  We consider a class of visual analogical reasoning problems that involve
discovering the sequence of transformations by which pairs of input/output
images are related, so as to analogously transform future inputs. This program
synthesis task can be easily solved via symbolic search. Using a variation of
the `neural analogical reasoning' approach of (Velickovic and Blundell 2021),
we instead search for a sequence of elementary neural network transformations
that manipulate distributed representations derived from a symbolic space, to
which input images are directly encoded. We evaluate the extent to which our
`neural reasoning' approach generalizes for images with unseen shapes and
positions.

Machine Learning;Artificial Intelligence