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One of the main problems encountered so far with recurrent neural networks isthat they struggle to retain long-time information dependencies in theirrecurrent connections. Neural Turing Machines (NTMs) attempt to mitigate thisissue by providing the neural network with an external portion of memory, inwhich information can be stored and manipulated later on. The whole mechanismis differentiable end-to-end, allowing the network to learn how to utilise thislong-term memory via stochastic gradient descent. This allows NTMs to infersimple algorithms directly from data sequences. Nonetheless, the model can behard to train due to a large number of parameters and interacting componentsand little related work is present. In this work we use NTMs to learn andgeneralise two arithmetical tasks: binary addition and multiplication. Thesetasks are two fundamental algorithmic examples in computer science, and are alot more challenging than the previously explored ones, with which we aim toshed some light on the real capabilities on this neural model.

Learning Numeracy: Binary Arithmetic with Neural Turing Machines