no code implementations • 28 Feb 2024 • 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 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
The field of deep generative modeling has grown rapidly and consistently over the years.
no code implementations • 9 May 2023 • David Pape, Sina Däubener, Thorsten Eisenhofer, Antonio Emanuele Cinà, Lea Schönherr
We realize that during training, the models tend to have similar predictions, indicating that the network diversity we wanted to leverage using uncertainty quantification models is not (high) enough for improvements on the model stealing task.
no code implementations • 22 Apr 2022 • Sina Däubener, Asja Fischer
Stochastic neural networks (SNNs) are random functions whose predictions are gained by averaging over multiple realizations.
no code implementations • 1 Jan 2021 • Arne Peter Raulf, Ben Luis Hack, Sina Däubener, Axel Mosig, Asja Fischer
With the excessive use of neural networks in safety critical domains the need for understandable explanations of their predictions is rising.
no code implementations • pproximateinference AABI Symposium 2021 • Sina Däubener, Joel Frank, Thorsten Holz, Asja Fischer
In this paper we propose to efficiently attack Bayesian neural networks with adversarial examples calculated for a deterministic network with parameters given by the mean of the posterior distribution.
no code implementations • 7 Aug 2020 • Sina Däubener, Asja Fischer
Uncertainty quantification in neural networks gained a lot of attention in the past years.
1 code implementation • 24 May 2020 • Sina Däubener, Lea Schönherr, Asja Fischer, Dorothea Kolossa
The neural networks for uncertainty quantification simultaneously diminish the vulnerability to the attack, which is reflected in a lower recognition accuracy of the malicious target text in comparison to a standard hybrid ASR system.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 4 Feb 2019 • Agustinus Kristiadi, Sina Däubener, Asja Fischer
Despite the huge success of deep neural networks (NNs), finding good mechanisms for quantifying their prediction uncertainty is still an open problem.