no code implementations • EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 • David Schmidt, Albin Zehe, Janne Lorenzen, Lisa Sergel, Sebastian Düker, Markus Krug, Frank Puppe
The release of this corpus provides an opportunity of training and comparing different algorithms for the extraction of character networks, which so far was barely possible due to heterogeneous interests of previous researchers.
no code implementations • 14 Apr 2023 • Matthias Kreuzer, David Schmidt, Simon Wokusch, Walter Kellermann
In this paper, we address the challenging problem of detecting bearing faults in railway vehicles by analyzing acoustic signals recorded during regular operation.
no code implementations • 9 Feb 2018 • Martin Erdmann, Lukas Geiger, Jonas Glombitza, David Schmidt
We use adversarial network architectures together with the Wasserstein distance to generate or refine simulated detector data.
Instrumentation and Methods for Astrophysics High Energy Physics - Experiment
