Sina Däubener

17 papers A* 2A 2B 2Journal 7Unranked 3
YearRankTypeTitle / Venue / Authors
2025 A conf
ELBO, regularized maximum likelihood, and their common one-sample approximation for training stochastic neural networks.
UAI
Sina Däubener, Simon Damm, Asja Fischer
2025 J jnl
On the Challenges and Opportunities in Generative AI.
Trans. Mach. Learn. Res.
Laura Manduchi, Clara Meister, 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
2024 J jnl
Integrating uncertainty quantification into randomized smoothing based robustness guarantees.
CoRR
Sina Däubener, Kira Maag, David Krueger, Asja Fischer
2024 — —
Leveraging stochasticity to increase the robustness of artificial neural networks.
Sina Däubener
2024 J jnl
On the Challenges and Opportunities in Generative AI.
CoRR
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
2023 B conf
On the Limitations of Model Stealing with Uncertainty Quantification Models.
ESANN
David Pape, Sina Däubener, Thorsten Eisenhofer, Antonio Emanuele Cinà, Lea Schönherr
2023 J jnl
On the Limitations of Model Stealing with Uncertainty Quantification Models.
CoRR
David Pape, Sina Däubener, Thorsten Eisenhofer, Antonio Emanuele Cinà, Lea Schönherr
2022 A* conf
How Sampling Impacts the Robustness of Stochastic Neural Networks.
NeurIPS
Sina Däubener, Asja Fischer
2022 J jnl
How Sampling Impacts the Robustness of Stochastic Neural Networks.
CoRR
Sina Däubener, Asja Fischer
2021 — conf
Approaches to Uncertainty Quantification in Federated Deep Learning.
PKDD/ECML Workshops (1)
Florian Linsner, Linara Adilova, Sina Däubener, Michael Kamp, Asja Fischer
2021 — conf
Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing.
EMNLP (Findings)
Denis Lukovnikov, Sina Däubener, Asja Fischer
2021 B conf
SmoothLRP: Smoothing LRP by Averaging over Stochastic Input Variations.
ESANN
Arne P. Raulf, Sina Däubener, Ben Hack, Axel Mosig, Asja Fischer
2020 A conf
Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification.
INTERSPEECH
Sina Däubener, Lea Schönherr, Asja Fischer, Dorothea Kolossa
2020 J jnl
Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification.
CoRR
Sina Däubener, Lea Schönherr, Asja Fischer, Dorothea Kolossa
2020 J jnl
Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification.
CoRR
Sina Däubener, Asja Fischer
2019 — conf
Anomaly Detection in Electrocardiogram Readings with Stacked LSTM Networks.
ITAT
Markus Thill, Sina Däubener, Wolfgang Konen, Thomas Bäck
2019 A* conf
Anomaly Detection in Univariate Time Series: An Empirical Comparison of Machine Learning Algorithms.
ICDM
Sina Däubener, Sebastian Schmitt, Hao Wang, Peter Krause, Thomas Bäck