Abstract: The present disclosure provides a method and system for adapting a machine learning model, such as an object detection model, to account for domain shift. The method includes receiving a labeled data elements and target image samples and performing a plurality of model adaptation epochs. Each adaptation epoch includes: predicting for each of the target image samples, using the machine learning model configured by a current set of configuration parameters, a corresponding target class label for the respective target data object included in the target image sample; generating a plurality of labeled mixed data elements that each include: (i) a mixed image sample including a source data object from one of the source image samples and a target data object from one of the target image samples, and (ii) the corresponding source class label for the source data object and the corresponding target class label for the target data object.

Abstract: The present disclosure provides a computer implemented method and system for generating an algebraic modelling language (AML) formulation of natural language text description of an optimization problem. The computer implemented method includes generating, based on the natural language text description, a text markup language intermediate representation (IR) of the optimization problem, the text markup language IR including an IR objective declaration that defines an objective for the optimization problem and a first IR constraint declaration that indicates a first constraint for the optimization problem. The computer implemented also includes generating, based on the text markup language IR, the AML formulation of the optimization problem, the AML formulation including an AML objective declaration that defines the objective for the optimization problem and a first AML constraint declaration that indicates the first constraint for the optimization problem.