Yuxiong He
- Hardware and Architecture top 1%
- Parallel Computing and Optimization Techniques 23
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- Distributed and Parallel Computing Systems 15
- Caching and Content Delivery 13
- Optimization and Search Problems 8
- Computational Mathematics top 5%
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- Advanced Neural Network Applications 17
- Information Systems top 1%
- Cloud Computing and Resource Management 24
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- Topic Modeling 10
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- Data Management and Algorithms 8
Yuxiong He
81 papers receiving 2.3k citations
Hit Papers
Peers
Comparison fields: 5 of 111
- Hardware and Architecture 545
- Computer Networks and Communications 1.1k
- Computational Mathematics 23
- Computer Vision and Pattern Recognition 723
- Information Systems 745
Countries citing papers authored by Yuxiong He
This map shows the geographic impact of Yuxiong He'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 Yuxiong He with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuxiong He more than expected).
Fields of papers citing papers by Yuxiong He
This network shows the impact of papers produced by Yuxiong He. 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 Yuxiong He. The network helps show where Yuxiong He may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yuxiong He, 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 | 2024 | 5 | |
| 2 | 2024 | 7 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 6 | |
| 5 | 2023 | 23 | |
| 6 | 2022 | 9 | |
| 7 | 2022 | 3 | |
| 8 | Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping | 2020 | 1 |
| 9 | 2019 | 3 | |
| 10 | 2018 | 9 | |
| 11 | DeepCPU: serving RNN-based deep learning models 10x faster | 2018 | 30 |
| 12 | Learning Intrinsic Sparse Structures within Long Short-Term Memory | 2017 | 14 |
| 13 | 2017 | 2 | |
| 14 | 2017 | 11 | |
| 15 | 2014 | 4 | |
| 16 | Power-effiicent resource allocation in MapReduce clusters | 2013 | 1 |
| 17 | Exploiting Processor Heterogeneity in Interactive Services | 2013 | 33 |
| 18 | Horton: Online Query Execution Engine for Large Distributed Graphs (Demo Track) | 2012 | 1 |
| 19 | 2012 | 79 | |
| 20 | 2008 | 1 |
About Yuxiong He
Yuxiong He is a scholar working on Hardware and Architecture, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 86 papers that have together received 2.4k indexed citations. Recurring topics across this work include Cloud Computing and Resource Management (24 papers), Parallel Computing and Optimization Techniques (23 papers), Advanced Neural Network Applications (17 papers), Distributed and Parallel Computing Systems (15 papers), Caching and Content Delivery (13 papers), Topic Modeling (10 papers), Data Management and Algorithms (8 papers) and Optimization and Search Problems (8 papers). The work is most often cited by research in Hardware and Architecture (545 citations), Computer Networks and Communications (1.1k citations) and Computational Mathematics (23 citations). Yuxiong He has collaborated with scholars based in United States, United Kingdom and Singapore. Frequent co-authors include Olatunji Ruwase, Samyam Rajbhandari, Jeff Rasley, Sameh Elnikety, Charles E. Leiserson, Shaolei Ren, Minjia Zhang, Kathryn S. McKinley, Kunal Agrawal and Feng Yan.
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.