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Neural Stored-program Memory

2019-05-25ICLR 2020Code Available0· sign in to hype

Hung Le, Truyen Tran, Svetha Venkatesh

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Abstract

Neural networks powered with external memory simulate computer behaviors. These models, which use the memory to store data for a neural controller, can learn algorithms and other complex tasks. In this paper, we introduce a new memory to store weights for the controller, analogous to the stored-program memory in modern computer architectures. The proposed model, dubbed Neural Stored-program Memory, augments current memory-augmented neural networks, creating differentiable machines that can switch programs through time, adapt to variable contexts and thus resemble the Universal Turing Machine. A wide range of experiments demonstrate that the resulting machines not only excel in classical algorithmic problems, but also have potential for compositional, continual, few-shot learning and question-answering tasks.

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DatasetModelMetricClaimedVerifiedStatus
bAbiNUTMMean Error Rate5.6Unverified

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