  We develop a first line of attack for solving programming competition-style
problems from input-output examples using deep learning. The approach is to
train a neural network to predict properties of the program that generated the
outputs from the inputs. We use the neural network's predictions to augment
search techniques from the programming languages community, including
enumerative search and an SMT-based solver. Empirically, we show that our
approach leads to an order of magnitude speedup over the strong non-augmented
baselines and a Recurrent Neural Network approach, and that we are able to
solve problems of difficulty comparable to the simplest problems on programming
competition websites.
