Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI. - Iris A. M. Huijben; Bastiaan S. Veeling; Ruud J. G. van Sloun (ICASSP, 2020)

Learning Task-Based Analog-to-Digital Conversion for MIMO Receivers. - Nir Shlezinger; Ruud J. G. van Sloun; Iris A. M. Huijben; Georgee Tsintsadze; Yonina C. Eldar (ICASSP, 2020)

Deep probabilistic subsampling for task-adaptive compressed sensing. - Iris A. M. Huijben; Bastiaan S. Veeling; Ruud J. G. van Sloun (ICLR, 2020)

Learning Sub-Sampling and Signal Recovery With Applications in Ultrasound Imaging. - Iris A. M. Huijben; Bastiaan S. Veeling; Kees Janse; Massimo Mischi; Ruud J. G. van Sloun (IEEE Trans. Medical Imaging, 2020)

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound. - Subhankar Roy; Willi Menapace; Sebastiaan Oei; Ben Luijten; Enrico Fini; Cristiano Saltori; Iris A. M. Huijben; Nishith Chennakeshava; Federico Mento; Alessandro Sentelli; Emanuele Peschiera; Riccardo Trevisan; et al. (IEEE Trans. Medical Imaging, 2020)

Overfitting for Fun and Profit: Instance-Adaptive Data Compression. - Ties van Rozendaal; Iris A. M. Huijben; Taco Cohen (ICLR, 2021)

Active Deep Probabilistic Subsampling. - Hans Van Gorp; Iris A. M. Huijben; Bastiaan S. Veeling; Nicola Pezzotti; Ruud J. G. van Sloun (ICML, 2021)

A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning. - Iris A. M. Huijben; Wouter Kool 002; Max B. Paulus; Ruud J. G. van Sloun (CoRR, 2021)

Self-Organizing Maps for Contrastive Embeddings of Sleep Recordings. - Iris A. M. Huijben; Arthur A. Nijdam; Lieke W. A. Hermans; Sebastiaan Overeem; Merel M. van Gilst; Ruud J. G. van Sloun (EMBC, 2022)

Contrastive Predictive Coding for Anomaly Detection of Fetal Health from the Cardiotocogram. - Bert de Vries; Iris A. M. Huijben; René D. Kok; Ruud J. G. van Sloun; Rik Vullings (ICASSP, 2022)

SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series. - Iris A. M. Huijben; Arthur Andreas Nijdam; Sebastiaan Overeem; Merel M. van Gilst; Ruud van Sloun (ICML, 2023)

A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning. - Iris A. M. Huijben; Wouter Kool 001; Max B. Paulus; Ruud J. G. van Sloun (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Attention on Sleep Stage Specific Characteristics. - Iris A. M. Huijben; Sebastiaan Overeem; Merel M. van Gilst; Ruud J. G. van Sloun (EMBC, 2024)

Residual Quantization with Implicit Neural Codebooks. - Iris A. M. Huijben; Matthijs Douze; Matthew J. Muckley; Ruud van Sloun; Jakob Verbeek (ICML, 2024)

Dynamic Probabilistic Pruning: A General Framework for Hardware-Constrained Pruning at Different Granularities. - Lizeth Gonzalez-Carabarin; Iris A. M. Huijben; Bas Veeling; Alexandre Schmid; Ruud J. G. van Sloun (IEEE Trans. Neural Networks Learn. Syst., 2024)

Learning Structured Compressed Sensing with Automatic Resource Allocation. - Han Wang 052; Eduardo Pérez; Iris A. M. Huijben; Hans Van Gorp; Ruud van Sloun; Florian Römer (ICASSP, 2025)

Conditional Contrastive Predictive Coding for Assessment of Fetal Health From the Cardiotocogram. - Ivar R. de Vries; Raoul Melaet; Iris A. M. Huijben; Judith O. E. H. Van Laar; René D. Kok; S. Guid Oei; Ruud J. G. van Sloun; Rik Vullings (IEEE J. Biomed. Health Informatics, 2025)