  Millimeter wave (mmWave) localization algorithms exploit the quasi-optical
propagation of mmWave signals, which yields sparse angular spectra at the
receiver. Geometric approaches to angle-based localization typically require to
know the map of the environment and the location of the access points. Thus,
several works have resorted to automated learning in order to infer a device's
location from the properties of the received mmWave signals. However,
collecting training data for such models is a significant burden. In this work,
we propose a shallow neural network model to localize mmWave devices indoors.
This model requires significantly fewer weights than those proposed in the
literature. Therefore, it is amenable for implementation in
resource-constrained hardware, and needs fewer training samples to converge. We
also propose to relieve training data collection efforts by retrieving
(inherently imperfect) location estimates from geometry-based mmWave
localization algorithms. Even in this case, our results show that the proposed
neural networks perform as good as or better than state-of-the-art algorithms.
