Reviews: Neural Program Meta-Induction

The paper is making an evaluation of several approaches for neural network-based induction of computer programs. The main proposal is on the use of meta-learning, in order to exploit knowledge learned from various tasks, for learning on a specific task with a small number of instances. For that purpose, three approaches are proposed: 1) transfer learning for adapting an existing model trained on a related task; 2) meta program induction, where the model has been trained to work on a variety of tasks; and 3) meta program adapted for a given task. The paper also proposes to make use of a synthetic domain Karel, which comes from an educational language for teach computer programming, which consists in moving a robot in a 2D grid through computer instructions. Results are reported with varying the number of instances used for program induction for the three meta approaches proposed plain method, with results showing some advantages with little number of instances.