Robert Boschgmbh

Robert Bosch Gmbh

Frank Hutter

Samuel Gabriel Mueller

Joerg Franke

Jan Hendrik Metzen

Jonathan Spitz

Lior Fuks

Andre Biedenkapp

Noor Awad

Danny Stoll

Thomas Elsken

Marius Lindauer

Diane Wagner

Thomas Brox

Simon Selg

Arber Zela

David Speck

Robert Mattmueller

Tonmoy Saikia

Yassine Marrakchi

A method for the transfer learning of hyperparameters of a machine learning algorithm. The method includes providing a current search space and a previous search space. A reduced search space is then created and candidate configurations are drawn repeatedly at random from the reduced search space and from the current search space, and the machine learning algorithm, parameterized in each case with the candidate configurations, is applied. A Tree Parzen Estimator (TPE) is then created as a function of the candidate solutions and the results of the machine learning algorithm parameterized with the candidate configurations, and the drawing of further candidate configurations from the current search space using the TPE is repeated multiple times, the TPE being updated upon each drawing.

Method and device for transfer learning between modified tasks

A method for generating training data for training a machine learning algorithm. The method includes the following steps: providing first training data and generating additional training data from at least one portion of the first training data, wherein the additional training data are generated in each case by applying, to all the training data included in the at least one portion of the first training data, an augmentation function randomly selected from a set of possible augmentation functions; providing the first training data and the additional training data for training the machine learning algorithm.

Method and control device for generating training data for training a machine learning algorithm

A computer-implemented method for training a machine learning system including: initializing parameters of the machine learning system and a metaparameter. Repeatedly carrying out the following as a loop: providing a batch of training data points and manipulating the provided training data points or a training method for optimizing the parameters of the machine learning system or a structure of the machine learning system based on the metaparameter. Ascertaining a cost function as a function of instantaneous parameters of the machine learning system and of the instantaneous metaparameters. Adapting the instantaneous parameters as a function of an ascertained first gradient, which has been ascertained with respect to the instantaneous parameters via the ascertained cost function for the training data points, and adapting the metaparameter as a function of a second gradient, which has been ascertained with respect to the metaparameter used in the preceding step via the ascertained cost function.

Training of machine learning systems for image processing

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.

Method and apparatus for automatically producing an artificial neural network

A method for producing a strategy for a robot. The method includes the following steps: initializing the strategy and an episode length; repeated execution of the loop including the following steps: producing a plurality of further strategies as a function of the strategy; applying the plurality of the further strategies for the length of the episode length; ascertaining respectively a cumulative reward, which is obtained in the application of the respective further strategy; updating the strategy as a function of a second plurality of the further strategies that obtained the greatest cumulative rewards. After each execution of the loop, the episode length is increased. A computer program, a device for carrying out the method, and a machine-readable memory element on which the computer program is stored, are also described.

Method, device and computer program for producing a strategy for a robot

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Freiburg, Germany

Frank Hutter