Lantao Yu
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 2%
- Information Systems top 5%
- Signal Processing top 5%
- Computer Networks and Communications top 10%
- Topics
- Image Enhancement Techniques (6 papers)Reinforcement Learning in Robotics (5 papers)Advanced Image Processing Techniques (5 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE Transactions on Instrumentation and MeasurementIEEE Robotics and Automation Letters
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Lantao Yu
22 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 113
- Artificial Intelligence 930
- Computer Vision and Pattern Recognition 664
- Information Systems 230
- Signal Processing 219
- Computer Networks and Communications 96
Countries citing papers authored by Lantao Yu
This map shows the geographic impact of Lantao Yu'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 Lantao Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lantao Yu more than expected).
Fields of papers citing papers by Lantao Yu
This network shows the impact of papers produced by Lantao Yu. 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 Lantao Yu. The network helps show where Lantao Yu may publish in the future.
Co-authorship network of co-authors of Lantao Yu
This figure shows the co-authorship network connecting the top 25 collaborators of Lantao Yu. A scholar is included among the top collaborators of Lantao Yu 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 Lantao Yu. Lantao Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 28 | |
| 4 | 3 | |
| 5 | 13 | |
| 6 | 12 | |
| 7 | 2 | |
| 8 | MOPO: Model-based Offline Policy Optimization | 7 |
| 9 | 2 | |
| 10 | 6 | |
| 11 | 13 | |
| 12 | Lipschitz Generative Adversarial Nets. | 5 |
| 13 | 11 | |
| 14 | 0 | |
| 15 | Understanding the Effectiveness of Lipschitz Constraint in Training of GANs via Gradient Analysis | 2 |
| 16 | CoT: Cooperative Training for Generative Modeling of Discrete Data | 6 |
| 17 | CoT: Cooperative Training for Generative Modeling. | 5 |
| 18 | 8 | |
| 19 | An Empirical Study of AI Population Dynamics with Million-agent Reinforcement Learning. | 2 |
| 20 | SeqGAN: Sequence Generative Adversarial Nets with Policy Gradientbreakdown → | 1277 |
About Lantao Yu
Lantao Yu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology, having authored 27 papers that have together received 1.6k indexed citations. Recurring topics across this work include Image Enhancement Techniques (6 papers), Reinforcement Learning in Robotics (5 papers) and Advanced Image Processing Techniques (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (664 citations), Artificial Intelligence (930 citations) and Signal Processing (219 citations). Lantao Yu has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Yong Yu, Weinan Zhang, Jun Wang, Kan Ren, Jun Wang, M.T. Orchard, Yuanhao Gong, Guoping Qiu, Wenming Tang and Yu-Fei Wang. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Instrumentation and Measurement and IEEE Robotics and Automation Letters.
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.