Publications

Here is the list of publications authored by the consortium.

2023
  • Schött, Svenja Y., Rifat Mehreen Amin, and Andreas Butz. “A Literature Survey of How to Convey Transparency in Co-Located Human–Robot Interaction.” Multimodal Technologies and Interaction 7.3 (2023): 25. – link
  • Li, Cong, et al. “Safe Planning and Control under Uncertainty: A Model-Free Design with One-Step Backward Data.” IEEE Transactions on Industrial Electronics (2023). – link
  • F. Debinksi, S. Renner, E. Müller-Seydlitz, Y. Huang, T. Schubert, L. Busse und T. Euler, “Response properties of suppressed-by-contrast cells in the early mouse visual system” Meeting of the German Neuroscience Society, Göttingen, 2023 (2023). (accepted)
2022
  • Boche, Holger, Adalbert Fono, and Gitta Kutyniok. “Limitations of Deep Learning for Inverse Problems on Digital Hardware.” arXiv preprint arXiv:2202.13490 (2022) – link.
  • Boche, Holger, Adalbert Fono, and Gitta Kutyniok. “Inverse Problems Are Solvable on Real Number Signal Processing Hardware.” arXiv preprint arXiv:2204.02066 (2022) – link.
  • Schött, Svenja Yvonne, and Andreas Butz. “Robo-Tooltips: Understandable Robots for Trustworthy Interactions.” Mensch und Computer 2022-Workshopband (2022). – link.
  • Scholl, Philipp, et al. “Well-definedness of Physical Law Learning: The Uniqueness Problem.” arXiv preprint arXiv:2210.08342 (2022) – link.
  • Bacho, Aras, Holger Boche, and Gitta Kutyniok. “Complexity Blowup for Solutions of the Laplace and the Diffusion Equation.” arXiv preprint arXiv:2212.00693 (2022) – link.
  • Bernáez Timón, Laura, Pierre Ekelmans, Nataliya Kraynyukova, Tobias Rose, Laura Busse, and Tatjana Tchumatchenko. “How to incorporate biological insights into network models and why it matters.” The Journal of Physiology (2022) – link.
  • Brandt, Jasmin, et al. “Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget.” arXiv preprint arXiv:2202.04487 (2022). – link
  • Hüllermeier, Eyke. “Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?.” arXiv preprint arXiv:2209.03302 (2022). – link.