Meng Fang
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 2%
- Information Systems top 5%
- Computer Networks and Communications top 10%
- Computer Science Applications top 5%
- Topics
- Topic Modeling (27 papers)Natural Language Processing Techniques (20 papers)Machine Learning and Algorithms (13 papers)
- Journals
- IEEE Transactions on CyberneticsIEEE Transactions on Neural Networks and Learning SystemsNeurocomputing
- Partner nations
- ChinaAustraliaUnited Kingdom
In The Last Decade
Meng Fang
93 papers receiving 1.6k citations
Peers
Comparison fields: 5 of 135
- Artificial Intelligence 916
- Computer Vision and Pattern Recognition 578
- Information Systems 126
- Computer Networks and Communications 77
- Computer Science Applications 77
Countries citing papers authored by Meng Fang
This map shows the geographic impact of Meng Fang'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 Meng Fang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Meng Fang more than expected).
Fields of papers citing papers by Meng Fang
This network shows the impact of papers produced by Meng Fang. 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 Meng Fang. The network helps show where Meng Fang may publish in the future.
Co-authorship network of co-authors of Meng Fang
This figure shows the co-authorship network connecting the top 25 collaborators of Meng Fang. A scholar is included among the top collaborators of Meng Fang 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 Meng Fang. Meng Fang 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 | 0 | |
| 3 | 3 | |
| 4 | 0 | |
| 5 | 6 | |
| 6 | 3 | |
| 7 | 1 | |
| 8 | 3 | |
| 9 | 14 | |
| 10 | 26 | |
| 11 | 81 | |
| 12 | 14 | |
| 13 | 6 | |
| 14 | Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games | 5 |
| 15 | 68 | |
| 16 | LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning | 45 |
| 17 | Curriculum-guided Hindsight Experience Replay | 50 |
| 18 | DHER: Hindsight Experience Replay for Dynamic Goals | 33 |
| 19 | Algorithm for Image Quality Assessment Based on Color Features | 1 |
| 20 | The Problems Appeared in Inspection,Repair andOperation of 104 Valves and Method for Solution | 0 |
About Meng Fang
Meng Fang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mathematics, having authored 104 papers that have together received 1.6k indexed citations. Recurring topics across this work include Topic Modeling (27 papers), Natural Language Processing Techniques (20 papers) and Machine Learning and Algorithms (13 papers). The work is most often cited by research in Artificial Intelligence (916 citations), Computer Vision and Pattern Recognition (578 citations) and Computational Mathematics (12 citations). Meng Fang has collaborated with scholars based in China, Australia and United Kingdom. Frequent co-authors include Trevor Cohn, Dacheng Tao, Yuan Li, Yali Du, Joey Tianyi Zhou, Xingquan Zhu, Dacheng Tao, Jie Yin, Chengqi Zhang and Rick Siow Mong Goh. Their work appears in journals such as IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks and Learning Systems 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.