Search Results - Burghoff, Julian

  • Showing 1 - 6 results of 6
Refine Results
  1. 1
    Drohende Studienabbrüche durch Frühwarnsysteme erkennen: Welche Informationen sind relevant?

    Drohende Studienabbrüche durch Frühwarnsysteme erkennen: Welche Informationen sind relevant? by Schneider, Kerstin, Berens, Johannes, Burghoff, Julian

    Get full text
    Items that this one cites
    Article
    Save to List
    Saved in:
  2. 2
    Who breaks early, looses: goal oriented training of deep neural networks based on port Hamiltonian dynamics

    Who breaks early, looses: goal oriented training of deep neural networks based on port Hamiltonian dynamics by Burghoff, Julian, Marc Heinrich Monells, Gottschalk, Hanno

    Published in arXiv.org
    Get full text
    Article
    Save to List
    Saved in:
  3. 3
    ResBuilder: Automated Learning of Depth with Residual Structures

    ResBuilder: Automated Learning of Depth with Residual Structures by Burghoff, Julian, Rottmann, Matthias, Jill von Conta, Schoenen, Sebastian, Witte, Andreas, Gottschalk, Hanno

    Published in arXiv.org
    Get full text
    Article
    Save to List
    Saved in:
  4. 4
    Risk stratification of malignant melanoma using neural networks

    Risk stratification of malignant melanoma using neural networks by Burghoff, Julian, Ackermann, Leonhard, Younes Salahdine, Bram, Veronika, Wunderlich, Katharina, Balkenhol, Julius, Dirschka, Thomas, Gottschalk, Hanno

    Published in arXiv.org
    Get full text
    Article
    Save to List
    Saved in:
  5. 5
    Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning

    Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning by Burghoff, Julian, Chan, Robin, Gottschalk, Hanno, Muetze, Annika, Riedlinger, Tobias, Rottmann, Matthias, Schubert, Marius

    Published in arXiv.org
    Get full text
    Article
    Save to List
    Saved in:
  6. 6
    Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods

    Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods by Berens, Johannes, Schneider, Kerstin, Gortz, Simon, Oster, Simon, Burghoff, Julian

    Published in Policy File
    Request full text
    Report
    Save to List
    Saved in:

Search Tools: