Bin Dai

3.9k citations
207 papers · 2.6k · 1 hit paper · h-index 26

Impact in

Papers in

Bin Dai

192 papers receiving 2.5k citations

Bin Dai's Hit Papers

Deep Reinforcement Learning: A Survey 2022 · 416 citations
4160+1+2Years since publication100200300400

Peers

Bin Dai
Comparison fields: 5 of 136
  • Automotive Engineering 610
  • Computer Vision and Pattern Recognition 945
  • Environmental Engineering 390
  • Geology 137
  • Instrumentation 70
Replace Yuxiang Sun with:
Yuxiang Sun China
Zhongwei Li China
Xiang Yu China
Wei Liu China
Hui Yuan China
Jian Wang China
Xu Zhao China
Tao Zhang China
Muhammad Shahzad Pakistan
Bin Dai relative to Yuxiang Sun China Yuxiang Sun's profile →
Citations per field
00.5×9.1×
Yuxiang Sun · 1×
Citations per year

Countries citing papers authored by Bin Dai

Since Specialization
Citations

This map shows the geographic impact of Bin Dai's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Bin Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bin Dai more than expected).

Fields of papers citing papers by Bin Dai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bin Dai. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Bin Dai. The network helps show where Bin Dai may publish in the future.

Co-authors

The 25 scholars most cited alongside Bin Dai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Bin Dai Line = papers co-authored together Bin Dai links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 207 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Deep Reinforcement Learning: A Survey
Hit paper breakdown →
2022416
2 2017132
3 2013108
4 201583
5 201865
6 201961
7 201361
8 201651
9 201449
10 201648
11 201847
12 202245
13 201844
14 201141
15 201439
16 201637
17 200935
18 201034
19 202333
20 201533

About Bin Dai

Bin Dai is a scholar working on Computer Vision and Pattern Recognition, Automotive Engineering, Aerospace Engineering, Computer Networks and Communications and Artificial Intelligence, having authored 207 papers that have together received 2.6k indexed citations. Recurring topics across this work include Autonomous Vehicle Technology and Safety (46 papers), Robotics and Sensor-Based Localization (36 papers), Robotic Path Planning Algorithms (21 papers), Advanced Neural Network Applications (20 papers), Video Surveillance and Tracking Methods (20 papers), Remote Sensing and LiDAR Applications (19 papers), Advanced Vision and Imaging (19 papers) and Cooperative Communication and Network Coding (17 papers). The work is most often cited by research in Automotive Engineering (610 citations), Computer Vision and Pattern Recognition (945 citations), Environmental Engineering (390 citations), Geology (137 citations) and Instrumentation (70 citations). Bin Dai has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Daxue Liu, Tao Wu, Liang Xiao, Xin Xu, Ruili Wang, Tongtong Chen, Dawei Zhao, Yuqiang Fang, Xu Wang and Xingxing Liang. Their work appears in journals such as IEEE Transactions on Intelligent Vehicles, IEEE Transactions on Intelligent Transportation Systems, Journal of Network and Computer Applications, Information Sciences and IEEE Robotics and Automation Letters.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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