Robert Boschgmbh

Melih Kandemir

Max Welling

Emiel Hoogeboom

Muhammad Bilal Zafar

Sebastian Gerwinn

Jorn Peters

Chen Qiu

Karim Said Mahmoud Barsim

Barbara Rakitsch

David Reeb

Manuel Haussmann

Andreas Look

Buote Xu

A generator for converting an input vector from a latent space to one or more records x of measurement data that is realistic with respect to a given application domain. The generator includes: a trained neural network that is configured to map the input vector to a set of distribution parameters that characterize a random distribution of realistic measurement data, where this random distribution is configured such that given said set of distribution parameters and at least one source of randomness, samples of realistic measurement data may be obtained; and a sampling module including a random or pseudo-random number generator as a source of randomness and configured to sample the realistic measurement data from the random distribution.

Generating verifiably realistic measurement data

A device and a method for generating a compressed network from a trained neural network are provided. The method includes: a model generating a compressing map from first training data, the compressing map representing the impact of model components of the model to first output data in response to the first training data; generating a compressed network by compressing the trained neural network in accordance with the compressing map; the trained neural network generating trained network output data in response to second training data; the compressed network generating compressed network output data in response to the second training data; training the model by comparing the trained network output data with the compressed network output data.

Device and method for generating a compressed network from a trained neural network

A computer-implemented method of training a model for making time-series predictions of a computer-controlled system. The model uses a stochastic differential equation (SDE) comprising a drift component and a diffusion component. The drift component has a predefined part representing domain knowledge, that is received as an input to the training; and a trainable part. When training the model, values of the set of SDE variables at a current time point are predicted based on their values at a previous time point, and based on this, the model is refined. In order to predict the values of the set of SDE variables, the predefined part of the drift component is evaluated to get a first drift, and the first drift is combined with a second drift obtained by evaluating the trainable part of the drift component.

Making time-series predictions of a computer-controlled system

A trained model is described in the form of a Bayesian neural network (BNN) which provides a quantification of its inference uncertainty during use and which is trained using marginal likelihood maximization. A Probably Approximately Correct (PAC) bound may be used in the training to incorporate prior knowledge and to improve training stability even when the network architecture is deep.

Control and monitoring of physical system based on trained Bayesian neural network

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Stuttgart, Germany

Melih Kandemir