Abstract: A computer-implemented method for training a neural network. The neural network is configured to accept an input characterizing at least one sensor measurement and provide an output characterizing a classification and/or regressions result of the at least one sensor measurement and/or a probability of the sensor measurement to occur among a set of sensor measurements or wherein the neural network is configured to provide an output characterizing a prediction of a sensor measurement for creating a training and/or test dataset for training another machine learning system. The method includes: performing a singular value decomposition of a weight matrix or a weight tensor of the neural network; grouping singular values and corresponding singular vectors into a plurality of groups based on the magnitudes of the singular values; training the neural network by adapting each group according to a distinct and non-zero learning rate and/or distinct optimizers.

Abstract: A computer-implemented method for training a neural network. The method for training includes: performing a singular value decomposition of a weight matrix or a weight tensor of the neural network; grouping singular values and corresponding singular vectors into a plurality of groups based on the magnitudes of the singular values; training the neural network by adapting the singular values and/or singular vectors of only the group corresponding to the lowest singular values or adapting the singular values and/or singular vectors of only a predefined number of groups corresponding to the lowest singular values.

Abstract: Adapting a model to tasks. The model includes a linear layer for mapping a multidimensional input of the layer depending on weights to a multidimensional output of the layer, experts, and a router gate for adapting the model to different tasks. The method includes providing the input to the router gate; determining an output of the experts depending on an output of the router gate in response to the input; modifying the model depending on the output of the experts; mapping the input with the modified layer to the output of the model; training a first expert with a first training method; training a second expert of the experts with a second training method; maintaining the weights and the second expert unchanged in the training with the first training method; and maintaining the weights and the first expert unchanged in the training with the second training method.

Abstract: Configuring a model. The method includes: providing the model configured for determining an output of the model depending on an output of a layer of the model, the layer being configured to map a multidimensional input of the layer depending on trained weights to a multidimensional output of the layer; iteratively arranging the trained weights in vectors of a first matrix; determining a second matrix by removing at least one vector from the first matrix; determining an input of reduced dimensions by removing from the multidimensional input the dimension that corresponds or the dimensions that correspond to the at least one vector; configuring the model with a layer of reduced dimensions configured to map the input of reduced dimensions depending on weights from the second matrix to an output of reduced dimensions; and configuring the model for determining output of the model depending on the output of reduced dimensions.

Abstract: Tuning weights of a neural network of a model for processing input of the model representing information about a technical system and outputting an output of the model for operating a technical system. The model includes a linear layer for mapping a multidimensional input of the layer depending on the weights to a multidimensional output of the layer. The model is configured to determine the input of the layer depending on the input of the model, and to determine the output of the model depending on the output of the layer. A method includes providing training data include the input of the model and a ground truth for the output of the model corresponding to the input of the model in the training data, providing a set of tuning methods for tuning the weights, determining the principal components decomposition of a weight matrix including the weights.

Abstract: A computer-implemented method for finetuning a neural network. The method includes: providing an input to a layer of the neural network; determining a block-diagonal matrix; determining a first matrix by multiplying the block-diagonal matrix with a weight matrix of the layer, wherein the result of the multiplication is obtained by multiplying at least a plurality of blocks of the block-diagonal matrix with a respective part of the weight matrix in parallel computing operations and combining the result to form the first matrix; determining an output of the layer by multiplying the first matrix with the input of the layer; determining an output of the neural network based on the output of the layer; adapting elements of the block-diagonal matrix based on a difference of the output of the neural network and a desired output with respect to the input datum.

Abstract: Adapting a pretrained model to a task. The method includes providing the pretrained model including a layer configured to map a multidimensional input depending on weights to a multidimensional output, wherein a vector includes a subset of the weights that weighs the elements of the multidimensional input for a dimension of the output of the layer; providing training data and learning at least one vector of a transformation for adapting the subset depending on the training data and the output of the model, the at least one vector having unit length, and the transformation includes an outer product of the at least one vector with the transposed at least one vector, or the at least one vector is normalized to have unit length, and the transformation includes an outer product of the normalized at least one vector with the transposed normalized at least one vector.

Abstract: A device and computer implemented method for machine learning. The method includes providing embeddings that are associated with objects, providing a token that represents a part of a digital image that depicts at least a part of an object, wherein the token represents the part with a lower resolution than a resolution of the pixel of the part, selecting with a model an embedding of the embeddings to represent the token in a representation of the digital image, determining with the model a reconstruction of the token that represents the object depending on the representation of the digital image, and determining a parameter that defines at least one of the embeddings and/or a parameter that defines the model depending on a difference between the token and the reconstruction of the token.

Abstract: A computer-implemented method for training a machine learning system. The machine learning system is trained for generating images in at least two stages.