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patents

SYSTEM AND METHOD

Published:

H. van Gorp, P. Fonseca, M. M. van Gilst, S. Overeem, and R. J. G. van Sloun [link] [pdf]

portfolio

Portfolio item number 1

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publications

Active deep probabilistic subsampling

Published in ICML, 2021

H. Van Gorp, I. A. M. Huijben, B. S. Veeling, N. Pezzotti, and R. J. G. Van Sloun, “Active deep probabilistic subsampling,” in International Conference on Machine Learning. PMLR, 2021, pp. 10509–10518. [link] [pdf]

Image denoising with deep unfolding and normalizing flows

Published in ICASSP, 2022

X. Wei, H. van Gorp, L. Gonzalez Carabarin, D. Freedman, Y. C. Eldar, and R. J. G. van Sloun, “Image denoising with deep unfolding and normalizing flows,” in 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). IEEE, 2022. [link] [pdf]

Deep unfolding with normalizing flow priors for inverse problems

Published in IEEE Transactions on Signal Processing, 2022

X. Wei, H. van Gorp, L. Gonzalez Carabarin, D. Freedman, Y. C. Eldar, and R. J. G. van Sloun, “Deep unfolding with normalizing flow priors for inverse problems,” IEEE Transactions on Signal Processing, vol. 70, pp. 2962–2971, 2022. [link] [pdf]

Hypnogram and Hypnodensity Analysis of REM Sleep Behaviour Disorder Using Both EEG and HRV-Based Sleep Staging Models

Published in Journal of Sleep Research, 2025

Jaap F. van der Aar, Merel M. van Gilst, Daan A. van den Ende, Hans van Gorp, Peter Anderer, Angelique Pijpers, Pedro Fonseca, Elisabetta Peri, Sebastiaan Overeem, “Hypnogram and Hypnodensity Analysis of REM Sleep Behaviour Disorder Using Both EEG and HRV-Based Sleep Staging Models”, Journal of Sleep Research, March 2025. [link] [pdf]

Removing Structured Noise using Diffusion Models

Published in Transactions on Machine Learning Research (TMLR), 2025

Tristan S.W. Stevens, Hans van Gorp, Faik C. Meral, Junseob Shin, Jason Yu, Jean-Luc Robert and Ruud J.G. van Sloun, “Removing Structured Noise using Diffusion Models,” Transactions on Machine Learning Research (TMLR), March 2025. [link] [pdf]

A Deep Generative Model for Five-Class Sleep Staging with Arbitrary Sensor Input

Published in IEEE Journal of Biomedical and Health Informatics, 2025

Hans van Gorp, Merel M van Gilst, Pedro Fonseca, Fokke B van Meulen, Johannes P van Dijk, Sebastiaan Overeem, and Ruud J G van Sloun, “A Deep Generative Model for Five-Class Sleep Staging with Arbitrary Sensor Input,” IEEE Journal of Biomedical and Health Informatics, Early Access 2025. [link] [pdf]

talks

teaching