  In this paper, we propose and investigate a new neural network architecture
called Neural Random Access Machine. It can manipulate and dereference pointers
to an external variable-size random-access memory. The model is trained from
pure input-output examples using backpropagation.
  We evaluate the new model on a number of simple algorithmic tasks whose
solutions require pointer manipulation and dereferencing. Our results show that
the proposed model can learn to solve algorithmic tasks of such type and is
capable of operating on simple data structures like linked-lists and binary
trees. For easier tasks, the learned solutions generalize to sequences of
arbitrary length. Moreover, memory access during inference can be done in a
constant time under some assumptions.
