74 citations · 156 across the 28 of their papers we have counts for
36 papers
Deep Active Learning with Noisy Oracle in Object Detection
Marius Schubert, Tobias Riedlinger, Karsten Kahl +1
Obtaining annotations for complex computer vision tasks such as object detection is an expensive and time-intense endeavor involving a large number of human workers or expert opini…
Constrained Local Approximate Ideal Restriction for Advection-Diffusion Problems
Ahsan Ali, James Brannick, Karsten Kahl +3
This paper focuses on developing a reduction-based algebraic multigrid method that is suitable for solving general (non)symmetric linear systems and is naturally robust from pure a…
LMD: Light-weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds
Tobias Riedlinger, Marius Schubert, Sarina Penquitt +7
Object detection on Lidar point cloud data is a promising technology for autonomous driving and robotics which has seen a significant rise in performance and accuracy during recent…
Identifying Label Errors in Object Detection Datasets by Loss Inspection
Marius Schubert, Tobias Riedlinger, Karsten Kahl +4
Labeling datasets for supervised object detection is a dull and time-consuming task. Errors can be easily introduced during annotation and overlooked during review, yielding inaccu…
A flexible short recurrence Krylov subspace method for matrices arising in the time integration of port Hamiltonian systems and ODEs/DAEs with a dissipative Hamiltonian
Malak Diab, Andreas Frommer, Karsten Kahl
For several classes of mathematical models that yield linear systems, the splitting of the matrix into its Hermitian and skew Hermitian parts is naturally related to properties of…
Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection
Tobias Riedlinger, Marius Schubert, Karsten Kahl +2
Active learning as a paradigm in deep learning is especially important in applications involving intricate perception tasks such as object detection where labels are difficult and…
MGiaD: Multigrid in all dimensions. Efficiency and robustness by coarsening in resolution and channel dimensions
Antonia van Betteray, Matthias Rottmann, Karsten Kahl
Current state-of-the-art deep neural networks for image classification are made up of 10 - 100 million learnable weights and are therefore inherently prone to overfitting. The comp…
Krylov subspace restarting for matrix Laplace transforms
Andreas Frommer, Karsten Kahl, Marcel Schweitzer +1
A common way to approximate -- the action of a matrix function on a vector -- is to use the Arnoldi approximation. Since a new vector needs to be generated and stored in ev…
Matrix functions via linear systems built from continued fractions
Andreas Frommer, Karsten Kahl, Manuel Tsolakis
A widely used approach to compute the action of a matrix function on a vector is to use a rational approximation for and compute instead. If …
MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection
Marius Schubert, Karsten Kahl, Matthias Rottmann
In object detection with deep neural networks, the box-wise objectness score tends to be overconfident, sometimes even indicating high confidence in presence of inaccurate predicti…
Coarsening in Algebraic Multigrid using Gaussian Processes
Hanno Gottschalk, Karsten Kahl
Multigrid methods have proven to be an invaluable tool to efficiently solve large sparse linear systems arising in the discretization of partial differential equations (PDEs). Alge…
A multigrid accelerated eigensolver for the Hermitian Wilson-Dirac operator in lattice QCD
Andreas Frommer, Karsten Kahl, Francesco Knechtli +3
Eigenvalues of the Hermitian Wilson-Dirac operator are of special interest in several lattice QCD simulations, e.g., for noise reduction when evaluating all-to-all propagators. In…
On the equivalence of the Hermitian eigenvalue problem and hypergraph edge elimination
Karsten Kahl, Bruno Lang
It is customary to identify sparse matrices with the corresponding adjacency or incidence graph. For the solution of linear systems of equations using Gaussian elimination, the rep…
Krylov type methods exploiting the quadratic numerical range
Andreas Frommer, Birgit Jacob, Karsten Kahl +2
The quadratic numerical range is a subset of the standard numerical range of a linear operator which still contains its spectrum. It arises naturally in operators which ha…
Automated Local Fourier Analysis (aLFA)
Karsten Kahl, Nils Kintscher
Local Fourier analysis is a commonly used tool to assess the quality and aid in the construction of geometric multigrid methods for translationally invariant operators. In this pap…
Deep Bayesian Active Semi-Supervised Learning
Matthias Rottmann, Karsten Kahl, Hanno Gottschalk
In many applications the process of generating label information is expensive and time consuming. We present a new method that combines active and semi-supervised deep learning to…
Schur complement solver for Quantum Monte-Carlo simulations of strongly interacting fermions
Maksim Ulybyshev, Nils Kintscher, Karsten Kahl +1
We present a non-iterative solver based on the Schur complement method for sparse linear systems of special form which appear in Quantum Monte-Carlo (QMC) simulations of strongly i…
Least Angle Regression Coarsening in Bootstrap Algebraic Multigrid
Karsten Kahl, Matthias Rottmann
The bootstrap algebraic multigrid framework allows for the adaptive construction of algebraic multigrid methods in situations where geometric multigrid methods are not known or not…
Optimal interpolation and Compatible Relaxation in Classical Algebraic Multigrid
James Brannick, Fei Cao, Karsten Kahl +2
In this paper, we consider a classical form of optimal algebraic multigrid (AMG) interpolation that directly minimizes the two-grid convergence rate and compare it with the so-call…
Adaptive Aggregation-based Domain Decomposition Multigrid for Twisted Mass Fermions
Constantia Alexandrou, Simone Bacchio, Jacob Finkenrath +3
The Adaptive Aggregation-based Domain Decomposition Multigrid method (arXiv:1303.1377) is extended for two degenerate flavors of twisted mass fermions. By fine-tuning the parameter…
DDalphaAMG for Twisted Mass Fermions
Simone Bacchio, Costantia Alexandrou, Jacob Finkenrath +3
We present the Adaptive Aggregation-based Domain Decomposition Multigrid method extended to the twisted mass fermion discretization action. We show comparisons of results as a func…
(Approximate) Low-Mode Averaging with a new Multigrid Eigensolver
Gunnar Bali, Sara Collins, Andreas Frommer +5
We present a multigrid based eigensolver for computing low-modes of the Hermitian Wilson Dirac operator. For the non-Hermitian case multigrid methods have already replaced conventi…
Multigrid methods for tensor structured Markov chains with low rank approximation
Matthias Bolten, Karsten Kahl, Sonja Sokolović
Tensor structured Markov chains are part of stochastic models of many practical applications, e.g., in the description of complex production or telephone networks. The most interes…
Multigrid Preconditioning for the Overlap Operator in Lattice QCD
James Brannick, Andreas Frommer, Karsten Kahl +3
The overlap operator is a lattice discretization of the Dirac operator of quantum chromodynamics, the fundamental physical theory of the strong interaction between the quarks. As o…