Robert Boschgmbh

Priyank Jaini

Max Welling

Emiel Hoogeboom

Anna Khoreva

Thomas Andy Keller

Jorn Peters

Didrik Nielsen

William Harris Beluch

Lars Holdijk

Kaspar Sakmann

A computer-implemented method of training an image classifier which uses any combination of labelled and/or unlabelled training images. The image classifier comprises a set of transformations between respective transformation inputs and transformation outputs. An inverse model is defined in which for a deterministic, non-injective transformation of the image classifier, its inverse is approximated by a stochastic inverse transformation. During training, for a given training image, a likelihood contribution for this transformation is determined based on a probability of its transformation inputs being generated by the stochastic inverse transformation given its transformation outputs. This likelihood contribution is used to determine a log-likelihood for the training image to be maximized (and its label, if the training image is labelled), based on which the model parameters are optimized.

Image classifier comprising a non-injective transformation

A computer-implemented method of training a machine learnable model for controlling and/or monitoring a computer-controlled system. The machine learnable model is configured to make inferences based on a probability distribution of sensor data of the computer-controlled system. The machine learnable model is configured to account for symmetries in the probability distribution imposed by the system and/or its environment. The training involves sampling multiple samples of the sensor data according to the probability distribution. Initial values are sampled from a source probability distribution invariant to the one or more symmetries. The samples are iteratively evolved according to a kernel function equivariant to the one or more symmetries. The evolution uses an attraction term and a repulsion term that are defined for a selected sample in terms of gradient directions of the probability distribution and of the kernel function for the multiple samples.

Machine learning based on a probability distribution of sensor data

A method for training a neural network. The neural network comprises a first layer which includes a plurality of filters to provide a first layer output comprising a plurality of feature maps. Training of the classifier includes: receiving, by a preceding layer, a first layer input in the first layer, wherein the first layer input is based on the input signal; determining the first layer output based on the first layer input and a plurality of parameters of the first layer; determining a first layer loss value based on the first layer output, wherein the first layer loss value characterizes a degree of dependency between the feature maps, the first layer loss value being obtained in an unsupervised fashion; and training the neural network. The training includes an adaption of the parameters of the first layer, the adaption being based on the first layer loss value.

Device and method for training a neuronal network

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 computer-implemented method for training a normalizing flow. The normalizing flow predicts a first density value based on a first input image. The first density value characterizes a likelihood of the first input image to occur. The first density value is predicted based on an intermediate output of a first convolutional layer of the normalizing flow. The intermediate output is determined based on a plurality of weights of the first convolutional layer. The method for training includes: determining a second input image; determining an output, wherein the output is determined by providing the second input image to the normalizing flow and providing an output of the normalizing flow as output; determining a second density value based on the output tensor and on the plurality of weights; determining a natural gradient of the plurality of weights with respect to the second density value; adapting the weights according to the natural gradient.

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Amsterdam, Netherlands

Priyank Jaini