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Bingfeng Luo

ResearcherPublications, citations & collaboration network

Bingfeng Luo is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 57 works, 456 citations, an h-index of 11 and an i10-index of 14.

57
Works
456
Citations
11
h-index
14
i10-index

How has Bingfeng Luo's publication output changed over time?

ScholarIQpublication output · 2006–2023

Output grew0% over the shown period — from 1 works in 2006 to 1 in 2023.

1
1
1
2
3
2
1
2006200820092017201820222023

What are the most-cited papers on Bingfeng Luo?

ScholarIQmost cited works
Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix
Bingfeng Luo, Yansong Feng, Zheng Wang, Zhanxing Zhu, Songfang Huang, Rui Yan, Dongyan Zhao
2017104 CitationsOPEN ACCESS
Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding
Bingfeng Luo, Yansong Feng, Zheng Wang, Songfang Huang, Rui Yan, Dongyan Zhao
201849 CitationsOPEN ACCESS
A PORTABLE RAMAN SYSTEM FOR THE IDENTIFICATION OF FOODBORNE PATHOGENIC BACTERIA
Bingfeng Luo, Min Lin
Journal of Rapid Methods & Automation in Microbiology. 200845 Citations
Monoclonal antibodies binding to the cell surface of Listeria monocytogenes serotype 4b
Min Lin, Dobrila Todoric, Maria Mallory, Bingfeng Luo, Erin Trottier, Hanhong Dan
S154078021. 200630 Citations
Playing 20 Question Game with Policy-Based Reinforcement Learning
Huang Hu, Xianchao Wu, Bingfeng Luo, Chongyang Tao, Can Xu, Wei Biao Wu, Zhan Chen
201826 CitationsOPEN ACCESS

Related on ScholarIQ

University of Hong Kong - Shenzhen Hospital
Institution
Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix
Paper
Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding
Paper
A PORTABLE RAMAN SYSTEM FOR THE IDENTIFICATION OF FOODBORNE PATHOGENIC BACTERIA
Paper
Monoclonal antibodies binding to the cell surface of Listeria monocytogenes serotype 4b
Paper
Playing 20 Question Game with Policy-Based Reinforcement Learning
Paper
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