Neural Turing Machines

We extend the capabilities of neural networks by coupling them to external memory re-sources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-to-end, allowing it to be efficiently trained with gradient descent. Preliminary results demon-strate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.