Making Neural Programming Architectures Generalize via Recursion

Understand

Empirically, neural networks that attempt to learn programs from data have exhibited poor generalizability.

• Moreover, it has traditionally been difficult to reason about the behavior of these models beyond a certain level of input complexity.
• In order to address these issues, we propose augmenting neural architectures with a key abstraction: recursion.
• As an application, we implement recursion in the Neural Programmer-Interpreter framework on four tasks: grade-school addition, bubble sort, topological sort, and quicksort.