Yihe Deng

768 citations
4 papers · 409 · 1 hit paper · h-index 2

Impact in

    • Adversarial Robustness in Machine Learning
    • Topic Modeling
    • Natural Language Processing Techniques
    • Hate Speech and Cyberbullying Detection
    • Anomaly Detection Techniques and Applications
    • Explainable Artificial Intelligence (XAI)
    • Advanced Malware Detection Techniques

Papers in

Journals
Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)arXiv (Cornell University) (1 paper)
Partner nations
ChinaUnited States

In The Last Decade

Yihe Deng

3 papers receiving 399 citations

Yihe Deng's Hit Papers

Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency 2019 · 363 citations
3630+2+4Years since publication100200300

Peers

Yihe Deng
Comparison fields: 5 of 33
  • Artificial Intelligence 390
  • Signal Processing 115
  • Software 9
  • Information Systems 47
  • Computer Vision and Pattern Recognition 42
Replace Shuhuai Ren with:
Shuhuai Ren China
Jin Yong Yoo United States
Jake Grigsby United States
Walter Rudametkin France
Jiazhu Dai China
Marc Rennhard Switzerland
Luke Valenta United States
Rajvardhan Oak United States
Christian Mainka Germany
Tom Van Goethem Belgium
Yihe Deng relative to Shuhuai Ren China Shuhuai Ren's profile →
Citations per field
00.5×1.5×
Shuhuai Ren · 1×
Citations per year

Countries citing papers authored by Yihe Deng

Since Specialization
Citations

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

Fields of papers citing papers by Yihe Deng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 5 scholars most cited alongside Yihe Deng, 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 Yihe Deng Line = papers co-authored together Yihe Deng links everyone, so they are left out of the graph.

All Works

4 of 4 papers shown
#Work
1
Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency
Hit paper breakdown →
2019363
2 202145
3
Fast Gradient Projection Method for Text Adversary Generation and Adversarial Training
20201
4 20220

About Yihe Deng

Yihe Deng is a scholar working on Artificial Intelligence, Signal Processing, Infectious Diseases, Organic Chemistry and Surgery, having authored 4 papers that have together received 409 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (3 papers), Anomaly Detection Techniques and Applications (2 papers), Topic Modeling (1 paper) and Advanced Malware Detection Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (390 citations), Signal Processing (115 citations), Software (9 citations), Information Systems (47 citations) and Computer Vision and Pattern Recognition (42 citations). Yihe Deng has collaborated with scholars based in China and United States. Frequent co-authors include Kun He, Shuhuai Ren, Wanxiang Che, Yichen Yang and Xiaosen Wang. Their work appears in journals such as Proceedings of the AAAI Conference on Artificial Intelligence and arXiv (Cornell University).

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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