Dynamic Cell Structure via Recursive-Recurrent Neural Networks

Xin Qian; Matthew Kennedy; Diego Klabjan

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arXiv · 1905.10540

Dynamic Cell Structure via Recursive-Recurrent Neural Networks

Xin Qian·Matthew Kennedy·Diego Klabjan

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Abstract

In a recurrent setting, conventional approaches to neural architecture search find and fix a general model for all data samples and time steps. We propose a novel algorithm that can dynamically search for the structure of cells in a recurrent neural network model. Based on a combination of recurrent and recursive neural networks, our algorithm is able to construct customized cell structures for each data sample and time step, allowing for a more efficient architecture search than existing models. Experiments on three common datasets show that the algorithm discovers high-performance cell architectures and achieves better prediction accuracy compared to the GRU structure for language modelling and sentiment analysis.

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Xin Qian, Matthew Kennedy, Diego Klabjan. 2019-05-25. Dynamic Cell Structure via Recursive-Recurrent Neural Networks. https://arxiv.org/abs/1905.10540

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