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ORCIDSina Däubener
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2020 – today
- 2024
[b1]Sina Däubener:
Leveraging stochasticity to increase the robustness of artificial neural networks. Ruhr University Bochum, Germany, 2024- [i5]Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric T. Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E. Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin:
On the Challenges and Opportunities in Generative AI. CoRR abs/2403.00025 (2024) - 2023
- [c8]David Pape, Sina Däubener, Thorsten Eisenhofer, Antonio Emanuele Cinà, Lea Schönherr:
On the Limitations of Model Stealing with Uncertainty Quantification Models. ESANN 2023 - [i4]David Pape, Sina Däubener, Thorsten Eisenhofer, Antonio Emanuele Cinà, Lea Schönherr:
On the Limitations of Model Stealing with Uncertainty Quantification Models. CoRR abs/2305.05293 (2023) - 2022
- [c7]Sina Däubener, Asja Fischer:
How Sampling Impacts the Robustness of Stochastic Neural Networks. NeurIPS 2022 - [i3]Sina Däubener, Asja Fischer:
How Sampling Impacts the Robustness of Stochastic Neural Networks. CoRR abs/2204.10839 (2022) - 2021
- [c6]Denis Lukovnikov, Sina Däubener, Asja Fischer:
Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing. EMNLP (Findings) 2021: 591-598 - [c5]Arne P. Raulf, Sina Däubener, Ben Hack, Axel Mosig, Asja Fischer:
SmoothLRP: Smoothing LRP by Averaging over Stochastic Input Variations. ESANN 2021 - [c4]Florian Linsner, Linara Adilova, Sina Däubener, Michael Kamp, Asja Fischer:
Approaches to Uncertainty Quantification in Federated Deep Learning. PKDD/ECML Workshops (1) 2021: 128-145 - 2020
- [c3]Sina Däubener, Lea Schönherr
, Asja Fischer, Dorothea Kolossa:
Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification. INTERSPEECH 2020: 4661-4665 - [i2]Sina Däubener, Lea Schönherr, Asja Fischer, Dorothea Kolossa:
Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification. CoRR abs/2005.14611 (2020) - [i1]Sina Däubener, Asja Fischer:
Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification. CoRR abs/2008.03209 (2020)
2010 – 2019
- 2019
- [c2]Sina Däubener, Sebastian Schmitt, Hao Wang, Peter Krause, Thomas Bäck:
Anomaly Detection in Univariate Time Series: An Empirical Comparison of Machine Learning Algorithms. ICDM 2019: 161-175 - [c1]Markus Thill, Sina Däubener, Wolfgang Konen, Thomas Bäck:
Anomaly Detection in Electrocardiogram Readings with Stacked LSTM Networks. ITAT 2019: 17-25
Coauthor Index
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