2018 ImplementingNeuralTuringMachine

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Subject Headings: Neural Turing Machine; Memory-Augmented Neural Network, Memory-based Neural Network.

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Abstract

Neural Turing Machines (NTMs) are an instance of Memory Augmented Neural Networks, a new class of recurrent neural networks which decouple computation from memory by introducing an external memory unit. NTMs have demonstrated superior performance over Long Short-Term Memory Cells in several sequence learning tasks. A number of open source implementations of NTMs exist but are unstable during training and/or fail to replicate the reported performance of NTMs. This paper presents the details of our successful implementation of a NTM. Our implementation learns to solve three sequential learning tasks from the original NTM paper. We find that the choice of memory contents initialization scheme is crucial in successfully implementing a NTM. Networks with memory contents initialized to small constant values converge on average 2 times faster than the next best memory contents initialization scheme.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2018 ImplementingNeuralTuringMachineMark Collier
Joeran Beel
Implementing Neural Turing Machines10.1007/978-3-030-01424-7_102018
AuthorMark Collier + and Joeran Beel +
doi10.1007/978-3-030-01424-7_10 +
titleImplementing Neural Turing Machines +
year2018 +