Abstract: A computer-implemented method for training a machine learning system. The method includes: ascertaining a first training time series of input signals and a desired training output signal which corresponds to the first training time series, the desired training output signal characterizing a desired classification and/or a desired regression result of the first training time series; ascertaining a first adversarial example when is an overlap between the first training time series and an ascertained first adversarial perturbation, a first noise value of the first adversarial perturbation is not greater than a specifiable threshold, and the specifiable threshold is based on the ascertained noise values of the training time series; ascertaining a training output signal for the first adversarial example using the machine learning system; and adapting at least one parameter of the machine learning system according to a gradient of a loss value.

Abstract: A computer-implemented machine learning system configured to ascertain an output signal based on a time series of input signals of a technical system. The output signal characterizes a classification and/or a regression result of at least one first operating state and/or at least one first operating variable of the technical system. The training of the machine learning system includes: ascertaining a first training time series of input signals from a plurality of training time series and a desired training output signal which corresponds to the first training time series; ascertaining a worst possible training time series which characterizes an overlap of the first training time series with an ascertained first noise signal; ascertaining a training output signal based on the worst possible training time series using the machine learning system; and adapting at least one parameter of the machine learning system according to a gradient of a loss value.