Search Results for author: Patrick Putzky

Found 6 papers, 3 papers with code

Invert to Learn to Invert

1 code implementation NeurIPS 2019 Patrick Putzky, Max Welling

Iterative learning to infer approaches have become popular solvers for inverse problems.

Image Reconstruction

i-RIM applied to the fastMRI challenge

1 code implementation20 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).

Data-Driven Reconstruction of Gravitationally Lensed Galaxies using Recurrent Inference Machines

no code implementations5 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

Analyzing interferometric observations of strong gravitational lenses with recurrent and convolutional neural networks

no code implementations31 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

Recurrent Inference Machines for Solving Inverse Problems

4 code implementations13 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.

Image Denoising Image Restoration +1

A Bayesian model for identifying hierarchically organised states in neural population activity

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.

Bayesian Inference

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