  We present a variational Monte Carlo method that solves the nuclear many-body
problem in the occupation number formalism exploiting an artificial neural
network representation of the ground-state wave function. A memory-efficient
version of the stochastic reconfiguration algorithm is developed to train the
network by minimizing the expectation value of the Hamiltonian. We benchmark
this approach against widely used nuclear many-body methods by solving a model
used to describe pairing in nuclei for different types of interaction and
different values of the interaction strength. Despite its polynomial
computational cost, our method outperforms coupled-cluster and provides
energies that are in excellent agreement with the numerically-exact full
configuration interaction values.
