Abstract: A system and method for providing a task- and hardware-architecture-specific machine learning model. A trained superposition model is provided, which includes a superposition of a set of machine learning models, individual ones of the set of machine learning models being extractable from the trained superposition model. A characterization of a target hardware architecture is received. The trained superposition model is finetuned for an application task in a hardware-architecture-agnostic way. A machine learning model is selected from the finetuned superposition model, the selecting including, for the target hardware architecture, a search using a first function describing a first performance of a candidate machine learning model for the application task and a second function describing a second performance of the candidate machine learning model when executed on the target hardware architecture. The selected machine learning model is provided as output for deployment on the target hardware architecture.

Abstract: Training a hardware metric predictor. The hardware metric predictor is configured to receive as input a query description of a neural network architecture and to produce as output a predicted hardware metric predicted to be incurred by a neural network corresponding to the query description when run on the target hardware. A method may include giving as training input a number of input/output pairs of a given training function.

Abstract: A computer-implemented method for ascertaining an optimal architecture for a neural network that solves a given task in accordance with given boundary conditions and/or optimization goals. The method includes: providing a graph of the possible architectures of nodes and edges, wherein nodes correspond to data, edges correspond to parameterized operations to be carried out on the data, and a path which traverses the entire graph corresponds to an architecture; in a search phase, generating candidate architectures based on already known architectures, wherein the candidate architectures are similar but not identical to the known architectures in accordance with a predetermined criterion; evaluating the candidate architectures using the given boundary conditions and/or optimization goals; ascertaining a candidate architecture having the best rating as the sought optimal architecture.

Abstract: A method for automatically generating an artificial neural network that encompasses modules and connections that link those modules, successive modules and/or connections being added to a current starting network. Modules and/or connections that are to be added are selected randomly from a predefinable plurality of possible modules and connections that can be added. A plurality of possible refinements of the current starting network respectively are generated by adding to the starting network modules and/or connections that are to be added. One of the refinements from the plurality of possible refinements is then selected in order to serve as a current starting network in a subsequent execution of the method.

Abstract: A method for parameterizing a function, which outputs an ideal parameterization of a machine learning system for a large number of different data sets. A first training of a machine learning system is carried out in succession on multiple training data sets, the individual optimized parameterizations of the machine learning system being stored for each of the training data sets. A second training of the machine learning system simultaneously on all data sets then follows, the optimal parameterization of the machine learning system being stored. An optimization of the parameterization of the function thereupon follows in such a way that, given an optimal parameterization of the first training, the function outputs the associated optimal parameterization of the second training.

Abstract: A method for operating an artificial neural network (ANN), which processes inputs in a sequence of layers, to give outputs. In the method: within the ANN, at least one iterative block comprising one or more layers is defined and is to be executed multiple times; a number J of iterations is defined; an input of the iterative block is mapped onto an output by the iterative block; the output is supplied to the iterative block again as an input and is mapped onto a new output; once the iterative block has been executed J times, the output delivered by the iterative block is supplied, as an input, to the layer of the ANN succeeding the iterative block or is provided as the output of the ANN. A portion of the parameters that characterize the behavior of the layers in the iterative block are changed between the iterations.

Abstract: A device, computer-implemented method for the processing of digital sensor data and training methods therefor. A plurality of training tasks from a distribution of training tasks are provided, the training tasks characterizing the processing of digital sensor data. A parameter set for an architecture and for weights of an artificial neural network are determined with a first gradient-based learning algorithm and with a second gradient-based algorithm as a function of at least one first training task from the distribution of training tasks. The artificial neural network is trained with the first gradient-based learning algorithm as a function of the parameter set and as a function of a second training task.

Abstract: A method for automatically generating an artificial neural network that encompasses modules and connections that link those modules, successive modules and/or connections being added to a current starting network. Modules and/or connections that are to be added are selected randomly from a predefinable plurality of possible modules and connections that can be added. A plurality of possible refinements of the current starting network respectively are generated by adding to the starting network modules and/or connections that are to be added. One of the refinements from the plurality of possible refinements is then selected in order to serve as a current starting network in a subsequent execution of the method.

Abstract: A method for ascertaining a suitable network configuration for a neural network.

Abstract: A method for ascertaining a suitable network configuration for a neural network for a predefined application that is determined in the form of training data.

Abstract: A fin stabilizer for stabilizing a watercraft against rolling movements includes a main fin configured to be pivoted by a watercraft-side fin drive, a tail fin, and an elastically deformable connection between the main fin and the tail fin, the elastically deformable connection being configured to flex whenever a water force acting on the tail fin is greater than a predetermined amount.

Abstract: A fin stabilizer for stabilizing a watercraft against rolling movements includes a main fin configured to be pivoted by a watercraft-side fin drive, a tail fin, and an elastically deformable connection between the main fin and the tail fin, the elastically deformable connection being configured to flex whenever a water force acting on the tail fin is greater than a predetermined amount.