44 citations · 72 across the 5 of their papers we have counts for
9 papers
Attentional Feature Fusion
Yimian Dai, Fabian Gieseke, Stefan Oehmcke +2
Feature fusion, the combination of features from different layers or branches, is an omnipresent part of modern network architectures. It is often implemented via simple operations…
Inferring astrophysical X-ray polarization with deep learning
Nikita Moriakov, Ashwin Samudre, Michela Negro +3
We investigate the use of deep learning in the context of X-ray polarization detection from astrophysical sources as will be observed by the Imaging X-ray Polarimetry Explorer (IXP…
Magnitude and Uncertainty Pruning Criterion for Neural Networks
Vinnie Ko, Stefan Oehmcke, Fabian Gieseke
Neural networks have achieved dramatic improvements in recent years and depict the state-of-the-art methods for many real-world tasks nowadays. One drawback is, however, that many…
Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data
Stefan Oehmcke, Christoffer Thrysøe, Andreas Borgstad +3
Massive amounts of satellite data have been gathered over time, holding the potential to unveil a spatiotemporal chronicle of the surface of Earth. These data allow scientists to i…
Massively-Parallel Break Detection for Satellite Data
Malte von Mehren, Fabian Gieseke, Jan Verbesselt +3
The field of remote sensing is nowadays faced with huge amounts of data. While this offers a variety of exciting research opportunities, it also yields significant challenges regar…
Return of the features. Efficient feature selection and interpretation for photometric redshifts
Antonio D'Isanto, Stefano Cavuoti, Fabian Gieseke +1
The explosion of data in recent years has generated an increasing need for new analysis techniques in order to extract knowledge from massive datasets. Machine learning has proved…