Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Wirtz, Tim"'
We interpret solving the multi-vehicle routing problem as a team Markov game with partially observable costs. For a given set of customers to serve, the playing agents (vehicles) have the common goal to determine the team-optimal agent routes with mi
Externí odkaz:
http://arxiv.org/abs/2206.05990
Autor:
Sicking, Joachim, Akila, Maram, Schneider, Jan David, Hüger, Fabian, Schlicht, Peter, Wirtz, Tim, Wrobel, Stefan
Uncertainty estimation bears the potential to make deep learning (DL) systems more reliable. Standard techniques for uncertainty estimation, however, come along with specific combinations of strengths and weaknesses, e.g., with respect to estimation
Externí odkaz:
http://arxiv.org/abs/2204.13963
Autor:
Rosenzweig, Julia, Brito, Eduardo, Kobialka, Hans-Ulrich, Akila, Maram, Schmidt, Nico M., Schlicht, Peter, Schneider, Jan David, Hüger, Fabian, Rottmann, Matthias, Houben, Sebastian, Wirtz, Tim
Many machine learning applications can benefit from simulated data for systematic validation - in particular if real-life data is difficult to obtain or annotate. However, since simulations are prone to domain shift w.r.t. real-life data, it is cruci
Externí odkaz:
http://arxiv.org/abs/2106.05549
Autor:
Houben, Sebastian, Abrecht, Stephanie, Akila, Maram, Bär, Andreas, Brockherde, Felix, Feifel, Patrick, Fingscheidt, Tim, Gannamaneni, Sujan Sai, Ghobadi, Seyed Eghbal, Hammam, Ahmed, Haselhoff, Anselm, Hauser, Felix, Heinzemann, Christian, Hoffmann, Marco, Kapoor, Nikhil, Kappel, Falk, Klingner, Marvin, Kronenberger, Jan, Küppers, Fabian, Löhdefink, Jonas, Mlynarski, Michael, Mock, Michael, Mualla, Firas, Pavlitskaya, Svetlana, Poretschkin, Maximilian, Pohl, Alexander, Ravi-Kumar, Varun, Rosenzweig, Julia, Rottmann, Matthias, Rüping, Stefan, Sämann, Timo, Schneider, Jan David, Schulz, Elena, Schwalbe, Gesina, Sicking, Joachim, Srivastava, Toshika, Varghese, Serin, Weber, Michael, Wirkert, Sebastian, Wirtz, Tim, Woehrle, Matthias
Publikováno v:
Fingscheidt, T., Gottschalk, H., Houben, S. (eds) Deep Neural Networks and Data for Automated Driving, Springer, Cham (2022)
The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over i
Externí odkaz:
http://arxiv.org/abs/2104.14235
Autor:
Adilova, Linara, Schulz, Elena, Akila, Maram, Houben, Sebastian, Schneider, Jan David, Hueger, Fabian, Wirtz, Tim
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. However, learning common knowledge only from data is hard and approaches for knowledge
Externí odkaz:
http://arxiv.org/abs/2104.09254
Autor:
von Rueden, Laura, Wirtz, Tim, Hueger, Fabian, Schneider, Jan David, Piatkowski, Nico, Bauckhage, Christian
Artificial intelligence for autonomous driving must meet strict requirements on safety and robustness, which motivates the thorough validation of learned models. However, current validation approaches mostly require ground truth data and are thus bot
Externí odkaz:
http://arxiv.org/abs/2104.07538
Fake information poses one of the major threats for society in the 21st century. Identifying misinformation has become a key challenge due to the amount of fake news that is published daily. Yet, no approach is established that addresses the dynamics
Externí odkaz:
http://arxiv.org/abs/2103.15581
Autor:
Sicking, Joachim, Kister, Alexander, Fahrland, Matthias, Eickeler, Stefan, Hüger, Fabian, Rüping, Stefan, Schlicht, Peter, Wirtz, Tim
Statistical models are inherently uncertain. Quantifying or at least upper-bounding their uncertainties is vital for safety-critical systems such as autonomous vehicles. While standard neural networks do not report this information, several approache
Externí odkaz:
http://arxiv.org/abs/2101.02974
Quantification of uncertainty is one of the most promising approaches to establish safe machine learning. Despite its importance, it is far from being generally solved, especially for neural networks. One of the most commonly used approaches so far i
Externí odkaz:
http://arxiv.org/abs/2101.02726
Despite of its importance for safe machine learning, uncertainty quantification for neural networks is far from being solved. State-of-the-art approaches to estimate neural uncertainties are often hybrid, combining parametric models with explicit or
Externí odkaz:
http://arxiv.org/abs/2012.12687