58 citations · 126 across the 8 of their papers we have counts for
9 papers
IDSD: Iterative Deep-Learning-Based Signal Decomposition
Iris Huijben, Joël Karel, Ralf Peeters +1
Data-driven signal decomposition methods decompose a signal into its underlying components in a flexible and adaptive way, taking into account the signal characteristics. Here we f…
Learning Structured Compressed Sensing with Automatic Resource Allocation
Han Wang, Eduardo Pérez, Iris A. M. Huijben +3
Multidimensional data acquisition often requires extensive time and poses significant challenges for hardware and software regarding data storage and processing. Rather than design…
Residual Quantization with Implicit Neural Codebooks
Iris A. M. Huijben, Matthijs Douze, Matthew Muckley +2
Vector quantization is a fundamental operation for data compression and vector search. To obtain high accuracy, multi-codebook methods represent each vector using codewords across…
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series
Iris A. M. Huijben, Arthur A. Nijdam, Sebastiaan Overeem +2
Continuous monitoring with an ever-increasing number of sensors has become ubiquitous across many application domains. However, acquired time series are typically high-dimensional…
A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning
Iris A. M. Huijben, Wouter Kool, Max B. Paulus +1
The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by its unnormalized (log-)probabilities. Over the past years, the machine learning communit…
Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities
Lizeth Gonzalez-Carabarin, Iris A. M. Huijben, Bastiaan S. Veeling +2
Unstructured neural network pruning algorithms have achieved impressive compression rates. However, the resulting - typically irregular - sparse matrices hamper efficient hardware…
Overfitting for Fun and Profit: Instance-Adaptive Data Compression
Ties van Rozendaal, Iris A. M. Huijben, Taco S. Cohen
Neural data compression has been shown to outperform classical methods in terms of performance, with results still improving rapidly. At a high level, neural compression is ba…
Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI
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. Consequently, the design of optimal sa…
Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging
Iris A. M. Huijben, Bastiaan S. Veeling, Kees Janse +2
Limitations on bandwidth and power consumption impose strict bounds on data rates of diagnostic imaging systems. Consequently, the design of suitable (i.e. task- and data-aware) co…