Zebang Shen
- Electrical and Electronic Engineering
- Artificial Intelligence top 10%
- Signal Processing top 10%
- Information Systems top 10%
- Computer Networks and Communications top 10%
- Topics
- Stochastic Gradient Optimization Techniques (14 papers)Sparse and Compressive Sensing Techniques (12 papers)Ferroelectric and Negative Capacitance Devices (4 papers)
- Partner nations
- ChinaUnited StatesSpain
In The Last Decade
Zebang Shen
30 papers receiving 439 citations
Peers
Comparison fields: 5 of 61
- Electrical and Electronic Engineering 228
- Artificial Intelligence 112
- Signal Processing 85
- Information Systems 81
- Computer Networks and Communications 65
Countries citing papers authored by Zebang Shen
This map shows the geographic impact of Zebang Shen'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 Zebang Shen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zebang Shen more than expected).
Fields of papers citing papers by Zebang Shen
This network shows the impact of papers produced by Zebang Shen. 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 Zebang Shen. The network helps show where Zebang Shen may publish in the future.
Co-authorship network of co-authors of Zebang Shen
This figure shows the co-authorship network connecting the top 25 collaborators of Zebang Shen. A scholar is included among the top collaborators of Zebang Shen 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 Zebang Shen. Zebang Shen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | One Sample Stochastic Frank-Wolfe. | 2 |
| 4 | Sinkhorn Natural Gradient for Generative Models | 2 |
| 5 | 1 | |
| 6 | 0 | |
| 7 | Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match | 1 |
| 8 | Decentralized Gradient Tracking for Continuous DR-Submodular Maximization | 3 |
| 9 | Hessian Aided Policy Gradient | 11 |
| 10 | Multitask Metric Learning: Theory and Algorithm | 7 |
| 11 | 14 | |
| 12 | 18 | |
| 13 | 1 | |
| 14 | Towards Memory-Friendly Deterministic Incremental Gradient Method. | 2 |
| 15 | 9 | |
| 16 | 2 | |
| 17 | Adaptive variance reducing for stochastic gradient descent | 9 |
| 18 | Simple atom selection strategy for greedy matrix completion | 2 |
| 19 | 12 | |
| 20 | 10 |
About Zebang Shen
Zebang Shen is a scholar working on Computational Mathematics, Artificial Intelligence and Computational Mechanics, having authored 34 papers that have together received 454 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (14 papers), Sparse and Compressive Sensing Techniques (12 papers) and Ferroelectric and Negative Capacitance Devices (4 papers). The work is most often cited by research in Computational Mathematics (9 citations), Signal Processing (85 citations) and Electrical and Electronic Engineering (228 citations). Zebang Shen has collaborated with scholars based in China, United States and Spain. Frequent co-authors include M. Nafrı́a, M. Porti, Mario Lanza, G. Bersuker, Qingguo Zhou, Jinfeng Kang, D. C. Gilmer, Fang Feng, Rui Zhou and Kuan‐Ching Li. Their work appears in journals such as Applied Physics Letters, Neurocomputing and Solid State Communications.
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.