Hao Kong

484 total citations
41 papers, 289 citations indexed

About

Hao Kong is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Hao Kong has authored 41 papers receiving a total of 289 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Vision and Pattern Recognition, 18 papers in Artificial Intelligence and 14 papers in Electrical and Electronic Engineering. Recurrent topics in Hao Kong's work include Advanced Neural Network Applications (16 papers), Adversarial Robustness in Machine Learning (7 papers) and Domain Adaptation and Few-Shot Learning (6 papers). Hao Kong is often cited by papers focused on Advanced Neural Network Applications (16 papers), Adversarial Robustness in Machine Learning (7 papers) and Domain Adaptation and Few-Shot Learning (6 papers). Hao Kong collaborates with scholars based in China, Singapore and Norway. Hao Kong's co-authors include Suhui Wu, Xianpei Han, Di Liu, Le Sun, Weichen Liu, Hui Chen, Wei Zhang, Ben He, Bo Chen and Qinfen Lu and has published in prestigious journals such as IEEE Transactions on Industry Applications, Neurocomputing and IEEE Transactions on Computers.

In The Last Decade

Hao Kong

37 papers receiving 274 citations

Peers

Hao Kong
Comparison fields: 5 of 59
  • Artificial Intelligence 168
  • Management Science and Operations Research 88
  • Computer Vision and Pattern Recognition 68
  • Electrical and Electronic Engineering 60
  • Control and Systems Engineering 37
Replace Xiaofeng Yu with:
Xiaofeng Yu Hong Kong
Akshay Agrawal United States
Sanmit Narvekar United States
Yanxiang Huang Belgium
István Hegedűs Hungary
Matthew Riemer United States
N. Kumar India
Yuchen Xie China
Hai Wan China
Atul Rawal United States
Xiaofeng Yu Hong Kong View profile →
Citations per field, relative to Hao Kong
Hao Kong · 1×
Citations per year, relative to Hao Kong
Hao Kong · 1×

Countries citing papers authored by Hao Kong

Since Specialization
Citations

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

Fields of papers citing papers by Hao Kong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hao Kong

This figure shows the co-authorship network connecting the top 25 collaborators of Hao Kong. A scholar is included among the top collaborators of Hao Kong based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hao Kong. Hao Kong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 4
2 1
3 0
4 0
5 0
6 4
7 3
8 7
9 1
10 3
11 2
12 1
13 8
14 4
15 4
16 19
17 3
18 8
19 35
20 14

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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