Neural Random-Access Machines

Understand

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.