Robert Boschgmbh

Thomas Pfeil

Volker Fischer

Michael Pfeiffer

Chaithanya Kumar Mummadi

Alexander Kugele

Thomas Elsken

David Reeb

Manuel Nonnenmacher

A computer-implemented method for training a classifier. The classifier is configured to determine an output signal characterizing a classification of an input signal. The training of the classifier includes: determining a first training input signal; determining a first latent representation comprising a plurality of factors based on the first training input signal by means of an invertible factorization model, wherein the invertible factorization model, determining a second latent representation by adapting at least one factor of the first latent representation; determining a second training input signal based on the second latent representation by means of the invertible factorization model; and training the classifier based on the second training input signal.

Device and method for training a classifier using an invertible factorization model

A method for ascertaining a physical property of an object. The method includes detecting, for each input point in time of a sequence of input points in time, sensor data including information about a physical object, using an event-based sensor; for each subsequence of a breakdown of the sequence of input points in time into multiple subsequences including: feeding the sensor data detected for the input points in time of the subsequence to a pulsed neural network which generates a first processing result of the subsequence; feeding the processing result of the subsequence to a non-pulsed neural network; and processing the processing result of the subsequence by non-pulsed neurons of one or multiple first layer(s) of the non-pulsed neural network for generating a second processing result of the subsequence; and feeding the second processing results of the multiple subsequences to one or multiple second layer(s) of the non-pulsed neural network.

Device and method for ascertaining a physical property of a physical object

A method for generating a simplified computer-implemented neural network. The method includes: receiving a predefined neural network, which includes a plurality of neural network structures and is described by weights, each neural network structure being assigned a pruning vector which describes a change in weights as a result of the pruning of the respective neural network; calculating a product of a matrix including a structure vector, the matrix including partial second order derivations of a loss function with respect to the plurality of weights; determining changes in the loss function with respect to the predefined neural network, each change occurring as a result of a pruning of a corresponding neural network structure of the two or more neural network structures to be pruned; and pruning at least one neural network structure based on the determined two or more changes in the loss function to generate the simplified neural network.

Efficient second order pruning of computer-implemented neural networks

A method for compressing a deep neural network. The deep neural network includes a multitude of layers, which are each connected on the input side according to a predefinable sequence to their directly preceding layer of the sequence. The method includes adding a skip connection. Thereafter, a reduction of a resolution of the parameters of the layers follows. A computer program as well as to a device for carrying out the method are also described.

Compressing a deep neural network

A machine learning system, in particular a deep neural network. The machine learning system includes a plurality of layers that are connected to one another. The layers each ascertain an output variable as a function of an input variable and at least one parameter that is stored in a memory. The parameters of those layers that are connected to a further, in particular preceding, layer are each stored in the memory using a higher resolution than the parameters of those layers that are connected to a plurality of further, in particular preceding, layers. In addition, A method, a computer program, and a device for creating the machine learning system, are described.

Machine learning system and a method, a computer program and a device for creating the machine learning system

A machine learning system, including at least one temporal filter. An input variable, encompassing a chronological sequence of images, is processed with the aid of the machine learning system, using the filter. The machine learning system is configured to use the filter on a sequence of pixels, which are all situated at identical coordinates of the images, or at identical coordinates of intermediate results. Filter coefficients of the filter are quantized. A method, a computer program, and a device for creating the machine learning system are also described.

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Renningen, Germany

Thomas Pfeil