  Recently developed neural network-based \emph{ab-initio} solutions (Pfau et.
al arxiv:1909.02487v2) for finding ground states of fermionic systems can
generate state-of-the-art results on a broad class of systems. In this work, we
improve the results for this Ansatz with Diffusion Monte Carlo. Additionally,
we introduce several modifications to the network (Fermi Net) and optimization
method (Kronecker Factored Approximate Curvature) that reduce the number of
required resources while maintaining or improving the modelling performance. In
terms of the model, we remove redundant computations and alter the way data is
handled in the permutation equivariant function. The Diffusion Monte Carlo
results exceed or match state-of-the-art performance for all systems
investigated: atomic systems Be-Ne, and the carbon cation C$^+$.
