Hangfeng He

594 citations
16 papers · 247 · h-index 6

Impact in

Papers in

    • Topic Modeling 10
    • Natural Language Processing Techniques 9
    • Text and Document Classification Technologies 3
    • Text Readability and Simplification 3
    • Neural Networks and Applications 2
    • Adversarial Robustness in Machine Learning 2
    • Multimodal Machine Learning Applications 2
    • Advanced Neural Network Applications 2

Hangfeng He

11 papers receiving 234 citations

Peers

Hangfeng He
Comparison fields: 5 of 31
  • Artificial Intelligence 234
  • Management Science and Operations Research 24
  • General Social Sciences 5
  • Computer Vision and Pattern Recognition 19
  • Information Systems 15
Replace Tim O’Gorman with:
Tim O’Gorman United States
Rishabh Joshi India
Laura Perez-Beltrachini France
Qianying Liu Japan
Rujun Han United States
Benjamin Heinzerling Japan
Mohammad Golam Sohrab Japan
Daniil Sorokin Germany
Elizabeth Boschee United States
Prasetya Ajie Utama Germany
Hangfeng He relative to Tim O’Gorman United States Tim O’Gorman's profile →
Citations per field
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Tim O’Gorman · 1×
Citations per year

Countries citing papers authored by Hangfeng He

Since Specialization
Citations

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

Fields of papers citing papers by Hangfeng He

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 17 scholars most cited alongside Hangfeng He, 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 Hangfeng He Line = papers co-authored together Hangfeng He links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 201785
2 201782
3 201823
4 201718
5 202014
6 201710
7 20235
8 20214
9
The Local Elasticity of Neural Networks
20203
10
Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity
20201
11 20171
12 20201
13 20240
14 20240
15 20250
16 20250

About Hangfeng He

Hangfeng He is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Management Science and Operations Research and General Social Sciences, having authored 16 papers that have together received 247 indexed citations. Recurring topics across this work include Topic Modeling (10 papers), Natural Language Processing Techniques (9 papers), Text and Document Classification Technologies (3 papers), Text Readability and Simplification (3 papers), Multimodal Machine Learning Applications (2 papers), Neural Networks and Applications (2 papers), Adversarial Robustness in Machine Learning (2 papers) and Advanced Neural Network Applications (2 papers). The work is most often cited by research in Artificial Intelligence (234 citations), Management Science and Operations Research (24 citations), General Social Sciences (5 citations), Computer Vision and Pattern Recognition (19 citations) and Information Systems (15 citations). Hangfeng He has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Xu Sun, Adam Lopez, Bonnie Webber, Jingjing Xu, Sujian Li, Xuancheng Ren, Weijie Su, Dan Roth, Jonathan Mamou and Ido Dagan. Their work appears in journals such as Language Resources and Evaluation, IEEE/ACM Transactions on Audio Speech and Language Processing, Physical review. E, Proceedings of the National Academy of Sciences and Edinburgh Research Explorer.

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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