  Nuclear quantum many-body methods rely on integral transform techniques to
infer properties of electroweak response functions from ground-state
expectation values. Retrieving the energy dependence of these responses is
highly non-trivial, especially for quantum Monte Carlo methods, as it requires
inverting the Laplace transform -- a notoriously ill-posed problem. In this
work, we propose an artificial neural network architecture suitable for
accurate response function reconstruction with precise estimation of the
uncertainty of the inversion. We demonstrate the capabilities of this new
architecture benchmarking it against Maximum Entropy and previously developed
neural network methods designed for a similar task, paying particular attention
to its robustness against increasing noise in the input Euclidean responses.
