# Neural Random Access Machines

### Venue

ICLR (2016) (to appear)

### Publication Year

2016

### Authors

Karol Kurach, Marcin Andrychowicz, Ilya Sutskever

### BibTeX

## Abstract

Deep Neural Networks (DNNs) have achieved great success in supervised learning
tasks mainly due to their “depth”, allowing DNNs to represent functions whose
implementation requires some sequential computation, which turned out to be
sufficient to solve many previously-intractable problems. Given that depth was a
key ingredient in the success of DNNs, it is plausible that much deeper neural
models — namely, models that are computationally uni- versal — would be able to
solve much harder problems using less training data. The Neural Turing Machine is
the first neural network model of this kind, which has been able to learn to solve
a number of algorithmic tasks from input-output examples using backpropagation.
Although the Neural Turing Machine is compu- tationally universal, it used a memory
addressing mechanism that does not allow for pointers, which makes the
implementation of a number of natural algorithms cumbersome. In this paper, we
propose and investigate a computationally universal model that can manipulate and
dereference pointers. Pointer manipulation is a natural opera- tion, so it is
interesting to determine whether they can be learned with backprop- agation as
well. We evaluate the new model on a number of simple algorithmic tasks whose
solution requires pointer manipulation and dereferencing. Our results show that the
proposed model can learn to solve algorithmic tasks of such type, provided that
they are not too difficult.