  Reconstructing spectral functions from propagator data is difficult as
solving the analytic continuation problem or applying an inverse integral
transformation are ill-conditioned problems. Recent work has proposed using
neural networks to solve this problem and has shown promising results, either
matching or improving upon the performance of other methods. We generalize this
approach by not only reconstructing spectral functions, but also (possible)
pairs of complex poles or an infrared (IR) cutoff. We train our network on
physically motivated toy functions, examine the reconstruction accuracy and
check its robustness to noise. Encouraging results are found on both toy
functions and genuine lattice QCD data for the gluon propagator, suggesting
that this approach may lead to significant improvements over current
state-of-the-art methods.
