Raymond N. J. Veldhuis

Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers

Mykola Pechenizkiy, D. Mocanu, Zahra Atashgahi, 2023, ArXiv.

An unsupervised anomaly detection model to identify emphysema in low-dose computed tomography

M. Oudkerk, R. Vliegenthart, Jiali Cai, 2021 .

Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks

Mykola Pechenizkiy, D. Mocanu, Zahra Atashgahi, 2023, Trans. Mach. Learn. Res..

Hybrid biometric template protection: Resolving the agony of choice between bloom filters and homomorphic encryption

Andreas Peter, Florian Hahn, Raymond Veldhuis, 2022, IET Biom..

The U-Net Family for Epicardial Adipose Tissue Segmentation and Quantification in Low-Dose CT

P. V. van Ooijen, M. Oudkerk, C. Brune, 2023, Technologies.

Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting

Nikolay A. Pavlov, P. V. van Ooijen, M. Oudkerk, 2022, European Radiology.

Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders

Mykola Pechenizkiy, D. Mocanu, Zahra Atashgahi, 2021, Machine Learning.

DFM-X: Augmentation by Leveraging Prior Knowledge of Shortcut Learning

N. Strisciuglio, C. Brune, Shunxin Wang, 2023, 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).

What do neural networks learn in image classification? A frequency shortcut perspective

C. Brune, N. Strisciuglio, Shunxin Wang, 2023, 2023 IEEE/CVF International Conference on Computer Vision (ICCV).