Abstract: A method is disclosed for operating a technical apparatus with an electronic converter controlled a control signal. A control signal profile is provided with which the electronic converter is to be operated. A predicted control signal profile is predicted based on the provided control signal profile. The predicted control signal profile is a predicted future profile of the control signal. A modified control signal profile of is obtained by modifying the provided control signal profile using a trainable, data-based control signal model. The control signal model is trained to determine the modified control signal profile based on the provided control signal profile and the predicted control signal profile. The electronic converter is operated using the modified control signal profile.

Abstract: A computer-implemented method and system for training an anomaly detector to distinguish outlier data from inlier data on which the anomaly detector is trained. The anomaly detector comprises a set of learnable data transformations and a learnable feature extractor. The set of learnable data transformations and the learnable feature extractor are jointly trained based on a trained objective, which training objective comprises a function serving as anomaly scoring function which may also be used at test time to determine the anomaly score of test data samples. Evaluation results show that the anomaly detector is well-applicable to detect anomalies in non-image data, e.g., in data timeseries and in tabular data, and straightforward to apply at test time.

Abstract: A computer-implemented method for training a machine learning system. The method includes: providing at least one training data set that includes a number of numerical vectors; propagating numerical values of the at least one training data set by a parameterizable generic flow-based model, the parameterizable generic flow-based model including a concatenation of at least two parameterizable submodules, each submodule being one parameterizable function each; and learning the model parameter of the parameterizable generic flow-based model; parameterizations of each parameterizable submodule being learned successively in the flow direction and being fixed before parameterizations of the parameterizable submodule next in the flow direction are learned, and the learning being directed at output data of each submodule being distributed according to a predetermined probability distribution.

Abstract: A computer-implemented method for training a deep state space model using machine learning. The deep state space model includes a generative model and a multi-modal inference model. The generative model includes a transition model, and an emission model. The method includes: a) receiving a training data set comprising a sequence of observation vectors. For a plurality of observation vectors, the method iterates between b), c), and d) in sequence: b) inferring, using the multi-modal inference model, a current latent state of the generative model; c) constructing, using the multi-modal inference model, a posterior approximation of the current latent state as a mixture density network. For a plurality of observation vectors comprised in the sequence of observation vectors, d) decoding, using the emission model, the plurality of approximated latent state vectors to provide a plurality of synthetic observations; and e) outputting the trained deep state space model.

Abstract: A method is disclosed for operating a technical apparatus with an electronic converter controlled a control signal. A control signal profile is provided with which the electronic converter is to be operated. A predicted control signal profile is predicted based on the provided control signal profile. The predicted control signal profile is a predicted future profile of the control signal. A modified control signal profile of is obtained by modifying the provided control signal profile using a trainable, data-based control signal model. The control signal model is trained to determine the modified control signal profile based on the provided control signal profile and the predicted control signal profile. The electronic converter is operated using the modified control signal profile.

Abstract: A system for processing a model. The model provides a model output given an input instance. The model has been trained on a training dataset by iteratively optimizing an objective function including losses according to a loss function for training instances of the training dataset. Upon receiving a removal request message identifying one or more undesired training instances of the training dataset, the model is made independent from the one or more undesired training instances. To this end, the one or more undesired training instances are removed from the training dataset to obtain a remainder dataset, and an adapted model is determined for the remainder dataset. The parameters of the adapted model are first initialized based on the set of parameters of the trained model, and then iteratively adapted by optimizing the objective function with respect to the remainder dataset.

Abstract: A system for processing a classifier. The classifier is a Naïve Bayes-type classifier classifying an input instance into multiple classes based on multiple continuous probability distributions of respective features of the input instance and based on prior probabilities of the multiple classes. Upon receiving a removal request message identifying one or more undesired training instances, the classifier is made independent from one or more undesired training instances. To this end, for a continuous probability distribution of a feature, adapted parameters of the probability distribution are computed based on current parameters of the probability distribution and the one or more undesired training instances. Further, an adapted prior probability of a class is computed based on a current prior probability of the class and the one or more undesired training instances.