  Neural Turing Machines (NTM) contain memory component that simulates "working
memory" in the brain to store and retrieve information to ease simple
algorithms learning. So far, only linearly organized memory is proposed, and
during experiments, we observed that the model does not always converge, and
overfits easily when handling certain tasks. We think memory component is key
to some faulty behaviors of NTM, and better organization of memory component
could help fight those problems. In this paper, we propose several different
structures of memory for NTM, and we proved in experiments that two of our
proposed structured-memory NTMs could lead to better convergence, in term of
speed and prediction accuracy on copy task and associative recall task as in
(Graves et al. 2014).
