Learning Explicit and Implicit Knowledge with Differentiable Neural Computer

Neural Network can perform various of tasks well after learning process, but still have limitations in remembering. This is due to very limited memory. Differentiable Neural computer or DNC is proven to address the problem. DNC consist of Neural Network which associated with an external memory module that works like a tape on an accessible Turing Machine. DNC can solve simple problems that require memory, such as copy, graph, and Question Answering. DNC learns the algorithm to accomplish the task based on input and output. In this research, DNC with MLP or Multi-Layer Perceptron as the controller is compared with MLP only. The aim of this investigation is to test the ability of the neural network to learn explicit and implicit knowledge at once. The tasks are sequence classification and sequence addition of MMST handwritten digits. The results show that MLP which has an external memory-is much better than without external memory to process sequence data. The results also show that DNC as a fully differentiable system can solve the problem that requires explicit and implicit knowledge learning at once.