Prof. Dr.-Ing.

Bauckhage, Christian

Filters

3 1
Reset filters

Settings
Now showing 1 - 4 of 4
  • Publication
    Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems
    ( 2023)
    Rueden, Laura von
    ;
    Mayer, Sebastian
    ;
    Beckh, Katharina
    ;
    Georgiev, Bogdan
    ;
    Giesselbach, Sven
    ;
    Heese, Raoul
    ;
    Kirsch, Birgit
    ;
    Walczak, Michal
    ;
    Pfrommer, Julius
    ;
    Pick, Annika
    ;
    Ramamurthy, Rajkumar
    ;
    Garcke, Jochen
    ;
    Bauckhage, Christian
    ;
    Schuecker, Jannis
    Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. We provide a definition and propose a concept for informed machine learning which illustrates its building blocks and distinguishes it from conventional machine learning. We introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Based on this taxonomy, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning.
  • Publication
    Quantum Circuit Evolution on NISQ Devices
    ( 2022-09-06)
    Franken, Lukas
    ;
    Georgiev, Bogdan
    ;
    Mücke, Sascha
    ;
    Wolter, Moritz
    ;
    Heese, Raoul
    ;
    Bauckhage, Christian
    ;
    Piatkowski, Nico
    Variational quantum circuits build the foundation for various classes of quantum algorithms. In a nutshell, the weights of a parametrized quantum circuit are varied until the empirical sampling distribution of the circuit is sufficiently close to a desired outcome. Numerical first-order methods are applied frequently to fit the parameters of the circuit, but most of the time, the circuit itself, that is, the actual composition of gates, is fixed. Methods for optimizing the circuit design jointly with the weights have been proposed, but empirical results are rather scarce. Here, we consider a simple evolutionary strategy that addresses the trade-off between finding appropriate circuit architectures and parameter tuning. We evaluate our method both via simulation and on actual quantum hardware. Our benchmark problems include the transverse field Ising Hamiltonian and the Sherrington-Kirkpatrick spin model. Despite the shortcomings of current noisy intermediate-scale quantum hardware, we find only a minor slowdown on actual quantum machines compared to simulations. Moreover, we investigate which mutation operations most significantly contribute to the optimization. The results provide intuition on how randomized search heuristics behave on actual quantum hardware and lay out a path for further refinement of evolutionary quantum gate circuits.
  • Publication
    Shells within Minimum Enclosing Balls
    ( 2020)
    Bauckhage, Christian
    ;
    Bortz, Michael
    ;
    Sifa, Rafet
    Addressing the general problem of data clustering, we propose to group the elements of a data set with respect to their location within their minimum enclosing ball. In particular, we propose to cluster data according to their distance to the center of a kernel minimum enclosing ball. Focusing on kernel minimum enclosing balls which are computed in abstract feature spaces reveals latent structures within a data set and allows for applying our ideas to non-numeric data. Results obtained on image-, text-, and graph-data illustrate the behavior and practical utility of our approach.
  • Publication
    Informed Machine Learning - A Taxonomy and Survey of Integrating Knowledge into Learning Systems
    ( 2019-03-29)
    Rüden, Laura von
    ;
    Mayer, Sebastian
    ;
    Beckh, Katharina
    ;
    Georgiev, Bogdan
    ;
    Giesselbach, Sven
    ;
    Heese, Raoul
    ;
    Kirsch, Birgit
    ;
    Pfrommer, Julius
    ;
    Pick, Annika
    ;
    Ramamurthy, Rajkumar
    ;
    Schuecker, Jannis
    ;
    Garcke, Jochen
    ;
    Bauckhage, Christian
    ;
    Walczak, Michal
    Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process, which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. First, we provide a definition and propose a concept for informed machine learning, which illustrates its building blocks and distinguishes it from conventional machine learning. Second, we introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Third, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning.