95 citations · 151 across the 4 of their papers we have counts for
6 papers
Reconstructing missing seismic data using Deep Learning
Dieuwertje Kuijpers, Ivan Vasconcelos, Patrick Putzky
In current seismic acquisition practice, there is an increasing drive for sparsely (in space) acquired data, often in irregular geometry. These surveys can trade off subsurface inf…
Invert to Learn to Invert
Patrick Putzky, Max Welling
Iterative learning to infer approaches have become popular solvers for inverse problems. However, their memory requirements during training grow linearly with model depth, limiting…
i-RIM applied to the fastMRI challenge
Patrick Putzky, Dimitrios Karkalousos, Jonas Teuwen +4
We, team AImsterdam, summarize our submission to the fastMRI challenge (Zbontar et al., 2018). Our approach builds on recent advances in invertible learning to infer models as pres…
Data-Driven Reconstruction of Gravitationally Lensed Galaxies using Recurrent Inference Machines
Warren R. Morningstar, Laurence Perreault Levasseur, Yashar D. Hezaveh +6
We present a machine learning method for the reconstruction of the undistorted images of background sources in strongly lensed systems. This method treats the source as a pixelated…
Analyzing interferometric observations of strong gravitational lenses with recurrent and convolutional neural networks
Warren R. Morningstar, Yashar D. Hezaveh, Laurence Perreault Levasseur +4
We use convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to estimate the parameters of strong gravitational lenses from interferometric observations. We exp…
Recurrent Inference Machines for Solving Inverse Problems
Patrick Putzky, Max Welling
Much of the recent research on solving iterative inference problems focuses on moving away from hand-chosen inference algorithms and towards learned inference. In the latter, the i…