Abstract: In one embodiment, a method includes executing server operations for processing a data item, wherein the server operations are based on feature values associated with the data item, executing a heuristic to determine a heuristic result value based on the feature values, determining a prediction result by a machine-learning model based on the feature values and the heuristic result value, wherein the prediction result is associated with an assessment score indicating an effectiveness of the machine-learning model, invoking a feedback function configured to update the machine-learning model based on the prediction result and the assessment score, and updating the machine-learning model when resources on the server are available.

Abstract: In one embodiment, a method includes receiving a request to determine whether to perform an action, wherein the action is based on one or more feature values, generating a prediction of whether to perform the action, wherein the prediction is generated using a machine-learning model that is trained based on the feature values, a heuristic value based on the feature values, and one or more feedback scores based on corresponding past predictions generated by the machine-learning model, where the heuristic value indicates whether to perform the action based on one or more predetermined conditions that are based on the feature values, performing the action when the prediction indicates that the action is to be performed, receiving a feedback score that indicates a level of effectiveness of the prediction, and updating the machine-learning model based on the feedback score, the feature values, and the heuristic value.

Abstract: A method includes receiving location reports indicating locations of mobile devices associated with users of an internet platform, registering a count for each location report, determining, for each location report received from a mobile device, a recent location report received from the mobile device indicating a previous location and registering a transition for each of a paired location report and recent location report, corresponding to a pair of locations. The method includes counting a number of transitions corresponding to a particular pair of locations and determining common transitions by comparing the number of transitions to a threshold value.

Abstract: Techniques are disclosed relating to modifying an automatically predicted adjustment. In one embodiment, the automatically predicted adjustment may be adjusted, for example, based on a rule. The automatically predicted adjustment may be based on a machine learning prediction. A new image may be globally adjusted based on the modified automatically predicted adjustment.

Abstract: Techniques are disclosed relating to generating generic labels, translating generic labels to image pipeline-specific labels, and automatically adjusting images. In one embodiment, generic labels may be generated. Generic algorithm parameters may be generated based on training a regression algorithm with the generic labels. The generic labels may be translated to pipeline-specific labels, which may be usable to automatically adjust an image.

Abstract: Techniques are disclosed relating to automatically adjusting images. In one embodiment, an image may be automatically adjusted based on a regression model trained with a database of raw and adjusted images. In one embodiment, an image may be automatically adjusted based on a model trained by both a database of raw and adjusted images and a small set of images adjusted by a different user. In one embodiment, an image may be automatically adjusted based on a model trained by a database of raw and adjusted images and predicted differences between a user's adjustment to a small set of images and a predicted adjustment based on the database of raw and adjusted images.

Abstract: Techniques are disclosed relating to automatically adjusting images. In one embodiment, an image may be automatically adjusted based on a regression model trained with a database of raw and adjusted images. In one embodiment, an image may be automatically adjusted based on a model trained by both a database of raw and adjusted images and a small set of images adjusted by a different user. In one embodiment, an image may be automatically adjusted based on a model trained by a database of raw and adjusted images and predicted differences between a user's adjustment to a small set of images and a predicted adjustment based on the database of raw and adjusted images.

Abstract: Techniques are disclosed relating to modifying an automatically predicted adjustment. In one embodiment, the automatically predicted adjustment may be adjusted, for example, based on a rule. The automatically predicted adjustment may be based on a machine learning prediction. A new image may be globally adjusted based on the modified automatically predicted adjustment.