255 citations · 497 across the 19 of their papers we have counts for
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nucl-th2021★ 255 cited
Machine Learning in Nuclear Physics
Amber Boehnlein, Markus Diefenthaler, Cristiano Fanelli +15
Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics…
physics.comp-ph2021★ 1 cited
Implicit Quantile Neural Networks for Jet Simulation and Correction
Braden Kronheim, Michelle P. Kuchera, Harrison B. Prosper +1
Reliable modeling of conditional densities is important for quantitative scientific fields such as particle physics. In domains outside physics, implicit quantile neural networks (…