Sina Däubener

According to our database, Sina Däubener authored at least 11 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Leveraging stochasticity to increase the robustness of artificial neural networks.

Sina Däubener

PhD thesis, 2024

On the Challenges and Opportunities in Generative AI.

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

CoRR, 2024

2023

On the Limitations of Model Stealing with Uncertainty Quantification Models.

David Pape, Sina Däubener, Thorsten Eisenhofer, Antonio Emanuele Cinà, Lea Schönherr

Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

2022

How Sampling Impacts the Robustness of Stochastic Neural Networks.

Sina Däubener, Asja Fischer

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Approaches to Uncertainty Quantification in Federated Deep Learning.

Florian Linsner, Linara Adilova, Sina Däubener, Michael Kamp, Asja Fischer

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021

SmoothLRP: Smoothing LRP by Averaging over Stochastic Input Variations.

Arne P. Raulf, Sina Däubener, Ben Hack, Axel Mosig, Asja Fischer

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing.

Denis Lukovnikov, Sina Däubener, Asja Fischer

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, 2021

2020

Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification.

Sina Däubener, Asja Fischer

CoRR, 2020

Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification.

Sina Däubener, Lea Schönherr, Asja Fischer, Dorothea Kolossa

Proceedings of the 21st Annual Conference of the International Speech Communication Association, 2020

2019

Anomaly Detection in Electrocardiogram Readings with Stacked LSTM Networks.

Markus Thill, Sina Däubener, Wolfgang Konen, Thomas Bäck

Proceedings of the 19th Conference Information Technologies, 2019

Anomaly Detection in Univariate Time Series: An Empirical Comparison of Machine Learning Algorithms.

Sina Däubener, Sebastian Schmitt, Hao Wang, Peter Krause, Thomas Bäck

Proceedings of the Advances in Data Mining, 2019