METHOD AND SYSTEM FOR QUANTIFYING UNCERTAINTIES IN OUTPUT DATA FROM A MACHINE LEARNING SYSTEM AND METHOD FOR TRAINING A MACHINE LEARNING SYSTEM

원문 (중국어)

A method for training a machine learning (ML) system to quantify uncertainties in output data is disclosed. Training data, including training input data and training target values, are used to adjust parameters of the ML system. The ML system, when the training input data are input, generates output data similar to the training target values; and generates reconstruction data representing a measure of familiarity of training data. The method relates to quantifying uncertainties (U) in output data (Y′) from the machine learning system () trained in accordance with the above method. The ML system generates output data from input data and generates the reconstruction data. A metric, the reconstruction data and data corresponding to the reconstruction data are used to generate a deviation value, which is a measure of familiarity of training data for the input data, quantifies the uncertainty and is assigned to the output data.