Ruoyu Sun
- Computational Mathematics top 5%
-
- Cooperative Communication and Network Coding 7
- Wireless Communication Networks Research 5
- Computational Mechanics top 5%
- Sparse and Compressive Sensing Techniques 5
- Signal Processing top 10%
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- Stochastic Gradient Optimization Techniques 11
- Neural Networks and Applications 7
- Machine Learning and ELM 5
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- Advanced MIMO Systems Optimization 10
- Advanced Wireless Network Optimization 7
- Co-authors
- Zhi‐Quan LuoMingyi HongHadi BalighMaziar SanjabiQingjiang ShiEnbin SongTian DingR. Srikant
- Partner nations
- United StatesChinaHong Kong
In The Last Decade
Ruoyu Sun
39 papers receiving 944 citations
Peers
Comparison fields: 5 of 101
- Computational Mathematics 22
- Computer Networks and Communications 346
- Computational Mechanics 236
- Signal Processing 97
- Acoustics and Ultrasonics 8
Countries citing papers authored by Ruoyu Sun
This map shows the geographic impact of Ruoyu Sun'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 Ruoyu Sun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ruoyu Sun more than expected).
Fields of papers citing papers by Ruoyu Sun
This network shows the impact of papers produced by Ruoyu Sun. 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 Ruoyu Sun. The network helps show where Ruoyu Sun may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ruoyu Sun, 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 | 8 | |
| 2 | 2024 | 3 | |
| 3 | 2024 | 3 | |
| 4 | 2024 | 2 | |
| 5 | 2024 | 9 | |
| 6 | 2022 | 3 | |
| 7 | 2022 | 4 | |
| 8 | 2021 | 13 | |
| 9 | RMSprop converges with proper hyper-parameter. | 2020 | 4 |
| 10 | Global Convergence and Induced Kernels of Gradient-Based Meta-Learning with Neural Nets. | 2020 | 1 |
| 11 | 2020 | 93 | |
| 12 | 2019 | 3 | |
| 13 | Sub-Optimal Local Minima Exist for Almost All Over-parameterized Neural Networks. | 2019 | 4 |
| 14 | 2019 | 1 | |
| 15 | Understanding the Loss Surface of Neural Networks for Binary Classification | 2018 | 1 |
| 16 | Over-Parameterized Deep Neural Networks Have No Strict Local Minima For Any Continuous Activations. | 2018 | 6 |
| 17 | 2018 | 15 | |
| 18 | 2016 | 181 | |
| 19 | 2013 | 14 | |
| 20 | 2012 | 3 |
About Ruoyu Sun
Ruoyu Sun is a scholar working on Health Informatics, Artificial Intelligence and Computer Networks and Communications, having authored 42 papers that have together received 976 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (11 papers), Advanced MIMO Systems Optimization (10 papers), Advanced Wireless Network Optimization (7 papers), Cooperative Communication and Network Coding (7 papers), Neural Networks and Applications (7 papers), Sparse and Compressive Sensing Techniques (5 papers), Machine Learning and ELM (5 papers) and Wireless Communication Networks Research (5 papers). The work is most often cited by research in Computational Mathematics (22 citations), Computer Networks and Communications (346 citations) and Computational Mechanics (236 citations). Ruoyu Sun has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Zhi‐Quan Luo, Mingyi Hong, Hadi Baligh, Maziar Sanjabi, Qingjiang Shi, Enbin Song, Tian Ding, R. Srikant, Shiyu Liang and Zhi-Quan Luo.
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.