Neural Programmer-Interpreters
ICLR• 2016
Abstract
We propose the neural programmer-interpreter (NPI): a recurrent and
compositional neural network that learns to represent and execute programs. NPI
has three learnable components: a task-agnostic recurrent core, a persistent
key-value program memory, and domain-specific encoders that enable a single NPI
to operate in multiple perceptually diverse environments with distinct
affordances. By learning to compose lower-level programs to express
higher-level programs, NPI reduces sample complexity and increases
generalization ability compared to sequence-to-sequence LSTMs. The program
memory allows efficient learning of additional tasks by building on existing
programs. NPI can also harness the environment (e.g. a scratch pad with
read-write pointers) to cache intermediate results of computation, lessening
the long-term memory burden on recurrent hidden units. In this work we train
the NPI with fully-supervised execution traces; each program has example
sequences of calls to the immediate subprograms conditioned on the input.
Rather than training on a huge number of relatively weak labels, NPI learns
from a small number of rich examples. We demonstrate the capability of our
model to learn several types of compositional programs: addition, sorting, and
canonicalizing 3D models. Furthermore, a single NPI learns to execute these
programs and all 21 associated subprograms.