MACHINE LEARNING

Neural Turing Machines

Resource type
Authors/contributors
Title
Neural Turing Machines
Abstract
We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-to-end, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.
Publication
arXiv:1410.5401 [cs]
Date
2014-12-10
Accessed
2019-11-21T21:09:35Z
Library Catalog
Extra
ZSCC: 0001222 arXiv: 1410.5401
Citation
Graves, A., Wayne, G., & Danihelka, I. (2014). Neural Turing Machines. ArXiv:1410.5401 [Cs]. Retrieved from http://arxiv.org/abs/1410.5401
MODEL CHECKING AND STATE MACHINES
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