23 citations · 25 across the 4 of their papers we have counts for
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astro-ph.IM2019★ 2 cited
LRP2020: Machine Learning Advantages in Canadian Astrophysics
K. A. Venn, S. Fabbro, A Liu +12
The application of machine learning (ML) methods to the analysis of astrophysical datasets is on the rise, particularly as the computing power and complex algorithms become more po…
astro-ph.IM2019★ 23 cited
Cleaning our own Dust: Simulating and Separating Galactic Dust Foregrounds with Neural Networks
K. Aylor, M. Haq, L. Knox +2
Separating galactic foreground emission from maps of the cosmic microwave background (CMB), and quantifying the uncertainty in the CMB maps due to errors in foreground separation a…