Abstract: An example embodiment of the present techniques receives a plurality of object detections, each object detection including an identifier. A processor may detect that a threshold number of object detections with a same identifier has been exceeded. The processor may also construct a graph including at least one connected component. Each connected component includes object detections with the same identifier that do not exceed a distance threshold between each other as vertices connected by edges. The processor may also further merge vertices in the connected component to generate a merged detection.

Abstract: The disclosed embodiments provide a system for forecasting job applications. During operation, the system applies a first machine learning model to features that include a budget for a job to predict a number of impressions of the job. Next, the system applies a second machine learning model to additional features for the job to estimate an application rate for the job. The system then determines a distribution of applications to the job based on the number of impressions and the application rate. Finally, the system outputs one or more values from the distribution of applications to the job as guidance for setting the budget for the job.

Abstract: The disclosed embodiments provide a system for performing calibration of response rates. During operation, the system obtains a position of a content item in a ranking of content items generated for delivery to a member of an online system and a predicted response rate by the member to the content item. Next, the system determines an updated response rate by the member to the content item based on the position of the content item in the ranking and dimensions associated with the predicted response rate and the ranking. The system then outputs the updated response rate for use in managing delivery of the content item.

Abstract: The disclosed embodiments provide a system for forecasting job applications. During operation, the system applies a first machine learning model to features that include a budget for a job to predict a number of impressions of the job. Next, the system applies a second machine learning model to additional features for the job to estimate an application rate for the job. The system then determines a distribution of applications to the job based on the number of impressions and the application rate. Finally, the system outputs one or more values from the distribution of applications to the job as guidance for setting the budget for the job.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for automated profile image generation based on scheduled video conferences. A profile image generation system generates, based on image data captured during a first video conference, a first facial feature data set for a first identified face identified from the image data. The first facial feature data set includes numeric values representing the first identified face. The profile image generation system calculates, based on the first facial feature data set and historic facial feature data sets generated from image data captured during previous video conferences, a first value indicating a likelihood that the first identified face is a first meeting participant that participated in the first video conference. The profile image generation system determines that the first value meets or exceeds a threshold value, and in response, determines that the first identified face is the first meeting participant.

Abstract: Methods, systems, and computer programs are presented for calculating a hyperparameter value set for training a machine-learning program (MLP). One method includes an operation for identifying a model for the MLP that comprises hyperparameter value sets to be tested based on a dataset that has performance data for features of the MLP. The method further includes operations for breaking the dataset into fragments for evaluating the model with a graphics processing unit (GPU) and for loading the cores of the GPU with the model and a respective hyperparameter value set. Each fragment of the dataset is streamed to a GPU memory, and the cores of the GPU evaluate, in parallel, the fragment based on the model and the respective hyperparameter value set. Further, the method includes operations for determining, storing, and presenting the best hyperparameter value set for the MLP.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for automated profile image generation based on scheduled video conferences. A profile image generation system generates, based on image data captured during a first video conference, a first facial feature data set for a first identified face identified from the image data. The first facial feature data set includes numeric values representing the first identified face. The profile image generation system calculates, based on the first facial feature data set and historic facial feature data sets generated from image data captured during previous video conferences, a first value indicating a likelihood that the first identified face is a first meeting participant that participated in the first video conference. The profile image generation system determines that the first value meets or exceeds a threshold value, and in response, determines that the first identified face is the first meeting participant.

Abstract: The disclosed embodiments provide a system for managing the execution of a statistical model. During operation, the system tracks a distribution of one or more metrics related to a performance of a first version of a statistical model. When a deviation in the distribution is detected, the system outputs an alert of an anomaly in the performance of the statistical model. The system also triggers a rollback to a second version of the statistical model.

Abstract: An example device in accordance with an aspect of the present disclosure includes an initialization engine and a system usage engine. The initialization engine is to generate a collection of signatures representing canonical data and structure in canonical data. The system usage engine is to create a generated signature of a transformed datum, compare the generated signature to the collection of signatures, and identify a class of the generated signature based on the comparison.