1 citations · 1 across the 5 of their papers we have counts for
6 papers
Deterministic Bounds on Accuracy and Stability of Metric-Space Regression and Classification
Nina Maria Gottschling, Jakob Gawlikowski, Michele Caprio
Data-driven approaches often face ambiguity in datasets due to sensor or target noise, insufficient information, and noise in the input data. While best-fit machine learning method…
Beyond Accuracy: Assessing Calibration of Geospatial Foundation Models and Their Sensitivity to Distribution Shifts
Nils Lehmann, Jakob Gawlikowski, Burak Ekim +2
Geospatial Foundation Models (GeoFMs) are most commonly ranked and selected by accuracy on standard benchmark conditions via averaged ranks. We show that this protocol is too narro…
LunarFM: A Shared Multimodal Representation of the Moon's Surface
Marc Girona-Mata, Jakob Gawlikowski, Sumit Goski +6
The renewed global focus on lunar exploration, driven by the prospect of in-situ resource utilization and a sustained human presence on the Moon, has created growing demand for acc…
Data-Centric Benchmark for Label Noise Estimation and Ranking in Remote Sensing Binary Building Segmentation
Keiller Nogueira, Codrut-Andrei Diaconu, Dávid Kerekes +9
High-quality pixel-level annotations are essential for the semantic segmentation of remote sensing imagery. However, such labels are expensive to obtain and often affected by noise…
Average Kernel Sizes -- Computable Sharp Accuracy Bounds for Inverse Problems
Nina M. Gottschling, David Iagaru, Jakob Gawlikowski +1
The reconstruction of an unknown quantity from noisy measurements is a mathematical problem relevant in most applied sciences, for example, in medical imaging, radar inverse scatte…
Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning
Nils Lehmann, Jakob Gawlikowski, Adam J. Stewart +4
Uncertainty quantification (UQ) is an essential tool for applying deep neural networks (DNNs) to real world tasks, as it attaches a degree of confidence to DNN outputs. However, de…