49 citations · 157 across the 21 of their papers we have counts for
22 papers
Multi-Agent Neural Rewriter for Vehicle Routing with Limited Disclosure of Costs
Nathalie Paul, Tim Wirtz, Stefan Wrobel +1
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) h…
Tailored Uncertainty Estimation for Deep Learning Systems
Joachim Sicking, Maram Akila, Jan David Schneider +4
Uncertainty estimation bears the potential to make deep learning (DL) systems more reliable. Standard techniques for uncertainty estimation, however, come along with specific combi…
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis
Julia Rosenzweig, Eduardo Brito, Hans-Ulrich Kobialka +8
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 si…
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Sebastian Houben, Stephanie Abrecht, Maram Akila +38
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 s…
Plants Don't Walk on the Street: Common-Sense Reasoning for Reliable Semantic Segmentation
Linara Adilova, Elena Schulz, Maram Akila +4
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, learnin…
Street-Map Based Validation of Semantic Segmentation in Autonomous Driving
Laura von Rueden, Tim Wirtz, Fabian Hueger +3
Artificial intelligence for autonomous driving must meet strict requirements on safety and robustness, which motivates the thorough validation of learned models. However, current v…
Supporting verification of news articles with automated search for semantically similar articles
Vishwani Gupta, Katharina Beckh, Sven Giesselbach +2
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 publis…
Approaching Neural Network Uncertainty Realism
Joachim Sicking, Alexander Kister, Matthias Fahrland +5
Statistical models are inherently uncertain. Quantifying or at least upper-bounding their uncertainties is vital for safety-critical systems such as autonomous vehicles. While stan…
A Novel Regression Loss for Non-Parametric Uncertainty Optimization
Joachim Sicking, Maram Akila, Maximilian Pintz +3
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…
DenseHMM: Learning Hidden Markov Models by Learning Dense Representations
Joachim Sicking, Maximilian Pintz, Maram Akila +1
We propose DenseHMM - a modification of Hidden Markov Models (HMMs) that allows to learn dense representations of both the hidden states and the observables. Compared to the standa…
Towards Map-Based Validation of Semantic Segmentation Masks
Laura von Rueden, Tim Wirtz, Fabian Hueger +2
Artificial intelligence for autonomous driving must meet strict requirements on safety and robustness. We propose to validate machine learning models for self-driving vehicles not…
Characteristics of Monte Carlo Dropout in Wide Neural Networks
Joachim Sicking, Maram Akila, Tim Wirtz +2
Monte Carlo (MC) dropout is one of the state-of-the-art approaches for uncertainty estimation in neural networks (NNs). It has been interpreted as approximately performing Bayesian…
Efficient Decentralized Deep Learning by Dynamic Model Averaging
Michael Kamp, Linara Adilova, Joachim Sicking +4
We propose an efficient protocol for decentralized training of deep neural networks from distributed data sources. The proposed protocol allows to handle different phases of model…
The Correlated Jacobi and the Correlated Cauchy-Lorentz ensembles
Tim Wirtz, Daniel Waltner, Mario Kieburg +1
We calculate the -point generating function of the correlated Jacobi ensemble using supersymmetric methods. We use the result for complex matrices for to derive a closed-f…
Asymptotic Coincidence of the Statistics for Degenerate and Non-Degenerate Correlated Real Wishart Ensembles
Tim Wirtz, Mario Kieburg, Thomas Guhr
The correlated Wishart model provides the standard benchmark when analyzing time series of any kind. Unfortunately, the real case, which is the most relevant one in applications, p…
The Smallest Eigenvalue Distribution in the Real Wishart-Laguerre Ensemble with Even Topology
Tim Wirtz, Gernot Akemann, Thomas Guhr +2
We consider rectangular random matrices of size belonging to the real Wishart-Laguerre ensemble also known as the chiral Gaussian orthogonal ensemble. This ensemble app…
Eigenvalue Density of the Doubly Correlated Wishart Model: Exact Results
Daniel Waltner, Tim Wirtz, Thomas Guhr
Data sets collected at different times and different observing points can possess correlations at different times at different positions. The doubly correlated Wishart model…
Limiting Statistics of the Largest and Smallest Eigenvalues in the Correlated Wishart Model
Tim Wirtz, Mario Kieburg, Thomas Guhr
The correlated Wishart model provides a standard tool for the analysis of correlations in a rich variety of systems. Although much is known for complex correlation matrices, the em…
Completing the picture for the smallest eigenvalue of real Wishart matrices
G. Akemann, T. Guhr, M. Kieburg +2
Rectangular real matrices with a Gaussian distribution appear very frequently in data analysis, condensed matter physics and quantum field theory. A central…
Distribution of the Smallest Eigenvalue in Complex and Real Correlated Wishart Ensembles
Tim Wirtz, Thomas Guhr
For the correlated Gaussian Wishart ensemble we compute the distribution of the smallest eigenvalue and a related gap probability.We obtain exact results for the complex (β=2) and…
Distribution of the Smallest Eigenvalue in the Correlated Wishart Model
Tim Wirtz, Thomas Guhr
Wishart random matrix theory is of major importance for the analysis of correlated time series. The distribution of the smallest eigenvalue for Wishart correlation matrices is part…
Worldsheet operator product expansions and p-point functions in AdS3/CFT2
Ingo Kirsch, Tim Wirtz
We construct the operator product expansions (OPE) of the chiral primary operators in the worldsheet theory for strings on AdS_3 x S^3 x T^4. As an interesting application, we will…