| 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 | 347 | 2020 |
| Batchwise Patching of Classifiers S Kauschke, J Fürnkranz AAAI Conference on Artificial Intelligence, 3374-3381, 2018 | 35 | 2018 |
| 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 | 31 | 2020 |
| Patching Deep Neural Networks for Nonstationary Environments S Kauschke, D Lehmann, J Fürnkranz International Joint Conference on Neural Networks, 2019 | 16 | 2019 |
| 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 | 14 | 2004 |
| 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 | 9 | 2016 |
| 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 | 9 | 2015 |
| Towards neural network patching: Evaluating engagement-layers and patch-architectures S Kauschke, DH Lehmann arXiv preprint arXiv:1812.03468, 2018 | 8 | 2018 |
| Anomaly detection in univeriate time-series: a survey on the state-of-the-art. arXiv. doi: 10.48550 M Braei, IS Wagner arXiv, 2004 | 7 | 2004 |
| {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 | 6 | 2019 |
| Learning to Predict Component Failures in Trains. S Kauschke, I Schweizer, M Fiebrig, F Janssen LWA, 71-82, 2014 | 6 | 2014 |
| 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 | 5 | 2018 |
| Beta Distribution Drift Detection for Adaptive Classifiers L Fleckenstein, S Kauschke, J Fürnkranz European Symposium on Neural Networks, 2019 | 3 | 2019 |
| 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 | 2 | 2019 |
| 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 | 2 | 2018 |
| Leveraging reproduction-error representations for multi-instance classification S Kauschke, M Mühlhäuser, J Fürnkranz Discovery Science: 21st International Conference, DS 2018, Limassol, Cyprus …, 2018 | 2 | 2018 |
| Improving Cargo Train Availability with Predictive Maintenance: An Overview and Prototype Implementation S Kauschke European Transport Conference 2016Association for European Transport (AET), 2016 | 2 | 2016 |
| Towards Automatic Classification of Common Therapy Errors for Diabetes Therapy Support J Heuschkel, S Kauschke, M Mühlhäuser IEEE Global Communications Conference, 2019 | 1 | 2019 |
| Advances in predictive maintenance for a railway scenario-project techlok S Kauschke, F Janssen, I Schweizer Knowledge Engineering Group, University of Darmstadt, 2015 | 1 | 2015 |
| Patching-A Framework for Adapting Immutable Classifiers to Evolving Domains S Kauschke Technische Universität Darmstadt, 2019 | | 2019 |
Johannes FürnkranzJohannes Kepler University, LinzVerified email at faw.jku.at
