| Anomaly detection in electrocardiogram readings with stacked LSTM networks M Thill, S Däubener, W Konen, THW Bäck, P Barancikova, M Holena, ... Proceedings of the 19th Conference Information Technologies-Applications and …, 2019 | 27 | 2019 |
| Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification S Däubener, L Schönherr, A Fischer, D Kolossa INTERSPEECH, 2020 | 20 | 2020 |
| Anomaly Detection in Univariate Time Series: An Empirical Comparison of Machine Learning Algorithms. S Däubener, S Schmitt, H Wang, P Krause, T Bäck ICDM, 161-175, 2019 | 19 | 2019 |
| Predictive uncertainty quantification with compound density networks A Kristiadi, S Däubener, A Fischer arXiv preprint arXiv:1902.01080, 2019 | 14 | 2019 |
| Approaches to uncertainty quantification in federated deep learning F Linsner, L Adilova, S Däubener, M Kamp, A Fischer Machine Learning and Principles and Practice of Knowledge Discovery in …, 2022 | 8 | 2022 |
| Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing D Lukovnikov, S Daubener, A Fischer Findings of the Association for Computational Linguistics: EMNLP 2021, 591-598, 2021 | 5 | 2021 |
| On the Limitations of Model Stealing with Uncertainty Quantification Models D Pape, S Däubener, T Eisenhofer, AE Cinà, L Schönherr arXiv preprint arXiv:2305.05293, 2023 | 2 | 2023 |
| How Sampling Impacts the Robustness of Stochastic Neural Networks S Däubener, A Fischer arXiv preprint arXiv:2204.10839, 2022 | 2 | 2022 |
| Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification S Däubener, A Fischer arXiv preprint arXiv:2008.03209, 2020 | 2 | 2020 |
| SmoothLRP: Smoothing LRP by Averaging over Stochastic Input Variations. AP Raulf, S Däubener, B Hack, A Mosig, A Fischer ESANN, 2021 | 1 | 2021 |
| Uncertainty quantification with compound density networks A Kristiadi, S Däubener, A Fischer Proc. 4th Workshop Bayesian Deep Learn. NeurIPS, 2019 | 1 | 2019 |
| Efficient Calculation of Adversarial Examples for Bayesian Neural Networks S Däubener, J Frank, T Holz, A Fischer Third Symposium on Advances in Approximate Bayesian Inference, 0 | 1* | |
| On the Challenges and Opportunities in Generative AI L Manduchi, K Pandey, R Bamler, R Cotterell, S Däubener, S Fellenz, ... arXiv preprint arXiv:2403.00025, 2024 | | 2024 |
