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Sebastian Wagner
Sebastian Wagner
Verified email at ke.tu-darmstadt.de
Title
Cited by
Cited by
Year
Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art
M Braei, S Wagner
https://arxiv.org/abs/2004.00433, 2020
2602020
Batchwise Patching of Classifiers
S Kauschke, J Fürnkranz
AAAI Conference on Artificial Intelligence, 3374-3381, 2018
332018
Anomaly detection in univariate time-series: A survey on the state-of-the-art. arXiv 2020
M Braei, S Wagner
arXiv preprint arXiv:2004.00433, 2020
262020
Patching Deep Neural Networks for Nonstationary Environments
S Kauschke, D Lehmann, J Fürnkranz
International Joint Conference on Neural Networks, 2019
132019
Anomaly Detection in Univariate Time-Series: A Survey on the State-of-the-Art. 2020
M Braei, S Wagner
arXiv preprint arXiv:2004.00433, 2004
102004
On the Challenges of Real World Data in Predictive Maintenance Scenarios: A Railway Application.
S Kauschke, F Janssen, I Schweizer, R Bergmann, S Gürg, G Miiller
LWA, 121-132, 2015
92015
Towards neural network patching: Evaluating engagement-layers and patch-architectures
S Kauschke, DH Lehmann
arXiv preprint arXiv:1812.03468, 2018
82018
Predicting cargo train failures: a machine learning approach for a lightweight prototype
S Kauschke, J Fürnkranz, F Janssen
Discovery Science: 19th International Conference, DS 2016, Bari, Italy …, 2016
82016
{P} Net: Privacy-preserving personalization of AI-based models by anonymous inter-person similarity networks
C Meurisch, S Kauschke, T Grube, B Bayrak, M Mühlhäuser
Proceedings of the 16th EAI International Conference on Mobile and …, 2019
62019
Learning to Predict Component Failures in Trains.
S Kauschke, I Schweizer, M Fiebrig, F Janssen
LWA, 71-82, 2014
62014
More Data Matters: Improving CGM Prediction via Ubiquitous Data and Deep Learning
J Heuschkel, S Kauschke
3rd International Workshop on Ubiquitous Personal Assistance (co-located …, 2018
52018
Beta Distribution Drift Detection for Adaptive Classifiers
L Fleckenstein, S Kauschke, J Fürnkranz
European Symposium on Neural Networks, 2019
32019
Mending is Better than Ending: Adapting Immutable Classifiers to Nonstationary Environments using Ensembles of Patches
S Kauschke, L Fleckenstein, J Fürnkranz
International Joint Conference on Neural Networks, 2019
22019
Towards semi-supervised classification of event streams via denoising autoencoders
S Kauschke, M Mühlhäuser, J Fürnkranz
2018 17th IEEE International Conference on Machine Learning and Applications …, 2018
22018
Towards Automatic Classification of Common Therapy Errors for Diabetes Therapy Support
J Heuschkel, S Kauschke, M Mühlhäuser
IEEE Global Communications Conference, 2019
12019
Leveraging reproduction-error representations for multi-instance classification
S Kauschke, M Mühlhäuser, J Fürnkranz
Discovery Science: 21st International Conference, DS 2018, Limassol, Cyprus …, 2018
12018
Improving Cargo Train Availability with Predictive Maintenance: An Overview and Prototype Implementation
S Kauschke
European Transport Conference 2016Association for European Transport (AET), 2016
12016
Advances in predictive maintenance for a railway scenario-project techlok
S Kauschke, F Janssen, I Schweizer
Knowledge Engineering Group, University of Darmstadt, 2015
12015
Patching-A Framework for Adapting Immutable Classifiers to Evolving Domains
S Kauschke
Technische Universität Darmstadt, 2019
2019
Predicting and Forecasting the Lifetime of Automotive Vehicle Components
O Esbel, S Kauschke, S Rinderknecht
VDI-Fachtagung Technische Zuverlässigkeit 2019, 321-336, 2019
2019
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