120 citations · 223 across the 24 of their papers we have counts for
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cond-mat.mtrl-sci2020★ 12 cited
Interpretable, calibrated neural networks for analysis and understanding of inelastic neutron scattering data
Keith T. Butler, Manh Duc Le, Jeyarajan Thiyagalingam +1
Deep neural networks provide flexible frameworks for learning data representations and functions relating data to other properties and are often claimed to achieve 'super-human' pe…
astro-ph.CO2020★ 11 cited
Deep learning insights into cosmological structure formation
Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen +2
The evolution of linear initial conditions present in the early universe into extended halos of dark matter at late times can be computed using cosmological simulations. However, a…