13 citations · 25 across the 4 of their papers we have counts for
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hep-lat2022★ 13 cited
Applications of Machine Learning to Lattice Quantum Field Theory
Denis Boyda, Salvatore Calì, Sam Foreman +8
There is great potential to apply machine learning in the area of numerical lattice quantum field theory, but full exploitation of that potential will require new strategies. In th…
hep-lat2022★ 1 cited
LeapfrogLayers: A Trainable Framework for Effective Topological Sampling
Sam Foreman, Xiao-Yong Jin, James C. Osborn
We introduce LeapfrogLayers, an invertible neural network architecture that can be trained to efficiently sample the topology of a 2D lattice gauge theory. We show an improv…