Abstract: A method for determining for which inputs records of measurement data the processing by a neural network may be cut short by obtaining the output from an early-exit point of the neural network, rather than by traversing the whole neural network. The method includes: providing a set of calibration records of measurement data; processing the calibration records by the full neural network to obtain reference outputs; recording one or more early-exit outputs that the neural network outputs for the calibration records at one or more early-exit points, and respective confidences of the early-exit outputs; providing a set of predetermined conditions that are each dependent both on early-exit outputs and on reference outputs; and evaluating one or more thresholds for the confidences of the early-exit outputs such that, if the confidences exceed the thresholds, the respective early-exit outputs can be expected to meet the conditions.

Abstract: A computer-implemented method for classifying data elements of a data set using a machine-learning model. The method includes classifying the data elements of the data set associated with a time sequence, each data element being associated with a corresponding time step of the time sequence, the data elements being classified by inputting them into the machine-learning model one after the other according to their temporal order. Classifying a respective data element includes: determining features of the respective data element using a feature extractor of the machine-learning model; determining, using the features of the respective data element and the features of one or more other data elements temporally preceding the respective data element, parameters of a feature-dynamics-model which represents an evolution of a feature density of the features over time; and determining a class associated with the respective data element using the feature-dynamics-model.

Abstract: A device, data structure, and method for determining a prediction interval for a coordinate of a bounding box, for checking whether an object detector operates safely or not. The method includes providing calibration data and a test sample, the calibration data including digital images that are associated with a respective ground truth bounding box coordinate and class label, the test sample including a digital image; determining, with the object detector, a predicted box coordinate for the box coordinate depending on the test sample, determining, depending on the calibration data, conformal label quantiles for the respective classes and conformal box coordinate quantiles for the respective box coordinates, selecting, depending on the conformal label quantiles, a conformal box coordinate quantile for the box coordinate, determining the conformal box coordinate prediction interval for the box coordinate depending on the conformal box quantile for the box coordinate.

Abstract: Device and computer-implemented methods for determining a multi-dimensional quantity that characterizes an object in an environment of a technical system, or a technical system, for operating a technical system, and for providing a quantity prediction system. The method includes providing at least one quantity that characterizes the technical system, or an environment of the technical system, predicting, depending on the at least one quantity, a first prediction of the multi-dimensional quantity, providing a multi-dimensional parameter that defines a prediction interval, determining a second prediction of the multi-dimensional quantity depending on the first prediction of the multi-dimensional quantity and the multi-dimensional parameter, wherein the second multi-dimensional prediction determines the multi-dimensional quantity.

Abstract: A method for predicting the performance of a given classifier with respect to one or more given samples of input data. The method includes: providing further classifiers that, together with the given classifier f*, form a set F of classifiers f; computing, for the x, using each classifier f from the set F, classification scores fk(x) with respect to all available classes k=1, . . . , K covered by the classifiers; determining, for pairs (f,f?) of classifiers f and f? from the set F, divergences of the classification scores fk(x) and f?k(x) for all k=1, . . . , K as pairwise divergences dis(f,f?,x) of the classifiers f and f? with respect to the one or more samples x; and determining the performance P(f*,x) of the classifier f* with respect to the one or more samples x based at least in part on pairwise divergences dis(f*,f?,x) between the classifier f* and other classifiers f?.

Abstract: A computer-implemented method for determining a first element and a second element. The first element characterizes a classification or a regression result of a sensor signal, and the second element characterizes a confidence interval of likely classifications or regression results. The first element and second element are determined by an early-exit neural network.

Abstract: A device and a computer-implemented method for machine learning. The method includes: providing an input for a model, determining with the model a first classification that indicates a class for the input, determining with the model depending on the input a likelihood that an expert determines a correct classification for the input, determining with the model depending on the input a likelihood that the input is in-distribution data or out-of-distribution data with respect to a distribution of data that the model is trained on, determining a second classification that indicates whether the input is considered as in-distribution data or out-of-distribution data with respect to the distribution of data that the model is trained on depending on the first classification and depending on the likelihoods, determining an output of the model depending on the first classification and the second classification, and outputting the output.

Abstract: Generating intermediate predictions in a trained machine learning model. The trained machine learning models may include one or more input layers, multiple intermediate layers, and one or more output layers, which generate an output. A computer-implemented method may perform classification using the trained machine learning model, which includes the steps of feeding input data to the trained machine learning model, propagating the input data through a part of the trained machine learning model, obtaining intermediate predictions from the layers in the part of the trained machine learning model, ensembling these intermediate predictions to obtain an ensemble prediction, and using the ensemble prediction as a substitute for the output of the trained machine learning model in the classification. The ensembling phase may include determining the ensemble prediction as a product of weighted versions of the obtained intermediate predictions, and having a normalized probability density.

Abstract: The present disclosure is directed to generating and using a machine learning model, such as a neural network, by augmenting another machine learning model with an additional parameter. The additional parameter may be connected to some or all nodes of an internal layer of the neural network. A machine learning model can determine a value associated with the additional parameter using non-behavior or non-event-based information. The machine learning model can be trained using non-behavior or non-event-based information and parameter values of the other machine learning model.