no code implementations • 24 Jan 2024 • Timothée Schmude, Laura Koesten, Torsten Möller, Sebastian Tschiatschek
Explanations of AI systems rarely address the information needs of people affected by algorithmic decision-making (ADM).
no code implementations • 26 May 2023 • Timothée Schmude, Laura Koesten, Torsten Möller, Sebastian Tschiatschek
We argue that explanations for "algorithmic decision-making" (ADM) systems can profit by adopting practices that are already used in the learning sciences.
no code implementations • 9 Apr 2022 • Aleksandar Doknic, Torsten Möller
Despite their effective use in various fields, many aspects of neural networks are poorly understood.
no code implementations • 20 May 2021 • Meng Ling, Jian Chen, Torsten Möller, Petra Isenberg, Tobias Isenberg, Michael Sedlmair, Robert S. Laramee, Han-Wei Shen, Jian Wu, C. Lee Giles
We present document domain randomization (DDR), the first successful transfer of convolutional neural networks (CNNs) trained only on graphically rendered pseudo-paper pages to real-world document segmentation.
no code implementations • 28 Jan 2021 • Natalie Grasser, Sebastian Ratzenböck, João Alves, Josefa Großschedl, Stefan Meingast, Catherine Zucker, Alvaro Hacar, Charles Lada, Alyssa Goodman, Marco Lombardi, John C. Forbes, Immanuel M. Bomze, Torsten Möller
Finally, we flag 17 sources in the literature as impostors, which are sources that exhibit large deviations from the average distance and proper motion properties of the $\rho$ Oph population.
Solar and Stellar Astrophysics Astrophysics of Galaxies
no code implementations • 22 Dec 2020 • Jian Chen, Meng Ling, Rui Li, Petra Isenberg, Tobias Isenberg, Michael Sedlmair, Torsten Möller, Robert S. Laramee, Han-Wei Shen, Katharina Wünsche, Qiru Wang
We present the VIS30K dataset, a collection of 29, 689 images that represents 30 years of figures and tables from each track of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST).
no code implementations • 5 Nov 2019 • Florian Heimerl, Christoph Kralj, Torsten Möller, Michael Gleicher
Summarizing these local metrics over the embeddings provides global overviews of similarities and differences.