227 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- KU LeuvenBE24 papers
- University of AntwerpBE23 papers
- Ghent UniversityBE19 papers
- Hasselt UniversityBE9 papers
- University of ReadingGB7 papers
- Technical University of MunichDE5 papers
- Vrije Universiteit BrusselBE5 papers
- Eindhoven University of TechnologyNL4 papers
- University of MonsBE3 papers
- Core Laboratories (United States)US2 papers
- Corvinus University of BudapestHU2 papers
- Ghent University HospitalBE2 papers
8 papers · 1 filter
Direct Interval Propagation Methods using Neural-Network Surrogates for Uncertainty Quantification in Physical Systems Surrogate Model
Ghifari Adam Faza, Jolan Wauters, Fabio Cuzzolin +2
In engineering, uncertainty propagation aims to characterise system outputs under uncertain inputs. For interval uncertainty, the goal is to determine output bounds given interval-…
Efficient Training of Neural SDEs Using Stochastic Optimal Control
Rembert Daems, Manfred Opper, Guillaume Crevecoeur +1
We present a hierarchical, control theory inspired method for variational inference (VI) for neural stochastic differential equations (SDEs). While VI for neural SDEs is a promisin…
Explainability through uncertainty: Trustworthy decision-making with neural networks
Arthur Thuy, Dries F. Benoit
Uncertainty is a key feature of any machine learning model and is particularly important in neural networks, which tend to be overconfident. This overconfidence is worrying under d…
Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification
Arthur Thuy, Dries F. Benoit
Image classification with neural networks (NNs) is widely used in industrial processes, situations where the model likely encounters unknown objects during deployment, i.e., out-of…
Transfer learning-assisted inverse modeling in nanophotonics based on mixture density networks
Liang Cheng, Prashant Singh, Francesco Ferranti
The simulation of nanophotonic structures relies on electromagnetic solvers, which play a crucial role in understanding their behavior. However, these solvers often come with a sig…
FLASC: A Flare-Sensitive Clustering Algorithm
D. M. Bot, J. Peeters, J. Liesenborgs +1
Clustering algorithms are often used to find subpopulations in exploratory data analysis workflows. Not only the clusters themselves, but also their shape can represent meaningful…
KeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates from Images
Rembert Daems, Jeroen Taets, Francis wyffels +1
We present KeyCLD, a framework to learn Lagrangian dynamics from images. Learned keypoints represent semantic landmarks in images and can directly represent state dynamics. We show…
A general anomaly detection framework for fleet-based condition monitoring of machines
Kilian Hendrickx, Wannes Meert, Yves Mollet +4
Machine failures decrease up-time and can lead to extra repair costs or even to human casualties and environmental pollution. Recent condition monitoring techniques use artificial…