Cong Fang
- Electrical and Electronic Engineering top 5%
- Automotive Engineering top 2%
- Materials Chemistry
- Electronic, Optical and Magnetic Materials top 10%
- Artificial Intelligence top 10%
- Topics
- Stochastic Gradient Optimization Techniques (12 papers)Sparse and Compressive Sensing Techniques (10 papers)Machine Learning and ELM (6 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceProceedings of the IEEEScientific Reports
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Cong Fang
42 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 100
- Electrical and Electronic Engineering 942
- Automotive Engineering 377
- Materials Chemistry 266
- Electronic, Optical and Magnetic Materials 213
- Artificial Intelligence 170
Countries citing papers authored by Cong Fang
This map shows the geographic impact of Cong Fang'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 Cong Fang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cong Fang more than expected).
Fields of papers citing papers by Cong Fang
This network shows the impact of papers produced by Cong Fang. 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 Cong Fang. The network helps show where Cong Fang may publish in the future.
Co-authorship network of co-authors of Cong Fang
This figure shows the co-authorship network connecting the top 25 collaborators of Cong Fang. A scholar is included among the top collaborators of Cong Fang 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 Cong Fang. Cong Fang 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 | 0 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 3 | |
| 6 | 2 | |
| 7 | 1 | |
| 8 | How to Characterize The Landscape of Overparameterized Convolutional Neural Networks | 1 |
| 9 | 1 | |
| 10 | 9 | |
| 11 | Sharp Analysis for Nonconvex SGD Escaping from Saddle Points. | 7 |
| 12 | A Sharp Convergence Rate Analysis for Distributed Accelerated Gradient Methods | 6 |
| 13 | SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator | 47 |
| 14 | 51 | |
| 15 | 2 | |
| 16 | Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers | 4 |
| 17 | 41 | |
| 18 | 3D lithium metal embedded within lithiophilic porous matrix for stable lithium metal batteriesbreakdown → | 463 |
| 19 | 63 | |
| 20 | Synthesis and Characterization of Two Positional Isomeric Tetraamino-phthalocyanines Zinc(II) | 1 |
About Cong Fang
Cong Fang is a scholar working on Artificial Intelligence, Numerical Analysis and Computational Mechanics, having authored 47 papers that have together received 1.4k indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (12 papers), Sparse and Compressive Sensing Techniques (10 papers) and Machine Learning and ELM (6 papers). The work is most often cited by research in Automotive Engineering (377 citations), Computational Mathematics (11 citations) and Electrical and Electronic Engineering (942 citations). Cong Fang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Ouwei Sheng, Wenkui Zhang, Jianmin Luo, Yang Xia, Chengbin Jin, Chu Liang, Yongping Gan, Jun Zhang, Zhouchen Lin and Xinyong Tao. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Proceedings of the IEEE and Scientific Reports.
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.