David Moens

According to our database, David Moens authored at least 17 papers between 2017 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

A Review of Uncertainty Representation and Quantification in Neural Networks.

Kaizheng Wang, Fabio Cuzzolin, Keivan Shariatmadar, David Moens, Hans Hallez

IEEE Trans. Pattern Anal. Mach. Intell., March, 2026

Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study.

Kaizheng Wang, Yunjia Wang, Fabio Cuzzolin, David Moens, Hans Hallez, Siu Lun Chau

CoRR, February, 2026

Learning Credal Ensembles via Distributionally Robust Optimization.

Kaizheng Wang, Ghifari Adam Faza, Fabio Cuzzolin, Siu Lun Chau, David Moens, Hans Hallez

CoRR, February, 2026

Direct interval propagation methods using neural-network surrogates for uncertainty quantification in physical systems surrogate model.

Ghifari Adam Faza, Jolan Wauters, Fabio Cuzzolin, Hans Hallez, David Moens

Knowl. Based Syst., 2026

Credal Ensemble Distillation for Uncertainty Quantification.

Kaizheng Wang, Fabio Cuzzolin, David Moens, Hans Hallez

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025

Generalized Decision Focused Learning under Imprecise Uncertainty-Theoretical Study.

Keivan Shariatmadar, Neil Yorke-Smith, Ahmad Osman, Fabio Cuzzolin, Hans Hallez, David Moens

CoRR, February, 2025

CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks.

Kaizheng Wang, Keivan Shariatmadar, Shireen Kudukkil Manchingal, Fabio Cuzzolin, David Moens, Hans Hallez

Neural Networks, 2025

Most likely heteroscedastic Gaussian process via kernel smoothing.

Ghifari Adam Faza, Nasrulloh R. B. S. Loka, Keivan Shariatmadar, Hans Hallez, David Moens

Knowl. Based Syst., 2025

Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification.

Kaizheng Wang, Fabio Cuzzolin, Keivan Shariatmadar, David Moens, Hans Hallez

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Reliability evaluation of a multi-state system with dependent components and imprecise parameters: A structural reliability treatment.

Lechang Yang, Xinyao Zhang, Zitong Lu, Yuqiang Fu, David Moens, Michael Beer

Reliab. Eng. Syst. Saf., 2024

Credal Wrapper of Model Averaging for Uncertainty Estimation on Out-Of-Distribution Detection.

Kaizheng Wang, Fabio Cuzzolin, Keivan Shariatmadar, David Moens, Hans Hallez

CoRR, 2024

Credal Deep Ensembles for Uncertainty Quantification.

Kaizheng Wang, Fabio Cuzzolin, Shireen Kudukkil Manchingal, Keivan Shariatmadar, David Moens, Hans Hallez

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2022

An introduction to optimization under uncertainty - A short survey.

Keivan Shariatmadar, Kaizheng Wang, Calvin R. Hubbard, Hans Hallez, David Moens

CoRR, 2022

2021

Computationally Efficient Optimization Method to Quantify the Required Surgical Accuracy for a Ligament Balanced TKA.

Laura Bartsoen, Matthias G. R. Faes, Mariska Wesseling, Roel Wirix-Speetjens, David Moens, Ilse Jonkers, Jos Vander Sloten

IEEE Trans. Biomed. Eng., 2021

2019

Hybrid spatial uncertainty analysis for the estimation of imprecise failure probabilities in Laser Sintered PA-12 parts.

Matthias G. R. Faes, Ghosh Dastidar Sabyasachi, David Moens

Comput. Math. Appl., 2019

A fast inverse approach for the quantification of set-theoretical uncertainty.

Lars Bogaerts, Matthias G. R. Faes, David Moens

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2019

2017

Comparison of Bayesian and interval uncertainty quantification: Application to the AIRMOD test structure.

Matteo Broggi, Matthias G. R. Faes, Edoardo Patelli, Yves Govers, David Moens, Michael Beer

Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017