  We study the application of machine learning techniques for the detection of
the astrometric signature of dark matter substructure. In this proof of
principle a population of dark matter subhalos in the Milky Way will act as
lenses for sources of extragalactic origin such as quasars. We train {\it
ResNet-18}, a state-of-the-art convolutional neural network to classify angular
velocity maps of a population of quasars into lensed and no lensed classes. We
show that an SKA -like survey with extended operational baseline can be used to
probe the substructure content of the Milky Way, and demonstrate how axiomatic
attribution can be used to localize substructures in lensing maps.
