Neural Random-Access Machines
Venue
CoRR, vol. abs/1511.06392 (2016)
Publication Year
2016
Authors
Karol Kurach, Marcin Andrychowicz, Ilya Sutskever
BibTeX
Abstract
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.
