3 citations · 11 across the 9 of their papers we have counts for
11 papers
A Fast Heuristic for Computing Geodesic Cores in Large Networks
Florian Seiffarth, Tamás Horváth, Stefan Wrobel
Motivated by the increasing interest in applications of graph geodesic convexity in machine learning and data mining, we present a heuristic for computing the geodesic convex hull…
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…
Learning Weakly Convex Sets in Metric Spaces
Eike Stadtländer, Tamás Horváth, Stefan Wrobel
One of the central problems studied in the theory of machine learning is the question of whether, for a given class of hypotheses, it is possible to efficiently find a {consistent}…
A Generalized Weisfeiler-Lehman Graph Kernel
Till Hendrik Schulz, Tamás Horváth, Pascal Welke +1
The Weisfeiler-Lehman graph kernels are among the most prevalent graph kernels due to their remarkable time complexity and predictive performance. Their key concept is based on an…
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…
Learning Syllogism with Euler Neural-Networks
Tiansi Dong, Chengjiang Li, Christian Bauckhage +3
Traditional neural networks represent everything as a vector, and are able to approximate a subset of logical reasoning to a certain degree. As basic logic relations are better rep…
Maximal Closed Set and Half-Space Separations in Finite Closure Systems
Florian Seiffarth, Tamas Horvath, Stefan Wrobel
Several concept learning problems can be regarded as special cases of half-space separation in abstract closure systems over finite ground sets. For the typical scenario that the c…
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…
Adiabatic Quantum Computing for Binary Clustering
Christian Bauckhage, Eduardo Brito, Kostadin Cvejoski +3
Quantum computing for machine learning attracts increasing attention and recent technological developments suggest that especially adiabatic quantum computing may soon be of practi…
Using Echo State Networks for Cryptography
Rajkumar Ramamurthy, Christian Bauckhage, Krisztian Buza +1
Echo state networks are simple recurrent neural networks that are easy to implement and train. Despite their simplicity, they show a form of memory and can predict or regenerate se…