Abstract: A method includes receiving input data at a trained machine learning model that includes a common part and task-specific parts, receiving an execution instruction that identifies one or more processing tasks to be performed, processing the input data using the common part of the trained machine learning model to generate intermediate data, and processing the intermediate data using one or more of the task-specific parts of the trained machine learning model based on the execution instruction to generate one or more outputs.

Abstract: In various embodiments described herein, a content extension and programming interface enable third-party content extensions to supply transformation and filtering actions and associated criteria to native web clients on a system. In one embodiment, the native web client loads data for a third-party content filtering, blocking, or transformation extension and conducts extension defined actions without requiring additional intermediation by the third-party content. In one embodiment, a third-party application downloaded to a mobile or handheld device can include an extension data object that provides actions and triggers that are loaded by a native web client of the system. The third-party application is not required to execute continuously and can exit or be terminated once the web client retrieves the extension data object.

Abstract: Artistic styles extracted from one or more source images may be applied to one or more target images, e.g., in the form of stylized images and/or stylized video sequences. The extracted artistic style may be stored as a plurality of layers in a neural network, which neural network may be further optimized, e.g., via the fusion of various elements of the network's architectures. An optimized network architecture may be determined for each processing environment in which the network will be applied. The artistic style may be applied to the obtained images and/or video sequence of images using various optimization methods, such as the use of scalars to control the resolution of the unstylized and stylized images, temporal consistency constraints, as well as the use of dynamically adjustable or selectable versions of Deep Neural Networks (DNN) that are responsive to system performance parameters, such as available processing resources and thermal capacity.

Abstract: A method includes receiving input data at a trained machine learning model that includes a common part and task-specific parts, receiving an execution instruction that identifies one or more processing tasks to be performed, processing the input data using the common part of the trained machine learning model to generate intermediate data, and processing the intermediate data using one or more of the task-specific parts of the trained machine learning model based on the execution instruction to generate one or more outputs.

Abstract: In various embodiments described herein, a content extension and programming interface enable third-party content extensions to supply transformation and filtering actions and associated criteria to native web clients on a system. In one embodiment, the native web client loads data for a third-party content filtering, blocking, or transformation extension and conducts extension defined actions without requiring additional intermediation by the third-party content. In one embodiment, a third-party application downloaded to a mobile or handheld device can include an extension data object that provides actions and triggers that are loaded by a native web client of the system. The third-party application is not required to execute continuously and can exit or be terminated once the web client retrieves the extension data object.

Abstract: In various embodiments described herein, a content extension and programming interface enable third-party content extensions to supply transformation and filtering actions and associated criteria to native web clients on a system. In one embodiment, the native web client loads data for a third-party content filtering, blocking, or transformation extension and conducts extension defined actions without requiring additional intermediation by the third-party content. In one embodiment, a third-party application downloaded to a mobile or handheld device can include an extension data object that provides actions and triggers that are loaded by a native web client of the system. The third-party application is not required to execute continuously and can exit or be terminated once the web client retrieves the extension data object.

Abstract: In various embodiments described herein, a content extension and programming interface enable third-party content extensions to supply transformation and filtering actions and associated criteria to native web clients on a system. In one embodiment, the native web client loads data for a third-party content filtering, blocking, or transformation extension and conducts extension defined actions without requiring additional intermediation by the third-party content. In one embodiment, a third-party application downloaded to a mobile or handheld device can include an extension data object that provides actions and triggers that are loaded by a native web client of the system. The third-party application is not required to execute continuously and can exit or be terminated once the web client retrieves the extension data object.

Abstract: Implementations are disclosed for activating hardware on a device preemptively based on whether a service associated with the hardware will be required or likely be used by an application, or if the application's access to the service is granted by a user of the application. The disclosed implementations provide a perceived improvement of performance by applications supported by slow starting hardware.