no code implementations • 15 Aug 2019 • Iris A. M. Huijben, Bastiaan S. Veeling, Kees Janse, Massimo Mischi, Ruud J. G. van Sloun
Compressed sensing emerged as a popular framework for sparse signal reconstruction from a small set of compressed measurements.
no code implementations • 22 Apr 2020 • Iris A. M. Huijben, Bastiaan S. Veeling, Ruud J. G. van Sloun
Compressed sensing (CS) MRI relies on adequate undersampling of the k-space to accelerate the acquisition without compromising image quality.
1 code implementation • ICLR 2020 • Iris A. M. Huijben, Bastiaan S. Veeling, Ruud J. G. van Sloun
The field of deep learning is commonly concerned with optimizing predictive models using large pre-acquired datasets of densely sampled datapoints or signals.
no code implementations • ICLR 2021 • Ties van Rozendaal, Iris A. M. Huijben, Taco S. Cohen
At a high level, neural compression is based on an autoencoder that tries to reconstruct the input instance from a (quantized) latent representation, coupled with a prior that is used to losslessly compress these latents.
no code implementations • 26 May 2021 • Lizeth Gonzalez-Carabarin, Iris A. M. Huijben, Bastiaan S. Veeling, Alexandre Schmid, Ruud J. G. van Sloun
Relevantly, the non-magnitude-based nature of DPP allows for joint optimization of pruning and weight quantization in order to even further compress the network, which we show as well.
1 code implementation • 4 Oct 2021 • Iris A. M. Huijben, Wouter Kool, Max B. Paulus, Ruud J. G. van Sloun
The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by its unnormalized (log-)probabilities.
1 code implementation • 31 May 2022 • Iris A. M. Huijben, Arthur A. Nijdam, Sebastiaan Overeem, Merel M. van Gilst, Ruud J. G. van Sloun
Continuous monitoring with an ever-increasing number of sensors has become ubiquitous across many application domains.