144 citations · 169 across the 3 of their papers we have counts for
4 papers
LEAN: graph-based pruning for convolutional neural networks by extracting longest chains
Richard Schoonhoven, Allard A. Hendriksen, Daniël M. Pelt +1
Neural network pruning techniques can substantially reduce the computational cost of applying convolutional neural networks (CNNs). Common pruning methods determine which convoluti…
Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D Computed Tomography
Marinus J. Lagerwerf, Allard A. Hendriksen, Jan-Willem Buurlage +1
At X-ray beamlines of synchrotron light sources, the achievable time-resolution for 3D tomographic imaging of the interior of an object has been reduced to a fraction of a second,…
Noise2Inverse: Self-supervised deep convolutional denoising for tomography
Allard A. Hendriksen, Daniel M. Pelt, K. Joost Batenburg
Recovering a high-quality image from noisy indirect measurements is an important problem with many applications. For such inverse problems, supervised deep convolutional neural net…
Optional Stopping with Bayes Factors: a categorization and extension of folklore results, with an application to invariant situations
Allard Hendriksen, Rianne de Heide, Peter Grünwald
It is often claimed that Bayesian methods, in particular Bayes factor methods for hypothesis testing, can deal with optional stopping. We first give an overview, using elementary p…