Abstract: A computer-implemented method for estimating uncertainties using a neural network, in particular, a neural process, in a model. The model models a technical system and/or a system behavior of the technical system. An architecture of the neural network for estimating uncertainties is also described.

Abstract: A computer-implemented method for assessing uncertainties in a model with the aid of a neural network in particular, a neural process. The model models a technical system and/or a system behavior of the technical system. An architecture of the neural network for assessing uncertainties is also described.

Abstract: A method for training a conditional neural process for determining a position of an object from image data. The method includes: providing training data for training the conditional neural process, wherein the training data comprise labeled image data showing a particular object and labeled comparison image data regarding the particular object; and training the conditional neural process based on the provided training data, wherein the training of the conditional neural process comprises applying functional contrastive learning, and wherein the training of the conditional neural process comprises applying an end-to-end learning approach.

Abstract: A system is described for configuring another system, e.g., a robotics system. The other system interacts with an environment according to a deterministic policy by repeatedly obtaining, from a sensor, sensor data indicative of a state of the environment, determining a current action, and providing, to an actuator, actuator data causing the actuator to effect the current action in the environment. To configure the other system, the system optimizes a loss function based on an accumulated reward distribution with respect to a set of parameters of the policy. The accumulated reward distribution includes an action probability of an action of a previous interaction log being performed according to the current set of parameters. The action probability is approximated using a probability distribution defined by an action selected by the deterministic policy according to the current set of parameters.

Abstract: A computer-implemented method for training a machine learning system in which the machine learning system is configured to ascertain, based on at least a first input signal and a multiplicity of second input signals and second output signals corresponding to the second input signals, a first output signal corresponding to the first input signal, the first output signal characterizing a classification encumbered with an uncertainty and/or a regression encumbered with an uncertainty.

Abstract: A computer-implemented method for ascertaining, using a machine learning system, a first output signal characterizing a classification and/or a regression of a first input signal, and the output signal includes a first representation which characterizes an expected value of the classification or the regression, and a second representation, which characterizes a variance of the classification or regression.

Abstract: A method for generating a computer-implemented machine learning system. The method includes receiving a training data set, which corresponds to a dynamic response of a device, and computing an aggregation of at least one latent variable of the machine learning system, using Bayesian inference, and in view of the training data set. An information item contained in the training data set is transferred directly into a statistical description of the plurality of latent variables. The method further includes generating an a-posteriori predictive distribution for predicting the dynamic response of the device, using the calculated aggregation, and under the condition that the training data set has set in.

Abstract: A system is described for configuring another system, e.g., a robotics system. The other system interacts with an environment according to a deterministic policy by repeatedly obtaining, from a sensor, sensor data indicative of a state of the environment, determining a current action, and providing, to an actuator, actuator data causing the actuator to effect the current action in the environment. To configure the other system, the system optimizes a loss function based on an accumulated reward distribution with respect to a set of parameters of the policy. The accumulated reward distribution includes an action probability of an action of a previous interaction log being performed according to the current set of parameters. The action probability is approximated using a probability distribution defined by an action selected by the deterministic policy according to the current set of parameters.