Title: Learning to Infer Graphics Programs from Hand-Drawn Images. Abstract:   We introduce a model that learns to convert simple hand drawings
  into graphics programs written in a subset of \LaTeX.~The model
  combines techniques from deep learning and program synthesis.  We
  learn a convolutional neural network that proposes plausible drawing
  primitives that explain an image. These drawing primitives are like
  a trace of the set of primitive commands issued by a graphics
  program. We learn a model that uses program synthesis techniques to
  recover a graphics program from that trace. These programs have
  constructs like variable bindings, iterative loops, or simple kinds
  of conditionals. With a graphics program in hand, we can correct
  errors made by the deep network and extrapolate drawings.  Taken
  together these results are a step towards agents that induce useful,
  human-readable programs from perceptual input.