Junru Shao
Impact in
- Computational Mathematics top 10%
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
- Speech and dialogue systems
Papers in
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- Image Retrieval and Classification Techniques 2
- Advanced Image and Video Retrieval Techniques 2
- Advanced Neural Network Applications 2
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- Parallel Computing and Optimization Techniques 4
- Co-authors
- Claire Cardie (1 shared paper)Xinya Du (1 shared paper)Tianqi Chen (3 shared papers)Zihao Ye (3 shared papers)Luís Ceze (1 shared paper)Lianmin Zheng (2 shared papers)Yong Yu (1 shared paper)Hongtao Lu (2 shared papers)
- Journals
- IEEE Transactions on Multimedia (1 paper)International Conference on Artificial Intelligence and Statistics (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesChina
In The Last Decade
Junru Shao
8 papers receiving 412 citations
Junru Shao's Hit Papers
Peers
Comparison fields: 5 of 49
- Computational Mathematics 17
- Artificial Intelligence 367
- Computer Vision and Pattern Recognition 180
- Hardware and Architecture 54
- Information Systems 59
Countries citing papers authored by Junru Shao
This map shows the geographic impact of Junru Shao'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 Junru Shao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junru Shao more than expected).
Fields of papers citing papers by Junru Shao
This network shows the impact of papers produced by Junru Shao. 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 Junru Shao. The network helps show where Junru Shao may publish in the future.
Co-authors
The 20 scholars most cited alongside Junru Shao, 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 | Learning to Ask: Neural Question Generation for Reading Comprehension Hit paper breakdown → | 2017 | 336 |
| 2 | 2023 | 48 | |
| 3 | 2023 | 34 | |
| 4 | 2017 | 9 | |
| 5 | TenSet: A Large-scale Program Performance Dataset for Learned Tensor Compilers | 2021 | 8 |
| 6 | Deep Neural Networks with Multi-Branch Architectures Are Intrinsically Less Non-Convex | 2019 | 7 |
| 7 | 2025 | 1 | |
| 8 | 2016 | 1 |
About Junru Shao
Junru Shao is a scholar working on Computer Vision and Pattern Recognition, Hardware and Architecture, Artificial Intelligence, Computational Mathematics and Computational Mechanics, having authored 8 papers that have together received 444 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (4 papers), Tensor decomposition and applications (3 papers), Image Retrieval and Classification Techniques (2 papers), Advanced Image and Video Retrieval Techniques (2 papers), Advanced Neural Network Applications (2 papers), Natural Language Processing Techniques (1 paper), Remote-Sensing Image Classification (1 paper) and Computational Physics and Python Applications (1 paper). The work is most often cited by research in Computational Mathematics (17 citations), Artificial Intelligence (367 citations), Computer Vision and Pattern Recognition (180 citations), Hardware and Architecture (54 citations) and Information Systems (59 citations). Junru Shao has collaborated with scholars based in United States and China. Frequent co-authors include Claire Cardie, Xinya Du, Tianqi Chen, Zihao Ye, Luís Ceze, Lianmin Zheng, Yong Yu, Hongtao Lu, Shicong Liu and Siyuan Feng. Their work appears in journals such as IEEE Transactions on Multimedia, International Conference on Artificial Intelligence and Statistics 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.