347 citations · 809 across the 25 of their papers we have counts for
8 papers · 1 filter
Quantum Annealing for Industry Applications: Introduction and Review
Sheir Yarkoni, Elena Raponi, Thomas Bäck +1
Quantum annealing is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies hav…
Transfer Learning Based Co-surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Most existing multiobjetive evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time. Typically. this is unten…
Task-Sensitive Concept Drift Detector with Constraint Embedding
Andrea Castellani, Sebastian Schmitt, Barbara Hammer
Detecting drifts in data is essential for machine learning applications, as changes in the statistics of processed data typically has a profound influence on the performance of tra…
Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise
Andrea Castellani, Sebastian Schmitt, Barbara Hammer
In complex industrial settings, it is common practice to monitor the operation of machines in order to detect undesired states, adjust maintenance schedules, optimize system perfor…
A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition
Patricia Wollstadt, Sebastian Schmitt, Michael Wibral
Selecting a minimal feature set that is maximally informative about a target variable is a central task in machine learning and statistics. Information theory provides a powerful f…
Preprint: Norm Loss: An efficient yet effective regularization method for deep neural networks
Theodoros Georgiou, Sebastian Schmitt, Thomas Bäck +2
Convolutional neural network training can suffer from diverse issues like exploding or vanishing gradients, scaling-based weight space symmetry and covariant-shift. In order to add…
PREPRINT: Comparison of deep learning and hand crafted features for mining simulation data
Theodoros Georgiou, Sebastian Schmitt, Thomas Bäck +3
Computational Fluid Dynamics (CFD) simulations are a very important tool for many industrial applications, such as aerodynamic optimization of engineering designs like cars shapes,…
Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling
Sander van Rijn, Sebastian Schmitt, Matthijs van Leeuwen +1
In the context of optimization approaches to engineering applications, time-consuming simulations are often utilized which can be configured to deliver solutions for various levels…