Abstract: Provided are aspects relating to methods and computing devices for allocating computing resources and selecting hyperparameter configurations during continuous retraining and operation of a machine learning model. In one example, a computing device configured to be located at a network edge between a local network and a cloud service includes a processor and a memory storing instructions executable by the processor to operate a machine learning model. During a retraining window, a selected portion of a video stream is selected for labeling. At least a portion of a labeled retraining data set is selected for profiling a superset of hyperparameter configurations. For each configuration of the superset of hyperparameter configurations, a profiling test is performed. The profiling test is terminated, and a change in inference accuracy that resulted from the profiling test is extrapolated. Based upon the extrapolated inference accuracies, a set of selected hyperparameter configurations is output.

Abstract: Examples are disclosed that relate to methods and computing devices for allocating computing resources and selecting hyperparameter configurations during continuous retraining and operation of a machine learning model. In one example, a computing device configured to be located at a network edge between a local network and a cloud service comprises a processor and a memory storing instructions executable by the processor to operate a machine learning model. During a retraining window, a selected portion of a video stream is selected for labeling. At least a portion of a labeled retraining data set is selected for profiling a superset of hyperparameter configurations. For each configuration of the superset of hyperparameter configurations, a profiling test is performed. The profiling test is terminated, and a change in inference accuracy that resulted from the profiling test is extrapolated. Based upon the extrapolated inference accuracies, a set of selected hyperparameter configurations is output.

Abstract: Methods and computing devices for allocating computing resources and selecting hyperparameter configurations during continuous retraining and operation of a machine learning model. In one example, a computing device configured to be located at a network edge between a local network and a cloud service includes a processor and a memory storing instructions executable by the processor to operate a machine learning model. During a retraining window, a selected portion of a video stream is selected for labeling. At least a portion of a labeled retraining data set is selected for profiling a superset of hyperparameter configurations. For each configuration of the superset of hyperparameter configurations, a profiling test is performed. The profiling test is terminated, and a change in inference accuracy that resulted from the profiling test is extrapolated. Based upon the extrapolated inference accuracies, a set of selected hyperparameter configurations is output.

Abstract: Examples are disclosed that relate to methods and computing devices for allocating computing resources and selecting hyperparameter configurations during continuous retraining and operation of a machine learning model. In one example, a computing device configured to be located at a network edge between a local network and a cloud service comprises a processor and a memory storing instructions executable by the processor to operate a machine learning model. During a retraining window, a selected portion of a video stream is selected for labeling. At least a portion of a labeled retraining data set is selected for profiling a superset of hyperparameter configurations. For each configuration of the superset of hyperparameter configurations, a profiling test is performed. The profiling test is terminated, and a change in inference accuracy that resulted from the profiling test is extrapolated. Based upon the extrapolated inference accuracies, a set of selected hyperparameter configurations is output.

Abstract: This document relates to camera calibration. One example uses real-world distances and image coordinates of object features in images to determine multiple candidate camera calibrations for a camera.

Abstract: This document relates to camera calibration. One example uses real-world distances and image coordinates of object features in images to determine multiple candidate camera calibrations for a camera.

Abstract: A “Skip-Correlator” ensures carrier sensing symmetry between wireless devices of arbitrary transmission power levels, thereby enabling fair sharing of available spectrum of a wireless channel between the wireless devices. In various implementations, receivers of a wireless device receive wireless preambles from neighboring wireless transmitters. This preamble includes an indication of a transmission power level of the neighboring wireless transmitter. The wireless device then selects and correlates a subset of samples of the received preamble as a function of a transmission power level of that wireless device. The remaining samples of the preamble are skipped by the receiving wireless device, hence the use of the term “Skip-Correlator.” Further, the wireless device computes a total preamble energy of the correlated samples of the received preamble.

Abstract: A “Skip-Correlator” ensures carrier sensing symmetry between wireless devices of arbitrary transmission power levels, thereby enabling fair sharing of available spectrum of a wireless channel between the wireless devices. In various implementations, receivers of a wireless device receive wireless preambles from neighboring wireless transmitters. This preamble includes an indication of a transmission power level of the neighboring wireless transmitter. The wireless device then selects and correlates a subset of samples of the received preamble as a function of a transmission power level of that wireless device. The remaining samples of the preamble are skipped by the receiving wireless device, hence the use of the term “Skip-Correlator.” Further, the wireless device computes a total preamble energy of the correlated samples of the received preamble.