Robert Boschgmbh

Kathrin Skubch

Felix Berkenkamp

Julia Vinogradska

Anna Eivazi

Attila Reiss

Stefan Falkner

Petru Tighineanu

Paul Sebastian Baireuther

Jonathan Spitz

Lukas Grossberger

Amrutha Saseendran

Margret Keuper

A system, a device and a method for controlling a physical or chemical process. The method includes: determining a second a posteriori model based on a first a posteriori model that describes the relationship between an input variable and an output variable of a process related to the physical/chemical process, the second a posteriori model describing the relationship between an input variable and an output variable of the physical or chemical process; and controlling the physical or chemical process using the second a posteriori model.

System, device, and method for controlling a physical or chemical process

A method for controlling a physical system. The method includes training a neural network to output, for a plurality of tasks, a result of the task carried out, in each case in response to the input of a control configuration of the physical system and the input of a value of a task input parameter; ascertaining a control configuration for a further task with the aid of Bayesian optimization, the neural network, parameterized by the task input parameter, being used as a model for the relationship between control configuration and result; and controlling the physical system according to the control configuration to carry out the further task.

Device and method for controlling a physical system

A method for training a generator network that is configured to generate images with multiple objects. The method includes providing a set of training images, a generator network, and a discriminator network; drawing noise samples and target counts of objects; mapping, by the generator network, the noise samples and target counts of objects to generated images; randomly drawing images from a pool comprising generated images and training images; supplying the randomly drawn images to the discriminator network, thereby mapping them to a combination of: a decision whether the respective image is a training image or a generated image; optimizing discriminator parameters, optimizing generator parameters, and optimizing both the generator parameters and the discriminator parameters with the goal of improving the match between the predicted count of objects on the one hand, and the actual or target count of objects on the other hand.

Generator networks for generating images with predetermined counts of objects

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Frankfurt-am-Main, Germany

Kathrin Skubch