Neural Turing Machines: (arXiv:1410.5401)
"We extend the capabilities of neural networks by coupling them to external memory re- sources, which they can interact with by attentional processes."..."Preliminary results demon- strate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples."
Differentiable Neural Computer: DeepMind /on Nature
"Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer."..."Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read–write memory."
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes: (arXiv:1610.09027)
"Here, we present an end-to-end differentiable memory access scheme, which we call Sparse Access Memory (SAM), that retains the representational power of the original approaches whilst training efficiently with very large memories."