86 citations · 153 across the 3 of their papers we have counts for
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astro-ph.IM2019★ 2 cited
ConvoSource: Radio-Astronomical Source-Finding with Convolutional Neural Networks
V. Lukic, F. De Gasperin, M. Brüggen
Finding and classifying astronomical sources is key in the scientific exploitation of radio surveys. Source-finding usually involves identifying the parts of an image belonging to…
astro-ph.IM2019★ 65 cited
Morphological classification of radio galaxies: Capsule Networks versus Convolutional Neural Networks
V. Lukic, M. Brüggen, B. Mingo +3
Next-generation radio surveys will yield an unprecedented amount of data, warranting analysis by use of machine learning techniques. Convolutional neural networks are the deep lear…
astro-ph.IM2018★ 86 cited
Radio Galaxy Zoo: Compact and extended radio source classification with deep learning
V. Lukic, M. Brüggen, J. K. Banfield +4
Machine learning techniques have been increasingly useful in astronomical applications over the last few years, for example in the morphological classification of galaxies. Convolu…