected value of the parameter. Further, the one or more versions of the dependency networks is output to permit a user to analyze distinctions between the dependency networks.