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Rotational Unit of Memory
[article]
2017
arXiv
pre-print
The concepts of unitary evolution matrices and associative memory have boosted the field of Recurrent Neural Networks (RNN) to state-of-the-art performance in a variety of sequential tasks. However, RNN still have a limited capacity to manipulate long-term memory. To bypass this weakness the most successful applications of RNN use external techniques such as attention mechanisms. In this paper we propose a novel RNN model that unifies the state-of-the-art approaches: Rotational Unit of Memory
arXiv:1710.09537v1
fatcat:efsi4uk7kzhgpg5a3uu5a55cna