Spremljaj
Julia Niebling
Julia Niebling
Researcher, DLR, Institute of Data Sciene
Preverjeni e-poštni naslov na dlr.de
Naslov
Navedeno
Navedeno
Leto
Solving multiobjective mixed integer convex optimization problems
M De Santis, G Eichfelder, J Niebling, S Rocktäschel
SIAM Journal on Optimization 30 (4), 3122-3145, 2020
482020
A branch--and--bound-based algorithm for nonconvex multiobjective optimization
J Niebling, G Eichfelder
SIAM Journal on Optimization 29 (1), 794-821, 2019
472019
Evaluation of multi feature fusion at score-level for appearance-based person re-identification
M Eisenbach, A Kolarow, A Vorndran, J Niebling, HM Gross
2015 international joint conference on neural networks (IJCNN), 1-8, 2015
322015
An algorithmic approach to multiobjective optimization with decision uncertainty
G Eichfelder, J Niebling, S Rocktäschel
Journal of Global Optimization 77 (1), 3-25, 2020
202020
Is It Worth It? Comparing Six Deep and Classical Methods for Unsupervised Anomaly Detection in Time Series
F Rewicki, J Denzler, J Niebling
Applied Sciences 13 (3), 1778, 2023
172023
Analysis of railway track irregularities with convolutional autoencoders and clustering algorithms
J Niebling, B Baasch, A Kruspe
European Dependable Computing Conference, 78-89, 2020
102020
Domain shifts in dermoscopic skin cancer datasets: Evaluation of essential limitations for clinical translation
K Fogelberg, S Chamarthi, RC Maron, J Niebling, TJ Brinker
New Biotechnology 76, 106-117, 2023
82023
Impact of training set size on the ability of deep neural networks to deal with omission noise
J Gütter, A Kruspe, XX Zhu, J Niebling
Frontiers in Remote Sensing 3, 932431, 2022
72022
Nonconvex constrained optimization by a filtering branch and bound
G Eichfelder, K Klamroth, J Niebling
Journal of Global Optimization 80 (1), 31-61, 2021
72021
Is it worth it? An experimental comparison of six deep-and classical machine learning methods for unsupervised anomaly detection in time series
F Rewicki, J Denzler, J Niebling
arXiv preprint arXiv:2212.11080, 2022
52022
A branch-and-bound algorithm for biobjective problems
J Niebling, G Eichfelder
Proceedings of the XIII Global Optimization Workshop GOW16, 57-60, 2016
42016
Mitigating the Influence of Domain Shift in Skin Lesion Classification: A Benchmark Study of Unsupervised Domain Adaptation Methods on Dermoscopic Images
S Chamarthi, K Fogelberg, RC Maron, TJ Brinker, J Niebling
arXiv preprint arXiv:2310.03432, 2023
22023
Analysing the Interactions Between Training Dataset Size, Label Noise and Model Performance in Remote Sensing Data
J Gütter, J Niebling, XX Zhu
IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium …, 2022
22022
Using a B&B algorithm from multiobjective optimization to solve constrained optimization problems
G Eichfelder, K Klamroth, J Niebling
AIP Conference Proceedings 2070 (1), 2019
22019
Uncertainty is not sufficient for identifying noisy labels in training data for binary segmentation of building footprints
H Ulman, J Gütter, J Niebling
Frontiers in Remote Sensing 3, 1100012, 2023
12023
Structuring Uncertainty for Fine-Grained Sampling in Stochastic Segmentation Networks
F Nussbaum, J Gawlikowski, J Niebling
Advances in Neural Information Processing Systems 35, 27678-27691, 2022
12022
Robust Distribution-Shift Aware Sar-Optical data Fusion for Multi-Label Scene Classification
J Gawlikowski, S Saha, J Niebling, XX Zhu
IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium …, 2022
12022
Mitigating the influence of domain shift in skin lesion classification: A benchmark study of unsupervised domain adaptation methods
S Chamarthi, K Fogelberg, TJ Brinker, J Niebling
Informatics in Medicine Unlocked 44, 101430, 2024
2024
Unsupervised Anomaly Detection for Space Gardening
F Rewicki, J Denzler, J Niebling
2023
Handling unexpected inputs: incorporating source-wise out-of-distribution detection into SAR-optical data fusion for scene classification
J Gawlikowski, S Saha, J Niebling, XX Zhu
EURASIP Journal on Advances in Signal Processing 2023 (1), 47, 2023
2023
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