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Julia Niebling
Julia Niebling
Researcher, DLR, Institute of Data Sciene
Email verificata su dlr.de
Titolo
Citata da
Citata da
Anno
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
642020
A branch--and--bound-based algorithm for nonconvex multiobjective optimization
J Niebling, G Eichfelder
SIAM Journal on Optimization 29 (1), 794-821, 2019
542019
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
462023
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
332015
An algorithmic approach to multiobjective optimization with decision uncertainty
G Eichfelder, J Niebling, S Rocktäschel
Journal of Global Optimization 77 (1), 3-25, 2020
272020
Analysis of railway track irregularities with convolutional autoencoders and clustering algorithms
J Niebling, B Baasch, A Kruspe
European Dependable Computing Conference, 78-89, 2020
202020
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
172022
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
152023
Nonconvex constrained optimization by a filtering branch and bound
G Eichfelder, K Klamroth, J Niebling
Journal of Global Optimization 80 (1), 31-61, 2021
92021
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
CoRR, 2022
82022
Unraveling anomalies in time: Unsupervised discovery and isolation of anomalous behavior in bio-regenerative life support system telemetry
F Rewicki, J Gawlikowski, J Niebling, J Denzler
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2024
42024
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
42024
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
42023
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
42022
A branch-and-bound algorithm for biobjective problems
J Niebling, G Eichfelder
Proceedings of the XIII Global Optimization Workshop GOW16, 57-60, 2016
42016
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
32022
Functional tensor decompositions for physics-informed neural networks
SK Vemuri, T Büchner, J Niebling, J Denzler
International Conference on Pattern Recognition, 32-46, 2025
22025
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
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
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
Articoli 1–20