Abstract: A method for determining an optimal architecture of a neural network. The method includes: defining a search space by means of a context-free grammar; training neural networks with candidate architectures on the training data, and validating the trained neural networks on the validation data; initializing a Gaussian process, wherein the Gaussian process comprises a Weisfeiler-Lehman graph kernel; adapting the Gaussian process such that given the candidate architectures, the Gaussian process predicts the validation achieved with these candidate architectures; and performing a Bayesian optimization for finding the candidate architecture that achieved the best performance.

Abstract: 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.