no code implementations • 13 Jun 2022 • Nathalie Paul, Tim Wirtz, Stefan Wrobel, Alexander Kister
We propose the introduction of a so-called pool in the system which serves as a collection point for unvisited nodes.
no code implementations • 29 Apr 2022 • Joachim Sicking, Maram Akila, Jan David Schneider, Fabian Hüger, Peter Schlicht, Tim Wirtz, Stefan Wrobel
Uncertainty estimation bears the potential to make deep learning (DL) systems more reliable.
no code implementations • 10 Jun 2021 • Julia Rosenzweig, Eduardo Brito, Hans-Ulrich Kobialka, Maram Akila, Nico M. Schmidt, Peter Schlicht, Jan David Schneider, Fabian Hüger, Matthias Rottmann, Sebastian Houben, Tim Wirtz
We propose a novel framework consisting of a generative label-to-image synthesis model together with different transferability measures to inspect to what extent we can transfer testing results of semantic segmentation models from synthetic data to equivalent real-life data.
no code implementations • 29 Apr 2021 • Sebastian Houben, Stephanie Abrecht, Maram Akila, Andreas Bär, Felix Brockherde, Patrick Feifel, Tim Fingscheidt, Sujan Sai Gannamaneni, Seyed Eghbal Ghobadi, Ahmed Hammam, Anselm Haselhoff, Felix Hauser, Christian Heinzemann, Marco Hoffmann, Nikhil Kapoor, Falk Kappel, Marvin Klingner, Jan Kronenberger, Fabian Küppers, Jonas Löhdefink, Michael Mlynarski, Michael Mock, Firas Mualla, Svetlana Pavlitskaya, Maximilian Poretschkin, Alexander Pohl, Varun Ravi-Kumar, Julia Rosenzweig, Matthias Rottmann, Stefan Rüping, Timo Sämann, Jan David Schneider, Elena Schulz, Gesina Schwalbe, Joachim Sicking, Toshika Srivastava, Serin Varghese, Michael Weber, Sebastian Wirkert, Tim Wirtz, Matthias Woehrle
Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods.
no code implementations • 19 Apr 2021 • Linara Adilova, Elena Schulz, Maram Akila, Sebastian Houben, Jan David Schneider, Fabian Hueger, Tim Wirtz
Data-driven sensor interpretation in autonomous driving can lead to highly implausible predictions as can most of the time be verified with common-sense knowledge.
no code implementations • 15 Apr 2021 • Laura von Rueden, Tim Wirtz, Fabian Hueger, Jan David Schneider, Nico Piatkowski, Christian Bauckhage
Lastly, we present quantitative results on the Cityscapes dataset indicating that our validation approach can indeed uncover errors in semantic segmentation masks.
no code implementations • 29 Mar 2021 • Vishwani Gupta, Katharina Beckh, Sven Giesselbach, Dennis Wegener, Tim Wirtz
We find that our approach is agnostic to concept drifts, i. e. the machine learning task is independent of the hypotheses in a text.
no code implementations • 8 Jan 2021 • Joachim Sicking, Alexander Kister, Matthias Fahrland, Stefan Eickeler, Fabian Hüger, Stefan Rüping, Peter Schlicht, Tim Wirtz
Statistical models are inherently uncertain.
no code implementations • pproximateinference AABI Symposium 2021 • Joachim Sicking, Maram Akila, Maximilian Pintz, Tim Wirtz, Asja Fischer, Stefan Wrobel
One of the most commonly used approaches so far is Monte Carlo dropout, which is computationally cheap and easy to apply in practice.
1 code implementation • 23 Dec 2020 • Joachim Sicking, Maram Akila, Maximilian Pintz, Tim Wirtz, Asja Fischer, Stefan Wrobel
Despite of its importance for safe machine learning, uncertainty quantification for neural networks is far from being solved.
1 code implementation • 17 Dec 2020 • Joachim Sicking, Maximilian Pintz, Maram Akila, Tim Wirtz
We propose two optimization schemes that make use of this: a modification of the Baum-Welch algorithm and a direct co-occurrence optimization.
no code implementations • 3 Nov 2020 • Laura von Rueden, Tim Wirtz, Fabian Hueger, Jan David Schneider, Christian Bauckhage
Artificial intelligence for autonomous driving must meet strict requirements on safety and robustness.
no code implementations • 10 Jul 2020 • Joachim Sicking, Maram Akila, Tim Wirtz, Sebastian Houben, Asja Fischer
Monte Carlo (MC) dropout is one of the state-of-the-art approaches for uncertainty estimation in neural networks (NNs).
no code implementations • 25 Sep 2019 • Joachim Sicking, Alexander Kister, Matthias Fahrland, Stefan Eickeler, Fabian Hueger, Stefan Rueping, Peter Schlicht, Tim Wirtz
Statistical models are inherently uncertain.
1 code implementation • 9 Jul 2018 • Michael Kamp, Linara Adilova, Joachim Sicking, Fabian Hüger, Peter Schlicht, Tim Wirtz, Stefan Wrobel
We propose an efficient protocol for decentralized training of deep neural networks from distributed data sources.