3D Self-Supervised Methods for Medical Imaging A Taleb, W Loetzsch, N Danz, J Severin, T Gaertner, B Bergner, C Lippert Proceedings of NeurIPS 2020, 2020 | 214 | 2020 |

Who wrote the web? Revisiting influential author identification research applicable to information retrieval M Potthast, S Braun, T Buz, F Duffhauss, F Friedrich, JM Gülzow, J Köhler, ... Advances in Information Retrieval: 38th European Conference on IR Research …, 2016 | 69 | 2016 |

Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks W Lötzsch, S Ohler, J Otterbach ICML 2022 2nd AI for Science Workshop, 2022 | 15 | 2022 |

Using Deep Reinforcement Learning for the Continuous Control of Robotic Arms W Lötzsch Bachelor Thesis, 2018 | 6 | 2018 |

WISE: Whitebox Image Stylization by Example-Based Learning W Lötzsch, M Reimann, M Büssemeyer, A Semmo, J Döllner, M Trapp European Conference on Computer Vision, 135-152, 2022 | 4 | 2022 |

Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation D Botache, J Decke, W Ripken, A Dornipati, F Götz-Hahn, M Ayeb, B Sick arXiv preprint arXiv:2309.13179, 2023 | 1 | 2023 |

Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models J Siems*, K Ditschuneit*, W Ripken*, A Lindborg*, M Schambach, ... Proceedings of NeurIPS 2023, 2023 | 1 | 2023 |

Towards Learning Self-Organized Criticality of Rydberg Atoms using Graph Neural Networks S Ohler, DS Brady, W Lötzsch, M Fleischhauer, J Otterbach ICML 2022 2nd AI for Science Workshop, 0 | 1* | |

Multiscale Neural Operators for Solving Time-Independent PDEs W Ripken*, L Coiffard*, F Pieper*, S Dziadzio The Symbiosis of Deep Learning and Differential Equations III, 2023 | | 2023 |

Simulating the Temperature-Dependent Absorbing-State Phase Transition in a Rydberg Many-Body Facilitated Gas using Neural Networks S Ohler, D Brady, W Ripken, M Fleischhauer, J Otterbach APS Division of Atomic, Molecular and Optical Physics Meeting Abstracts 2023 …, 2023 | | 2023 |

Learning languages with decidable hypotheses J Berger, M Böther, V Doskoč, JG Harder, N Klodt, T Kötzing, W Lötzsch, ... Connecting with Computability: 17th Conference on Computability in Europe …, 2021 | | 2021 |

Maps for Learning Indexable Classes J Berger, M Böther, V Doskoč, JG Harder, N Klodt, T Kötzing, W Lötzsch, ... arXiv preprint arXiv:2010.09460, 2020 | | 2020 |

Training a deep policy gradient-based neural network with asynchronous learners on a simulated robotic problem W Lötzsch, J Vitay, F Hamker INFORMATIK 2017, 2017 | | 2017 |