@inproceedings{fe95943d34704da49dee2860c75c470a,
title = "Dynamic Cell Structure via Recursive-Recurrent Neural Networks",
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, leading to an efficient sample-level architecture search model. Experiments on three common datasets show that the algorithm discovers high-performance cell architectures and achieves better performance compared to the GRU structure for language modelling with the same network capacity.",
keywords = "Data-dependent and Time-varying Algorithm, Language Modelling, Recurrent Neural Networks, Recursive Neural Networks",
author = "Xin Qian and Matthew Kennedy and Diego Klabjan",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",
year = "2022",
doi = "10.1109/BigData55660.2022.10021136",
language = "English (US)",
series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1336--1349",
editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",
booktitle = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",
address = "United States",
}