Abstract: Methods, systems, and computer program products for active explanation guided learning are provided herein. A computer-implemented method includes identifying a subset of training examples, from a set of training examples, based on at least one of (i) an uncertainty metric computed for each one of the training examples and (ii) an influence metric computed for each one of the training examples; outputting said subset of training examples to a user; obtaining, from the user, a user explanation for each training example in said subset of training examples, wherein each of the user explanations identifies at least one part of the corresponding training example; and training a machine learning model based at least in part on the user explanations, wherein said training comprises prioritizing the identified parts of the training examples in the subset.

Abstract: A method, system, and computer program product for explaining predictions made by black box time series models. The method may include identifying a black box time series model. The method may also include predicting one or more time instances using the black box time series model. The method may also include selecting a predicted time instance from the predicted data. The method may also include receiving training data for the black box time series model. The method may also include generating a set of white box time series models similar to the black box time series model. The method may also include selecting a preferred white box time series model. The method may also include analyzing behavior of the preferred white box time series model. The method may also include generating an explanation illustrating why the black box time series model forecasted the predicted time instance.

Abstract: Methods, systems, and computer program products for active explanation guided learning are provided herein. A computer-implemented method includes identifying a subset of training examples, from a set of training examples, based on at least one of (i) an uncertainty metric computed for each one of the training examples and (ii) an influence metric computed for each one of the training examples; outputting said subset of training examples to a user; obtaining, from the user, a user explanation for each training example in said subset of training examples, wherein each of the user explanations identifies at least one part of the corresponding training example; and training a machine learning model based at least in part on the user explanations, wherein said training comprises prioritizing the identified parts of the training examples in the subset.

Abstract: Techniques are disclosed for generating a GUI model for an application based upon requirements information for an application. The requirements information may include an image of a GUI screen (or multiple images of multiple screens) included in the GUI for the application. The GUI model generated based upon the input image may include information about the type and placement (layout) of GUI components in the GUI screen, and may also include information indicative of one or more functions to be assigned to one or more of the GUI components in the GUI screen. The GUI model may then be used to generate an implementation of the application GUI, including generating executable code that implements the GUI screen.

Abstract: Techniques are disclosed for generating a GUI model for an application based upon requirements information for an application. The requirements information may include an image of a GUI screen (or multiple images of multiple screens) included in the GUI for the application. The GUI model generated based upon the input image may include information about the type and placement (layout) of GUI components in the GUI screen, and may also include information indicative of one or more functions to be assigned to one or more of the GUI components in the GUI screen. The GUI model may then be used to generate an implementation of the application GUI, including generating executable code that implements the GUI screen.