Yaru Hao
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Domain Adaptation and Few-Shot Learning
- Explainable Artificial Intelligence (XAI)
- Speech Recognition and Synthesis
Papers in
-
- Topic Modeling 6
- Natural Language Processing Techniques 5
- Machine Learning and Data Classification 2
- Explainable Artificial Intelligence (XAI) 2
- Adversarial Robustness in Machine Learning 1
- Co-authors
- Furu Wei (6 shared papers)Li Dong (5 shared papers)Ke Xu (3 shared papers)Zhifang Sui (2 shared papers)Damai Dai (2 shared papers)Baobao Chang (1 shared paper)Dong Li (1 shared paper)Shuming Ma (1 shared paper)
- Journals
- Acta Tropica (1 paper)Bioresource Technology (1 paper)Neurocomputing (1 paper)Computational and Mathematical Methods in Medicine (1 paper)Chinese Journal of Liquid Crystals and Displays (1 paper)
- Partner nations
- ChinaIndiaUnited States
In The Last Decade
Yaru Hao
13 papers receiving 471 citations
Peers
Comparison fields: 5 of 97
- Artificial Intelligence 262
- Health Informatics 7
- Computer Vision and Pattern Recognition 88
- Pollution 35
- Biomaterials 37
Countries citing papers authored by Yaru Hao
This map shows the geographic impact of Yaru Hao'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 Yaru Hao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yaru Hao more than expected).
Fields of papers citing papers by Yaru Hao
This network shows the impact of papers produced by Yaru Hao. 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 Yaru Hao. The network helps show where Yaru Hao may publish in the future.
Co-authors
The 25 scholars most cited alongside Yaru Hao, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 102 | |
| 2 | 2019 | 92 | |
| 3 | 2023 | 75 | |
| 4 | 2022 | 73 | |
| 5 | 2018 | 45 | |
| 6 | 2017 | 35 | |
| 7 | 2023 | 18 | |
| 8 | 2016 | 13 | |
| 9 | 2018 | 12 | |
| 10 | 2020 | 9 | |
| 11 | 2021 | 4 | |
| 12 | 2023 | 2 | |
| 13 | 2017 | 2 | |
| 14 | Luminance Uniformity Evaluation for LED Display Panel Based on Gray Histogram | 2009 | 0 |
About Yaru Hao
Yaru Hao is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Infectious Diseases, Computer Networks and Communications and Pathology and Forensic Medicine, having authored 14 papers that have together received 482 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Natural Language Processing Techniques (5 papers), Machine Learning and Data Classification (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Color perception and design (1 paper), Color Science and Applications (1 paper), Adversarial Robustness in Machine Learning (1 paper) and Membrane Separation Technologies (1 paper). The work is most often cited by research in Artificial Intelligence (262 citations), Health Informatics (7 citations), Computer Vision and Pattern Recognition (88 citations), Pollution (35 citations) and Biomaterials (37 citations). Yaru Hao has collaborated with scholars based in China, India and United States. Frequent co-authors include Furu Wei, Li Dong, Ke Xu, Zhifang Sui, Damai Dai, Baobao Chang, Dong Li, Shuming Ma, Yutao Sun and Long Huang. Their work appears in journals such as Acta Tropica, Bioresource Technology, Neurocomputing, Computational and Mathematical Methods in Medicine and Chinese Journal of Liquid Crystals and Displays.
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.