  We discuss differences and similarities between variational Monte Carlo
approaches that use conventional and artificial neural network
parameterizations of the ground-state wave function for systems of fermions. We
focus on a relatively shallow neural-network architectures, the so called
restricted Boltzmann machine, and discuss unsupervised learning algorithms that
are suitable to model complicated many-body correlations. We analyze the
strengths and weaknesses of conventional and neural-network wave functions by
solving various circular quantum-dots systems. Results for up to 90 electrons
are presented and particular emphasis is placed on how to efficiently implement
these methods on homogeneous and heterogeneous high-performance computing
facilities.
