1 code implementation • NeurIPS 2019 • Patrick Putzky, Max Welling
Iterative learning to infer approaches have become popular solvers for inverse problems.
1 code implementation • 20 Oct 2019 • Patrick Putzky, Dimitrios Karkalousos, Jonas Teuwen, Nikita Miriakov, Bart Bakker, Matthan Caan, Max Welling
We, team AImsterdam, summarize our submission to the fastMRI challenge (Zbontar et al., 2018).
no code implementations • 5 Jan 2019 • Warren R. Morningstar, Laurence Perreault Levasseur, Yashar D. Hezaveh, Roger Blandford, Phil Marshall, Patrick Putzky, Thomas D. Rueter, Risa Wechsler, Max Welling
We present a machine learning method for the reconstruction of the undistorted images of background sources in strongly lensed systems.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
no code implementations • 31 Jul 2018 • Warren R. Morningstar, Yashar D. Hezaveh, Laurence Perreault Levasseur, Roger D. Blandford, Philip J. Marshall, Patrick Putzky, Risa H. Wechsler
We use convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to estimate the parameters of strong gravitational lenses from interferometric observations.
Instrumentation and Methods for Astrophysics
4 code implementations • 13 Jun 2017 • 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.
no code implementations • NeurIPS 2014 • Patrick Putzky, Florian Franzen, Giacomo Bassetto, Jakob H. Macke
Here, we present a statistical model for extracting hierarchically organised neural population states from multi-channel recordings of neural spiking activity.