This article serves to briefly outline the design of the Neural Turing Machine (NTM), a backpropogatable architecture that can (among many possibilities) learn to dynamically execute programs.
I’ve added some specifications about the NTM’s architecture that the paper excludes for the sake of generality. These will be discussed upon presentation.
The Neural Turing Machine was proposed by Graves et al. as a Turing-Complete network capable of learning (rather complex) programs. Inspired by the sequential nature of the brain, and the large, addressable memory of the computer.
The NTM is composed of five modules:
Let’s do this…
We all know LSTM’s are super powerful; So, we should know how they work and how to use them.
The gates are defined as: