5 citations · 12 across the 3 of their papers we have counts for
4 papers
A self-supervised, physics-aware, Bayesian neural network architecture for modelling galaxy emission-line kinematics
James M. Dawson, Timothy A. Davis, Edward L. Gomez +1
In the upcoming decades large facilities, such as the SKA, will provide resolved observations of the kinematics of millions of galaxies. In order to assist in the timely exploitati…
Using machine learning to study the kinematics of cold gas in galaxies
James M. Dawson, Timothy A. Davis, Edward L. Gomez +3
Next generation interferometers, such as the Square Kilometre Array, are set to obtain vast quantities of information about the kinematics of cold gas in galaxies. Given the volume…
Radiomic Feature Stability Analysis based on Probabilistic Segmentations
Christoph Haarburger, Justus Schock, Daniel Truhn +4
Identifying image features that are robust with respect to segmentation variability and domain shift is a tough challenge in radiomics. So far, this problem has mainly been tackled…
Super-realtime facial landmark detection and shape fitting by deep regression of shape model parameters
Marcin Kopaczka, Justus Schock, Dorit Merhof
We present a method for highly efficient landmark detection that combines deep convolutional neural networks with well established model-based fitting algorithms. Motivated by esta…