Sina Däubener

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 13

Top Topics

Stochastic Model

Metropolis Hastings

Machine Learning Algorithms

Top Venues

Publications

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 (2024)
David Pape, Sina Däubener, Thorsten Eisenhofer, Antonio Emanuele Cinà, Lea Schönherr
On the Limitations of Model Stealing with Uncertainty Quantification Models. CoRR (2023)
David Pape, Sina Däubener, Thorsten Eisenhofer, Antonio Emanuele Cinà, Lea Schönherr
On the Limitations of Model Stealing with Uncertainty Quantification Models. ESANN (2023)
Sina Däubener, Asja Fischer
How Sampling Impacts the Robustness of Stochastic Neural Networks. CoRR (2022)
Sina Däubener, Asja Fischer
How Sampling Impacts the Robustness of Stochastic Neural Networks. NeurIPS (2022)
Denis Lukovnikov, Sina Däubener, Asja Fischer
Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing. EMNLP (Findings) (2021)
Arne P. Raulf, Sina Däubener, Ben Hack, Axel Mosig, Asja Fischer
SmoothLRP: Smoothing LRP by Averaging over Stochastic Input Variations. ESANN (2021)
Florian Linsner, Linara Adilova, Sina Däubener, Michael Kamp, Asja Fischer
Approaches to Uncertainty Quantification in Federated Deep Learning. PKDD/ECML Workshops (1) (2021)
Sina Däubener, Lea Schönherr, Asja Fischer, Dorothea Kolossa
Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification. CoRR (2020)
Sina Däubener, Lea Schönherr, Asja Fischer, Dorothea Kolossa
Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification. INTERSPEECH (2020)
Sina Däubener, Asja Fischer
Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification. CoRR (2020)
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)
Markus Thill, Sina Däubener, Wolfgang Konen, Thomas Bäck
Anomaly Detection in Electrocardiogram Readings with Stacked LSTM Networks. ITAT (2019)