Ruiwen Zhang

Orcid: 0009-0007-0314-552X

According to our database1, Ruiwen Zhang authored at least 12 papers between 2013 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Rapid poverty estimation using ready-to-use mobile phone data: An application to Côte d'Ivoire.
Proceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies, 2024

2022
A Novel Gesture Recognition Technique based on sEMG Armband and Leap Motion Controller.
Proceedings of the IEEE International Conference on Robotics and Biomimetics, 2022

2021
Phase Space Reconstruction Network for Lane Intrusion Action Recognition.
Proceedings of the International Joint Conference on Neural Networks, 2021

Lane Intrusion Behaviors Dataset: Action Recognition in Real-world Highway Scenarios for Self-driving.
Proceedings of the International Joint Conference on Neural Networks, 2021

PVGNet: A Bottom-Up One-Stage 3D Object Detector With Integrated Multi-Level Features.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
UAVs vs. Pirates: An Anticipatory Swarm Monitoring Method Using an Adaptive Pheromone Map.
ACM Trans. Auton. Adapt. Syst., 2020

Multi Scale Temporal Graph Networks For Skeleton-based Action Recognition.
CoRR, 2020

2018
UAVs versus Pirates: A Pheromone-based Swarm Monitoring Method.
Proceedings of the IEEE International Conference on Robotics and Biomimetics, 2018

2016
Absolute Fused Lasso and Its Application to Genome-Wide Association Studies.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

Successive Ray Refinement and Its Application to Coordinate Descent for Lasso.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2016, 2016

2015
Successive Ray Refinement and Its Application to Coordinate Descent for LASSO.
CoRR, 2015

2013
Massively parallel feature selection: an approach based on variance preservation.
Mach. Learn., 2013


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