scholarly journals End-to-End Bootstrapping Neural Network for Entity Set Expansion

2020 ◽  
Vol 34 (05) ◽  
pp. 9402-9409
Author(s):  
Lingyong Yan ◽  
Xianpei Han ◽  
Ben He ◽  
Le Sun
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Substantial Improvement ◽  
High Order ◽  
Experimental Results ◽  
Target Category ◽  
Attention Network ◽  
Order Relations ◽  
Decoder Architecture ◽  
End To End

Bootstrapping for entity set expansion (ESE) has long been modeled as a multi-step pipelined process. Such a paradigm, unfortunately, often suffers from two main challenges: 1) the entities are expanded in multiple separate steps, which tends to introduce noisy entities and results in the semantic drift problem; 2) it is hard to exploit the high-order entity-pattern relations for entity set expansion. In this paper, we propose an end-to-end bootstrapping neural network for entity set expansion, named BootstrapNet, which models the bootstrapping in an encoder-decoder architecture. In the encoding stage, a graph attention network is used to capture both the first- and the high-order relations between entities and patterns, and encode useful information into their representations. In the decoding stage, the entities are sequentially expanded through a recurrent neural network, which outputs entities at each stage, and its hidden state vectors, representing the target category, are updated at each expansion step. Experimental results demonstrate substantial improvement of our model over previous ESE approaches.

scholarly journals Improved recurrent neural network-based manipulator control with remote center of motion constraints: Experimental results

2020 ◽  
Vol 131 ◽  
pp. 291-299 ◽  
Author(s):  
Hang Su ◽  
Yingbai Hu ◽  
Hamid Reza Karimi ◽  
Alois Knoll ◽  
Giancarlo Ferrigno ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Experimental Results ◽  
Motion Constraints ◽  
Manipulator Control

scholarly journals Recurrent neural network for end-to-end modeling of laminar-turbulent transition

2021 ◽  
Vol 2 ◽  
Author(s):  
Muhammad I. Zafar ◽  
Meelan M. Choudhari ◽  
Pedro Paredes ◽  
Heng Xiao
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Transition Model ◽  
Large Set ◽  
Two Dimensional ◽  
Turbulent Transition ◽  
Wide Range ◽  
Network Methods ◽  
End To End ◽  
Laminar Turbulent Transition

Abstract Accurate prediction of laminar-turbulent transition is a critical element of computational fluid dynamics simulations for aerodynamic design across multiple flow regimes. Traditional methods of transition prediction cannot be easily extended to flow configurations where the transition process depends on a large set of parameters. In comparison, neural network methods allow higher dimensional input features to be considered without compromising the efficiency and accuracy of the traditional data-driven models. Neural network methods proposed earlier follow a cumbersome methodology of predicting instability growth rates over a broad range of frequencies, which are then processed to obtain the N-factor envelope, and then, the transition location based on the correlating N-factor. This paper presents an end-to-end transition model based on a recurrent neural network, which sequentially processes the mean boundary-layer profiles along the surface of the aerodynamic body to directly predict the N-factor envelope and the transition locations over a two-dimensional airfoil. The proposed transition model has been developed and assessed using a large database of 53 airfoils over a wide range of chord Reynolds numbers and angles of attack. The large universe of airfoils encountered in various applications causes additional difficulties. As such, we provide further insights on selecting training datasets from large amounts of available data. Although the proposed model has been analyzed for two-dimensional boundary layers in this paper, it can be easily generalized to other flows due to embedded feature extraction capability of convolutional neural network in the model.

scholarly journals Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

2019 ◽  
Vol 11 (12) ◽  
pp. 247
Author(s):  
Xin Zhou ◽  
Peixin Dong ◽  
Jianping Xing ◽  
Peijia Sun
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Public Transportation ◽  
Prediction Accuracy ◽  
Arrival Time ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Support Vector ◽  
Data Set ◽  
Dynamic Factors

Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors should be added into the prediction model? Traditional prediction methods mainly use the arrival time and the distance between stations, but do not make full use of dynamic factors such as passenger number, dwell time, bus driving efficiency, etc. We propose a novel approach that takes full advantage of dynamic factors. Our approach is based on a Recurrent Neural Network (RNN). The experimental results indicate that a variety of prediction algorithms (such as Support Vector Machine, Kalman filter, Multilayer Perceptron, and RNN) have significantly improved performance after using dynamic factors. Further, we introduce RNN with an attention mechanism to adaptively select the most relevant input factors. Experiments demonstrate that the prediction accuracy of RNN with an attention mechanism is better than RNN with no attention mechanism when there are heterogeneous input factors. The experimental results show the superior performances of our approach on the data set provided by Jinan Public Transportation Corporation.

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