Abstract: A method for generating synthetic time series for augmenting a training data set of training time series used for training a machine learning model.

Abstract: A method for evaluating a degree of realism of synthetic training data for a machine learning model includes (i) providing the synthetic training data, wherein the synthetic training data is described by a statistical quantity, wherein the synthetic training data simulates sensor data, (ii) determining an upper limit of a confidence interval for the statistical value on the basis of the synthetic training data as part of a training of the machine learning model, (iii) providing real data, the real data also being described by the statistical variable, the real data comprising sensor data, the sensor data resulting from the detection of at least one sensor, (iv) determining a lower limit of the confidence interval for the statistical value on the basis of the real data in the context of an inference of the machine learning model, the lower limit being determined continuously from the start of the inference, and (v) checking the degree of realism of the synthetic training data on the basis of a comparison of th

Abstract: A method for validating an attribution-based explainability method for a machine learning system.

Abstract: A method for providing recipes for cooking to a user of a cooking device includes providing a trained recipe generator and/or training a recipe generator, receiving a trigger, especially a request, a query or a prompt, from the user of the cooking device, generating, based on the trigger, a new recipe using the recipe generator, and providing the new recipe to the user. A related data processing program product, a control unit of the cooking device, a related central entity and a system, are also provided.

Abstract: A method for making the function of a machine learning algorithm explainable, wherein the machine learning algorithm is designed to assign input data to one of at least two groups. The method includes: providing input data for the machine learning algorithm; for all of the input data provided, assigning the corresponding input data to one of the at least two groups by means of the machine learning algorithm; selecting data from a first group of the at least two groups; ascertaining, from a second group of at least two groups, data that are most similar to the selected data from all the data contained in the second group; comparing the selected data with the ascertained data to make the machine learning algorithm explainable; and providing corresponding comparison results.

Abstract: A method is for providing a process parameter model for parameterizing one or more process steps of a production process for manufacturing a component includes providing a quality model for determining a quality. The quality model is configured to specify the quality of the resulting component directly or with the aid of a predefined quality function based on one or more predefined measurement variables and/or one or more predefined state variables, which each specify a property of a pre-product or intermediate product of the component being manufactured and/or a production device for performing a process step and/or at least one environmental condition, and based on one or more process parameters which control a corresponding one of the process steps. The method further includes training a data-based process parameter model to output one or more process parameters based on one or more measurement variables captured by a sensor.

Abstract: A method for evaluating a training data set for a machine learning model includes (i) providing sensor data, wherein a portion of the sensor data comprises a detection feature, (ii) generating synthetic data by another machine learning model based on the portion of the sensor data having the detection feature, (iii) determining a ratio between a fraction of synthetic data and a fraction of sensor data having the detection feature for the training data set, and (iv) evaluating the training data set by way of the determined ratio based on at least one metric. Also disclosed is a computer program, device, and a storage medium for this purpose.

Abstract: A method is for detecting anomalies on a surface of an object and includes creating a depth profile of the surface of the object, and pre-processing the depth profile by approximating a shape along a spatial dimension and subsequently subtracting the approximated shape from the depth profile in order to obtain a simplified profile. The method further includes detecting the anomalies on the surface of the object by applying a machine learning algorithm to the simplified profile. The machine learning algorithm is trained in order to detect anomalies in depth profiles.

Abstract: A method for ascertaining an assignment rule in order to merge test results from different tests of the same semiconductor device. The method includes the following steps: adapting a model, e.g., a linear regression model, using the model to predict the test data; calculating costs based on the predictions; using a gradient descent method to minimize the costs.

Abstract: A method determines an assignment rule in order to combine test results from different tests of the same semiconductor device. The method includes fitting a model, such as a linear regression model, using the model to predict the test data, calculating a cost matrix based on the predictions, and applying the Hungarian method to the cost matrix to obtain a new assignment rule and repeating these steps multiple times.