Neural Turing Machines

We discuss Neural Turing Machine(NTM), an architecture proposed by Graves et al. in DeepMind. NTMs are designed to solve tasks that require writing to and retrieving information from an external memory, which makes it resemble a working memory system that can be described by short-term storage(memory) of information and its rule-based manipulation. Compared with RNN structure with internal memory, NTMs utilize attentional mechanisms to efficiently read and write an external memory, which makes them a more favorable choice for capturing long-range dependencies. But, as we will see, these two are not independent of each other and can be combined to form a more powerful architecture. The overall architecture of NTM is demonstrated in Figure 1, where the controller is a general neural network, an MLP or RNN, which receives inputs and previous read vectors and omits outputs in response.