Method and system for estimating output uncertainty for deterministic artificial neural network
AssigneeKorea Advanced Institute of Science and Technology
InventorsSang Wan LEE, Yujin Cha
Life of this patent
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Abstract
Disclosed is a method and system for estimating output uncertainty of a deterministic artificial neural network (ANN). An output uncertainty estimation method of a deterministic ANN may include generating a dataset by combining training data used for training of a deterministic ANN model and output of the deterministic ANN model trained with the training data; and estimating output uncertainty of the deterministic ANN model based on output for test data of a proxy Gaussian process model trained through the generated dataset.
Claims20
An output uncertainty estimation method of a computer device including at least one processor, the output uncertainty estimation method comprising:
- generating, by the at least one processor, a dataset by combining training data used for training of a deterministic artificial neural network (ANN) model and output of the deterministic ANN model trained with the training data;
- and
- estimating, by the at least one processor, output uncertainty of the deterministic ANN model based on output for test data of a proxy Gaussian process model trained through the generated dataset.
A computer device comprising:
- at least one processor configured to execute computer-readable instructions,
- wherein the at least one processor is configured to
- generate a dataset by combining training data used for training of a deterministic artificial neural network (ANN) model and output of the deterministic ANN model trained with the training data, and
- estimate output uncertainty of the deterministic ANN model based on output for test data of a proxy Gaussian process model trained through the generated dataset.
Dependencies are inferred from the claim text.