  The generalized thimble method to treat field theories with sign problems
requires repeatedly solving the computationally-expensive holomorphic flow
equations. We present a machine learning technique to bypass this problem. The
central idea is to obtain a few field configurations via the flow equations to
train a feed-forward neural network. The trained network defines a new manifold
of integration which reduces the sign problem and can be rapidly sampled. We
present results for the $1+1$ dimensional Thirring model with Wilson fermions
on sizable lattices. In addition to the gain in speed, the parameterization of
the integration manifold we use avoids the "trapping" of Monte Carlo chains
which plagues large-flow calculations, a considerable shortcoming of the
previous attempts.
