Search Results for author: Iris A. M. Huijben

Found 7 papers, 2 papers with code

A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning

1 code implementation4 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.

BIG-bench Machine Learning

Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities

no code implementations26 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.

Image Classification Network Pruning +1

Overfitting for Fun and Profit: Instance-Adaptive Data Compression

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.

Data Compression Image Compression +1

Deep probabilistic subsampling for task-adaptive compressed sensing

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.

Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI

no code implementations22 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.

Image Reconstruction

Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging

no code implementations15 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.

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