Bin Dong

551 citations
20 papers · 128 · h-index 7

Impact in

Papers in

Bin Dong

19 papers receiving 123 citations

Peers

Bin Dong
Comparison fields: 5 of 53
  • Health Informatics 4
  • Artificial Intelligence 80
  • Signal Processing 24
  • Experimental and Cognitive Psychology 21
  • Computer Vision and Pattern Recognition 27
Replace Philip John Gorinski with:
Philip John Gorinski Sweden
Brian Dolhansky United States
Mahmoud Al Ismail United States
Ramona Comanescu United Kingdom
Aida Nematzadeh Canada
Cristian Canton Ferrer United States
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Arjun Akula United States
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Arun Babu India
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Citations per field
00.5×
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Citations per year

Countries citing papers authored by Bin Dong

Since Specialization
Citations

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

Fields of papers citing papers by Bin Dong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown
#Work
1 201644
2 201916
3
You Only Propagate Once: Painless Adversarial Training Using Maximal Principle
201912
4 202110
5 202310
6 20168
7
SRCB Entity Discovery and Linking (EDL) and Event Nugget Systems for TAC 2017.
20176
8 20203
9
Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View
20193
10 20243
11
SRCB Neural Machine Translation Systems in WAT 2018
20182
12 20212
13 20212
14 20132
15 20201
16 20211
17 20151
18 20211
19 20191
20 20250

About Bin Dong

Bin Dong is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Radiology, Nuclear Medicine and Imaging and Statistical and Nonlinear Physics, having authored 20 papers that have together received 128 indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Natural Language Processing Techniques (6 papers), Speech and Audio Processing (3 papers), Speech Recognition and Synthesis (3 papers), Computational Physics and Python Applications (2 papers), Multimodal Machine Learning Applications (2 papers), Advanced Neural Network Applications (2 papers) and Advanced Neuroimaging Techniques and Applications (1 paper). The work is most often cited by research in Health Informatics (4 citations), Artificial Intelligence (80 citations), Signal Processing (24 citations), Experimental and Cognitive Psychology (21 citations) and Computer Vision and Pattern Recognition (27 citations). Bin Dong has collaborated with scholars based in China, Germany and United States. Frequent co-authors include Eduardo Coutinho, Björn W. Schuller, Fabien Ringeval, Erik Marchi, Zixing Zhang, Shanshan Jiang, Hongyu Lin, Xianpei Han, Yiping Lu and Yaojie Lu. Their work appears in journals such as INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences, Journal of Applied Physics, Mechanical Systems and Signal Processing, Frontiers in Medicine and Theory and applications of categories.

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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