Kien Do

460 citations
13 papers · 65 · h-index 5

Impact in

Papers in

    • Domain Adaptation and Few-Shot Learning 5
    • Topic Modeling 4
    • Adversarial Robustness in Machine Learning 3
    • Anomaly Detection Techniques and Applications 2
    • Natural Language Processing Techniques 2
    • Advanced Graph Neural Networks 2
    • Machine Learning and Algorithms 2

Kien Do

13 papers receiving 64 citations

Peers

Kien Do
Comparison fields: 5 of 35
  • Artificial Intelligence 52
  • Computer Vision and Pattern Recognition 22
  • Signal Processing 6
  • Computer Networks and Communications 12
  • Statistical and Nonlinear Physics 6
Replace Seiya Tokui with:
Seiya Tokui Japan
Ruikun Li Australia
Itai Dinur Israel
Chinnadhurai Sankar United States
Erik Zenner Denmark
Victor Gabillon France
Toru Akishita Japan
Steve Babbage
Shaohua Tan China
Kien Do relative to Seiya Tokui Japan Seiya Tokui's profile →
Citations per field
00.5×3.8×
Seiya Tokui · 1×
Citations per year

Countries citing papers authored by Kien Do

Since Specialization
Citations

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

Fields of papers citing papers by Kien Do

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 15 scholars most cited alongside Kien Do, 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 Kien Do Line = papers co-authored together Kien Do links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1 201816
2 201913
3 202410
4 20237
5 20244
6 20233
7 20233
8
Theory and Evaluation Metrics for Learning Disentangled Representations
20202
9 20242
10 20212
11
Matrix-centric Neural Networks.
20171
12 20231
13 20211

About Kien Do

Kien Do is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Computer Networks and Communications and Information Systems, having authored 13 papers that have together received 65 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (5 papers), Topic Modeling (4 papers), Adversarial Robustness in Machine Learning (3 papers), Anomaly Detection Techniques and Applications (2 papers), Natural Language Processing Techniques (2 papers), Advanced Graph Neural Networks (2 papers), Machine Learning and Algorithms (2 papers) and Network Security and Intrusion Detection (1 paper). The work is most often cited by research in Artificial Intelligence (52 citations), Computer Vision and Pattern Recognition (22 citations), Signal Processing (6 citations), Computer Networks and Communications (12 citations) and Statistical and Nonlinear Physics (6 citations). Kien Do has collaborated with scholars based in Australia, Vietnam and Denmark. Frequent co-authors include Svetha Venkatesh, Truyen Tran, Thin Nguyen, Toan Nguyen, Bac Le, Khoat Than, Duc Thanh Nguyen, Yang-Wai Chow, Tung Kieu and Truong V. Vu. Their work appears in journals such as Pattern Recognition Letters, Machine Learning, Expert Systems with Applications, Knowledge and Information Systems and Research Online (University of Wollongong).

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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