Geng Ji
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Computer Networks and Communications top 10%
- Information Systems top 10%
- Neurology
- Co-authors
- Ting ZhongChengtai CaoFan ZhouKunpeng ZhangGoce TrajcevskiZhiguang QinJiangping HuHong Zhu
- Topics
- Domain Adaptation and Few-Shot Learning (5 papers)Advanced Neural Network Applications (5 papers)Brain Tumor Detection and Classification (4 papers)
- Partner nations
- ChinaUnited StatesIsrael
In The Last Decade
Geng Ji
35 papers receiving 437 citations
Peers
Comparison fields: 5 of 72
- Artificial Intelligence 275
- Computer Vision and Pattern Recognition 109
- Computer Networks and Communications 100
- Information Systems 88
- Neurology 42
Countries citing papers authored by Geng Ji
This map shows the geographic impact of Geng Ji'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 Geng Ji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Geng Ji more than expected).
Fields of papers citing papers by Geng Ji
This network shows the impact of papers produced by Geng Ji. 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 Geng Ji. The network helps show where Geng Ji may publish in the future.
Co-authorship network of co-authors of Geng Ji
This figure shows the co-authorship network connecting the top 25 collaborators of Geng Ji. A scholar is included among the top collaborators of Geng Ji 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 Geng Ji. Geng Ji is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 10 | |
| 6 | 14 | |
| 7 | Marginalized Stochastic Natural Gradients for Black-Box Variational Inference | 1 |
| 8 | 57 | |
| 9 | 1 | |
| 10 | 14 | |
| 11 | Variational Training for Large-Scale Noisy-OR Bayesian Networks. | 1 |
| 12 | 11 | |
| 13 | From Patches to Images: A Nonparametric Generative Model. | 2 |
| 14 | Cryptanalysis of attribute-based ring signcryption scheme | 5 |
| 15 | 6 | |
| 16 | 5 | |
| 17 | Information Structure and Organization Structure of Supply Chain Based on Electronic Procurement System | 1 |
| 18 | Detecting corners of text in spam images | 5 |
| 19 | 3 | |
| 20 | Study on fast packet filter under network processor IXP2400 | 1 |
About Geng Ji
Geng Ji is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Neurology, having authored 39 papers that have together received 448 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (5 papers), Advanced Neural Network Applications (5 papers) and Brain Tumor Detection and Classification (4 papers). The work is most often cited by research in Artificial Intelligence (275 citations), Computer Vision and Pattern Recognition (109 citations) and Neurology (42 citations). Geng Ji has collaborated with scholars based in China, United States and Israel. Frequent co-authors include Ting Zhong, Chengtai Cao, Fan Zhou, Kunpeng Zhang, Goce Trajcevski, Zhiguang Qin, Jiangping Hu, Hong Zhu, Yi Ding and Zhen Qin. Their work appears in journals such as Expert Systems with Applications, IEEE Access and Neurocomputing.
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.