1 citations · 1 across the 1 of their papers we have counts for
2 papers
math.OC2020
Deep neural networks for inverse problems with pseudodifferential operators: an application to limited-angle tomography
Tatiana A. Bubba, Mathilde Galinier, Matti Lassas +3
We propose a novel convolutional neural network (CNN), called DONet, designed for learning pseudodifferential operators (DOs) in the context of linear inverse problems. Our s…
cs.LG2019★ 1 cited
Mise en abyme with artificial intelligence: how to predict the accuracy of NN, applied to hyper-parameter tuning
Giorgia Franchini, Mathilde Galinier, Micaela Verucchi
In the context of deep learning, the costliest phase from a computational point of view is the full training of the learning algorithm. However, this process is to be used a signif…