Robert Boschgmbh

Robert Bosch Gmbh

Patrick Forré

Max Welling

Anna Khoreva

Emiel Hoogeboom

Priyank Jaini

William Harris Beluch

Thomas Andy Keller

Jorn Peters

Tim Genewein

Didrik Nielsen

Giorgio Patrini

Marcello Carioni

A computer-implemented method of training an image generation model. The image generation model comprises an argmax transformation configured to compute a discrete index feature indicating an index of a feature of the continuous feature vector with an extreme value. The image generation model is trained using a log-likelihood optimization. This involves obtaining a value of the index feature for the training image, sampling values of the continuous feature vector given the value of the index feature according to a stochastic inverse transformation of the argmax transformation, and determining a likelihood contribution of the argmax transformation for the log-likelihood based on a probability that the stochastic inverse transformation generates the values of the continuous feature vector given the value of the index feature.

Image generation model based on log-likelihood

A lossy data compressor for physical measurement data, comprising a parametrized mapping network hat, when applied to a measurement data point x in a space X, produces a point z in a lower-dimensional manifold Z, and configured to provide a point z on manifold Z as output in response to receiving a data point x as input, wherein the manifold Z is a continuous hypersurface that only admits fully continuous paths between any two points on the hypersurface; and the parameters &#x3b8; of the mapping network are trainable or trained towards an objective that comprises minimizing, on the manifold Z, a distance between a given prior distribution Pand a distribution Pinduced on manifold Z by mapping a given set Pof physical measurement data from X onto Z using the mapping network, according to a given distance measure.

Lossy data compressor for vehicle control systems

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Amsterdam, Netherlands

Patrick Forré