Xinjiang Wang
Impact in
-
- Advanced Neural Network Applications
- Oncology top 2%
- Cancer-related Molecular Pathways
Papers in
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- Cancer Research and Treatments 10
- Oncology 28
- Cancer-related Molecular Pathways 26
- Co-authors
- Baoling HuangRuiqiang GuoWei ZhangYoudi KuangZhanghan KeRynson W. H. LauLei ZhuAkihisa Takahashi
- Journals
- International Journal of Radiation Oncology*Biology*Physics (4 papers)The Journal of Physical Chemistry C (3 papers)Cancer Letters (3 papers)Journal of Biological Chemistry (3 papers)Scientific Reports (2 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Xinjiang Wang
73 papers receiving 3.9k citations
Hit Papers
Peers
Comparison fields: 5 of 165
- Computer Vision and Pattern Recognition 776
- Oncology 1.0k
- Biotechnology 251
- Cancer Research 356
- Media Technology 181
Countries citing papers authored by Xinjiang Wang
This map shows the geographic impact of Xinjiang Wang'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 Xinjiang Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xinjiang Wang more than expected).
Fields of papers citing papers by Xinjiang Wang
This network shows the impact of papers produced by Xinjiang Wang. 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 Xinjiang Wang. The network helps show where Xinjiang Wang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Xinjiang Wang, 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 | 2025 | 0 | |
| 2 | 2023 | 3 | |
| 3 | 2022 | 53 | |
| 4 | Rethinking the Pruning Criteria for Convolutional Neural Network | 2021 | 17 |
| 5 | 2020 | 22 | |
| 6 | Understanding Regularization in Batch Normalization | 2018 | 1 |
| 7 | Kalman Normalization: Normalizing Internal Representations Across Network Layers | 2018 | 17 |
| 8 | 2017 | 24 | |
| 9 | 2011 | 98 | |
| 10 | 2010 | 25 | |
| 11 | 2008 | 7 | |
| 12 | 2004 | 86 | |
| 13 | 2000 | 23 | |
| 14 | 1999 | 289 | |
| 15 | 1999 | 15 | |
| 16 | 1999 | 28 | |
| 17 | 1999 | 27 | |
| 18 | 1997 | 9 | |
| 19 | 1997 | 24 | |
| 20 | 1996 | 86 |
About Xinjiang Wang
Xinjiang Wang is a scholar working on Biotechnology, Oncology, Cancer Research, Computer Vision and Pattern Recognition and Aging, having authored 74 papers that have together received 4.0k indexed citations. Recurring topics across this work include Cancer-related Molecular Pathways (26 papers), Cancer Research and Treatments (10 papers), Cancer, Hypoxia, and Metabolism (10 papers), Advanced Neural Network Applications (10 papers), Thermal properties of materials (9 papers), Ubiquitin and proteasome pathways (9 papers), Domain Adaptation and Few-Shot Learning (8 papers) and Advanced Image and Video Retrieval Techniques (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (776 citations), Oncology (1.0k citations), Biotechnology (251 citations), Cancer Research (356 citations) and Media Technology (181 citations). Xinjiang Wang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Baoling Huang, Ruiqiang Guo, Wei Zhang, Youdi Kuang, Zhanghan Ke, Rynson W. H. Lau, Lei Zhu, Akihisa Takahashi, Ken Ohnishi and Takeo Ohnishi. Their work appears in journals such as International Journal of Radiation Oncology*Biology*Physics, The Journal of Physical Chemistry C, Cancer Letters, Journal of Biological Chemistry and Scientific Reports.
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.