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arXiv · 1806.08267

Complex Gated Recurrent Neural Networks

Moritz Wolter·Angela Yao
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Abstract

Complex numbers have long been favoured for digital signal processing, yet complex representations rarely appear in deep learning architectures. RNNs, widely used to process time series and sequence information, could greatly benefit from complex representations. We present a novel complex gated recurrent cell, which is a hybrid cell combining complex-valued and norm-preserving state transitions with a gating mechanism. The resulting RNN exhibits excellent stability and convergence properties and performs competitively on the synthetic memory and adding task, as well as on the real-world tasks of human motion prediction.

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Moritz Wolter, Angela Yao. 2018-06-21. Complex Gated Recurrent Neural Networks. https://arxiv.org/abs/1806.08267

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