Generating ground truth for machine learning from time series elements
AssigneeTesla, Inc.
InventorsAshok Kumar Elluswamy, Matthew Bauch, Christopher Payne, Andrej Karpathy, Joseph Polin
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Abstract
Sensor data, including a group of time series elements, is received. A training data set is determined, including by determining for at least a selected time series element in the group of time series elements a corresponding ground truth. The corresponding ground truth is based on a plurality of time series elements in the group of time series elements. A processor is used to train a machine learning model using the training dataset.
Claims20
A method, comprising:
- receiving, by one or more processors, sensor data of a group of time series elements;
- determining, by the one or more processors, a training dataset, including by determining for at least a selected time series element in the group of time series elements a corresponding ground truth based on a plurality of time series elements in the group of time series elements;
- and training, by the one or more processors, using the training dataset, a machine learning model to output a predicted ground truth based on a single time series element, the training dataset comprising the selected time series element in the group of time series elements and the corresponding ground truth.
A system comprising:
- one or more processors;
- and a memory coupled with the one or more processors, wherein the memory is configured to provide the one or more processors with instructions which when executed cause the one or more processors to:
- receive sensor data of a group of time series elements;
- determine a training dataset, including by determining for at least a selected time series element in the group of time series elements a corresponding ground truth based on a plurality of time series elements in the group of time series elements;
- and train, using the training dataset, a machine learning model to output a predicted ground truth based on a single time series element, the training dataset comprising the selected time series element in the group of time series elements and the corresponding ground truth.
A non-transitory computer-readable storage medium comprising instructions which when executed by one or more processors, cause the one or more processors to:
- receive sensor data of a group of time series elements;
- determine a training dataset, including by determining for at least a selected time series element in the group of time series elements a corresponding ground truth based on a plurality of time series elements in the group of time series elements;
- and train, using the training dataset, a machine learning model to output a predicted ground truth based on a single time series element, the training dataset comprising the selected time series element in the group of time series elements and the corresponding ground truth.
Dependencies are inferred from the claim text.