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Recurrent Neural Network Architectures

, 2017
In this chapter, we present three different recurrent neural network architectures that we employ for the prediction of real-valued time series. All the models reviewed in this chapter can be trained through the previously discussed backpropagation through time procedure.
Bianchi, Filippo Maria, Maiorino, Enrico, Kampffmeyer, Michael C., Rizzi, Antonello, Jenssen, Robert   +4 more
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SegRNN: Segment Recurrent Neural Network for Long-Term Time-Series Forecasting

IEEE Internet of Things Journal, 2023
With the proliferation of the Internet of Things (IoT) applications, advanced time-series forecasting techniques have become increasingly critical for managing and responding to complex temporal dynamics.
Shengsheng Lin, Weiwei Lin, Wentai Wu, Feiyu Zhao, Ruichao Mo, Hao-Tong Zhang   +5 more
semanticscholar   +1 more source

Hierarchically Gated Recurrent Neural Network for Sequence Modeling

Neural Information Processing Systems, 2023
Transformers have surpassed RNNs in popularity due to their superior abilities in parallel training and long-term dependency modeling. Recently, there has been a renewed interest in using linear RNNs for efficient sequence modeling.
Zhen Qin, Songlin Yang, Yi-Ran Zhong
semanticscholar   +1 more source

A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures

Neural Computation, 2019
Recurrent neural networks (RNNs) have been widely adopted in research areas concerned with sequential data, such as text, audio, and video. However, RNNs consisting of sigma cells or tanh cells are unable to learn the relevant information of input data ...
Yong Yu, Xiao-Sheng Si, Chang-Hua Hu, Jian-Xun Zhang   +3 more
semanticscholar   +1 more source

Flexible Recurrent Neural Networks

2021
We introduce two methods enabling recurrent neural networks (RNNs) to trade off accuracy for computational cost during the analysis of a sequence. This opens up the possibility to adapt RNNs in real time to changing computational constraints, such as when running on shared hardware with other processes or in mobile edge computing nodes.
Anne Lambert, Françoise Le Bolzer, François Schnitzler   +2 more
openaire   +1 more source

Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction

IEEE transactions on intelligent transportation systems (Print), 2021
Traffic prediction is a core problem in the intelligent transportation system and has broad applications in the transportation management and planning, and the main challenge of this field is how to efficiently explore the spatial and temporal ...
Kan Guo, Yongli Hu, Z. Qian, Hao Liu, Kecheng Zhang, Yanfeng Sun, Junbin Gao, Bao-Cai Yin   +7 more
semanticscholar   +1 more source

Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network

Knowledge Discovery and Data Mining, 2019
Industry devices (i.e., entities) such as server machines, spacecrafts, engines, etc., are typically monitored with multivariate time series, whose anomaly detection is critical for an entity's service quality management.
Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, Dan Pei   +5 more
semanticscholar   +1 more source

State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network

Energy, 2021
To reduce the influence of the measurement data noise on state of charge (SOC) estimation, a novel neural network method is proposed by combining an input data processing method with the conventional gated recurrent unit recurrent neural network (GRU-RNN)
Junxiong Chen, Xiong Feng, Lin Jiang, Qiao Zhu   +3 more
semanticscholar   +1 more source

Improved salient object detection using hybrid Convolution Recurrent Neural Network

Expert systems with applications, 2021
Salient object detection is a critical and active field that aims at the detection of objects in a video, however, it draws increased attention among researchers. With increasing dynamic video data, the performance of saliency object detection method has
N. V. Kousik, Yuvaraj Natarajan, R. Raja, Suresh Kallam, Rizwan Patan, Amir H. Gandomi   +5 more
semanticscholar   +1 more source

IDENTIFIABILITY OF RECURRENT NEURAL NETWORKS

Econometric Theory, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Al-Falou, A. A., Trummer, D.
openaire   +2 more sources