Search Results for author:
Found 14 papers, 2 papers with code
Guideline for Trustworthy Artificial Intelligence -- AI Assessment Catalog
Artificial Intelligence (AI) has made impressive progress in recent years and represents a key technology that has a crucial impact on the economy and society.
Robustness in Fatigue Strength Estimation
Fatigue strength estimation is a costly manual material characterization process in which state-of-the-art approaches follow a standardized experiment and analysis procedure.
Multi-Agent Neural Rewriter for Vehicle Routing with Limited Disclosure of Costs
We propose the introduction of a so-called pool in the system which serves as a collection point for unvisited nodes.
Tailored Uncertainty Estimation for Deep Learning Systems
Uncertainty estimation bears the potential to make deep learning (DL) systems more reliable.
Graph Filtration Kernels
The majority of popular graph kernels is based on the concept of Haussler's $\mathcal{R}$-convolution kernel and defines graph similarities in terms of mutual substructures.
Learning Weakly Convex Sets in Metric Spaces
Although it has been shown quite a while ago that efficient learning of weakly convex hypotheses, a parameterized relaxation of convex hypotheses, is possible for the special case of Boolean functions, the question of whether this idea can be developed into a generic paradigm has not been studied yet.
A Generalized Weisfeiler-Lehman Graph Kernel
The Weisfeiler-Lehman graph kernels are among the most prevalent graph kernels due to their remarkable time complexity and predictive performance.
A Novel Regression Loss for Non-Parametric Uncertainty Optimization
One of the most commonly used approaches so far is Monte Carlo dropout, which is computationally cheap and easy to apply in practice.
Wasserstein Dropout
Despite of its importance for safe machine learning, uncertainty quantification for neural networks is far from being solved.
Learning Syllogism with Euler Neural-Networks
In contrast to traditional neural network, ENN can precisely represent all 24 different structures of Syllogism.
Maximal Closed Set and Half-Space Separations in Finite Closure Systems
As a first approach to overcome this negative result, we relax the problem to maximal closed set separation, give a generic greedy algorithm solving this problem with a linear number of closure operator calls, and show that this bound is sharp.
Efficient Decentralized Deep Learning by Dynamic Model Averaging
We propose an efficient protocol for decentralized training of deep neural networks from distributed data sources.
Adiabatic Quantum Computing for Binary Clustering
Quantum computing for machine learning attracts increasing attention and recent technological developments suggest that especially adiabatic quantum computing may soon be of practical interest.
Using Echo State Networks for Cryptography
The key idea is to assume that Alice and Bob share a copy of an echo state network.
