Abstract: Embodiments are directed to probabilistic neural programs, a framework for program induction that permits flexible specification of the computational model and inference algorithm, while simultaneously enabling the use of deep neural networks. The approach implemented by one or more embodiments builds on computation graph frameworks for specifying neural networks by adding an operator for weighted nondeterministic choice that is used to specify the computational model. Thus, a program sketch describes both the decisions to be made and the architecture of the neural network used to score these decisions. The computation graph interacts with nondeterminism: the scores produced by the neural network determine the weights of nondeterministic choices, while the choices determine the network's architecture.