116 citations
- Charité - Universitätsmedizin BerlinDE11 papers
- Technische Universität BerlinDE7 papers
- University of CopenhagenDK4 papers
- ETH ZurichCH3 papers
- Humboldt-Universität zu BerlinDE3 papers
- Lawrence Berkeley National LaboratoryUS3 papers
- Arcada University of Applied SciencesFI2 papers
- Birmingham City UniversityGB2 papers
- Cornell UniversityUS2 papers
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)DE2 papers
- Freie Universität BerlinDE2 papers
- German Cancer Research CenterDE2 papers
25 papers
LinkML: An Open Data Modeling Framework
Sierra A. T. Moxon, Harold Solbrig, Nomi L. Harris +33
Scientific research relies on well-structured, standardized data; however, much of it is stored in formats such as free-text lab notebooks, non-standardized spreadsheets, or data r…
Seabed-Net: A multi-task network for joint bathymetry estimation and seabed classification from remote sensing imagery in shallow waters
Panagiotis Agrafiotis, Begüm Demir
Accurate, detailed, and regularly updated bathymetry, coupled with complex semantic content, is essential for under-mapped shallow-water environments facing increasing climatologic…
Mitigating Clever Hans Strategies in Image Classifiers through Generating Counterexamples
Sidney Bender, Ole Delzer, Jan Herrmann +3
Deep learning models remain vulnerable to spurious correlations, leading to so-called Clever Hans predictors that undermine robustness even in large-scale foundation and self-super…
Fast and Accurate Explanations of Distance-Based Classifiers by Uncovering Latent Explanatory Structures
Florian Bley, Jacob Kauffmann, Simon León Krug +2
Distance-based classifiers, such as k-nearest neighbors and support vector machines, continue to be a workhorse of machine learning, widely used in science and industry. In practic…
Getting Ready for the EU AI Act in Healthcare. A call for Sustainable AI Development and Deployment
John Brandt Brodersen, Ilaria Amelia Caggiano, Pedro Kringen +6
Assessments of trustworthiness have become a cornerstone of responsible AI development. Especially in high-stakes fields like healthcare, aligning technical, evidence-based, and et…
Wasserstein Distances Made Explainable: Insights Into Dataset Shifts and Transport Phenomena
Philip Naumann, Jacob Kauffmann, Grégoire Montavon
Wasserstein distances provide a powerful framework for comparing data distributions. They can be used to analyze processes over time or to detect inhomogeneities within data. Howev…