Yishu Miao
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Aerospace Engineering top 10%
- Information Systems top 10%
- Electrical and Electronic Engineering
- Co-authors
- Phil BlunsomAndrew MarkhamNiki TrigoniChris Xiaoxuan LuZhenghua XuThomas LukasiewiczXiangwu MengEdward Grefenstette
- Topics
- Topic Modeling (9 papers)Multimodal Machine Learning Applications (5 papers)Natural Language Processing Techniques (5 papers)
- Journals
- IEEE Transactions on Neural Networks and Learning SystemsApplied Ecology and Environmental ResearchNeural Information Processing Systems
- Partner nations
- United KingdomChinaUnited States
In The Last Decade
Yishu Miao
17 papers receiving 425 citations
Peers
Comparison fields: 5 of 53
- Artificial Intelligence 235
- Computer Vision and Pattern Recognition 203
- Aerospace Engineering 115
- Information Systems 102
- Electrical and Electronic Engineering 54
Countries citing papers authored by Yishu Miao
This map shows the geographic impact of Yishu Miao'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 Yishu Miao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yishu Miao more than expected).
Fields of papers citing papers by Yishu Miao
This network shows the impact of papers produced by Yishu Miao. 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 Yishu Miao. The network helps show where Yishu Miao may publish in the future.
Co-authorship network of co-authors of Yishu Miao
This figure shows the co-authorship network connecting the top 25 collaborators of Yishu Miao. A scholar is included among the top collaborators of Yishu Miao 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 Yishu Miao. Yishu Miao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 9 | |
| 3 | 8 | |
| 4 | 7 | |
| 5 | 36 | |
| 6 | 33 | |
| 7 | 5 | |
| 8 | 3 | |
| 9 | 38 | |
| 10 | 104 | |
| 11 | 31 | |
| 12 | Memory Architectures in Recurrent Neural Network Language Models | 21 |
| 13 | Discovering Discrete Latent Topics with Neural Variational Inference | 51 |
| 14 | 34 | |
| 15 | 57 | |
| 16 | Bayesian Optimisation for Machine Translation | 1 |
| 17 | 1 |
About Yishu Miao
Yishu Miao is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems, having authored 17 papers that have together received 442 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Multimodal Machine Learning Applications (5 papers) and Natural Language Processing Techniques (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (203 citations), General Social Sciences (26 citations) and Artificial Intelligence (235 citations). Yishu Miao has collaborated with scholars based in United Kingdom, China and United States. Frequent co-authors include Phil Blunsom, Andrew Markham, Niki Trigoni, Chris Xiaoxuan Lu, Zhenghua Xu, Thomas Lukasiewicz, Xiangwu Meng, Edward Grefenstette, Changhao Chen and Stefano Rosa. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Applied Ecology and Environmental Research and Neural Information Processing Systems.
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.