Learning Arithmetic Operations With A Multistep Deep Learning

Deep neural networks are difficult to train when applied to tasks that can be expressed as algorithmic procedures. In this article, we propose to study how the explicit guidance of a network through all steps of the algorithm, using external memory and active choice of inputs, can improve its learning capability. The idea is to take inspiration from a child's learning and running through a procedure via interaction with an external support such as a paper. We show that this mechanism applied to a simple multilayer perceptron can significantly improve its performance when learning either a multi-digit addition or multiplication, which are simple but yet challenging operations to learn.