Abstract: A system, method, and media are provided for generating a structured labeling graphical user interface. The user interface receives user input that associates multimedia content with categories. The user input may include user-supplied tags that further define the category for the multimedia content. The user-supplied tags are rendered proximate to the categories. In turn, a database logs user events to store, among other things, the categories, the user-supplied tags, time associated with completing the user-supplied tags, and time for associating multimedia content with the categories or tags.

Abstract: Multiple machine learning models can be jointly trained in parallel. An example process for jointly training multiple machine learning models includes providing a set of machine learning models that are to learn a respective task, the set of machine learning models including a first machine learning model and a second machine learning model. The process can initiate training of the first machine learning model to learn a task using training data. During the training of the first machine learning model, information can be passed between the first machine learning model and the second machine learning model. Such passing of information (or “transfer of knowledge”) between the machine learning models can be accomplished via the formulation, and optimization, of an objective function that comprises model parameters that are based on the multiple machine learning models in the set.

Abstract: Systems and methods of pre-launching applications in a computer system, said applications being likely to be activated by a user from a terminated and/or suspended process state, are disclosed. The pre-launching of an application may be based on the assessed probability of the application being activated—as well as the level of availability of system resources to affect such pre-launching. Applications may be pre-launched based on these and other conditions/considerations, designed to improve the user's experience of a quick launch of applications in the background. Several prediction models are presented to provide a good estimate of the likelihood of an application being activated by a user. Such prediction models may comprise an adaptive predictor (based on past application usage situations) and/or a switch rate predictor (based on historic data of an application being switched and, possibly, having a decay rate applied to such switch rate measure).

Abstract: A system, method, and media are provided for generating a structured labeling graphical user interface. The user interface receives user input that associates multimedia content with categories. The user input may include user-supplied tags that further define the category for the multimedia content. The user-supplied tags are rendered proximate to the categories. In turn, a database logs user events to store, among other things, the categories, the user-supplied tags, time associated with completing the user-supplied tags, and time for associating multimedia content with the categories or tags.

Abstract: Systems and methods of pre-launching applications in a computer system, said applications being likely to be activated by a user from a terminated and/or suspended process state, are disclosed. The pre-launching of an application may be based on the assessed probability of the application being activated—as well as the level of availability of system resources to affect such pre-launching. Applications may be pre-launched based on these and other conditions/considerations, designed to improve the user's experience of a quick launch of applications in the background. Several prediction models are presented to provide a good estimate of the likelihood of an application being activated by a user. Such prediction models may comprise an adaptive predictor (based on past application usage situations) and/or a switch rate predictor (based on historic data of an application being switched and, possibly, having a decay rate applied to such switch rate measure).