3 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
Efficient Learning of Lattice Gauge Theories with Fermions
Shreya Shukla, Yukari Yamauchi, Andrey Y. Lokhov +2
We introduce a learning method for recovering action parameters in lattice field theories. Our method is based on the minimization of a convex loss function constructed using the S…
Contour deformations for non-holomorphic actions
Scott Lawrence, Semeon Valgushev, Jianan Xiao +1
We show how contour deformations may be used to control the sign problem of lattice Monte Carlo calculations with non-holomorphic actions. Such actions arise naturally in quantum m…
Mitigating a discrete sign problem with extreme learning machines
Scott Lawrence, Yukari Yamauchi
An extreme learning machine is a neural network in which only the weights in the last layer are changed during training; for such networks training can be performed efficiently and…
Convex optimization and contour deformations
Scott Lawrence, Yukari Yamauchi
We discuss various formal aspects of contour deformations used to alleviate sign problems; most importantly, relating these contour deformations to a certain convex optimization pr…