Bin Bi

1.8k citations
31 papers · 1.2k · h-index 15

Impact in

Papers in

Bin Bi

31 papers receiving 1.2k citations

Peers

Bin Bi
Comparison fields: 5 of 117
  • Pollution 431
  • Industrial and Manufacturing Engineering 158
  • Molecular Medicine 78
  • Applied Microbiology and Biotechnology 23
  • Computer Vision and Pattern Recognition 267
Replace Qi Han with:
Qi Han China
Cong Ma China
Zhu Zhu China
A. P. Mathews United States
Yuwei Jia China
Lizeth Parra-Arroyo Mexico
Vinay Kumar Srivastava India
Volker Linnemann Germany
Yujing Xie China
Bin Bi relative to Qi Han China Qi Han's profile →
Citations per field
00.5×2.6×
Qi Han · 1×
Citations per year

Countries citing papers authored by Bin Bi

Since Specialization
Citations

This map shows the geographic impact of Bin Bi'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 Bi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bin Bi more than expected).

Fields of papers citing papers by Bin Bi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bin Bi. 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 Bi. The network helps show where Bin Bi may publish in the future.

Co-authors

The 25 scholars most cited alongside Bin Bi, 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 Bi Line = papers co-authored together Bin Bi links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019286
2 2018258
3 2022107
4 201396
5 202170
6 202160
7 201458
8 201930
9 201528
10 201826
11 202123
12 201920
13 202220
14 201620
15 201218
16
IDST at TREC 2019 Deep Learning Track: Deep Cascade Ranking with Generation-based Document Expansion and Pre-trained Language Modeling.
201914
17 201111
18 202110
19 202110
20 20099

About Bin Bi

Bin Bi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Pollution and Industrial and Manufacturing Engineering, having authored 31 papers that have together received 1.2k indexed citations. Recurring topics across this work include Topic Modeling (10 papers), Natural Language Processing Techniques (9 papers), Multimodal Machine Learning Applications (9 papers), Recommender Systems and Techniques (6 papers), Domain Adaptation and Few-Shot Learning (3 papers), Advanced Text Analysis Techniques (3 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Pharmaceutical and Antibiotic Environmental Impacts (3 papers). The work is most often cited by research in Pollution (431 citations), Industrial and Manufacturing Engineering (158 citations), Molecular Medicine (78 citations), Applied Microbiology and Biotechnology (23 citations) and Computer Vision and Pattern Recognition (267 citations). Bin Bi has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Shaoyong Lu, Xiaohui Liu, Xiaochun Guo, Ying Liu, Beidou Xi, Jian Zhang, Zhi Wang, Songfang Huang, Junghoo Cho and Weiliang Wang. Their work appears in journals such as Environmental Pollution, Chemosphere, Environmental Science and Pollution Research, PeerJ and Frontiers in Environmental Science.

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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