Hongzhou Lin
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Computational Mechanics
- Statistical and Nonlinear Physics
- Numerical Analysis
- Co-authors
- Stefanie JegelkaQingyi WangJoan L. WalkerAli JadbabaieXiaohu ZhangShenhao WangJingzhao ZhangJulien Mairal
- Topics
- Stochastic Gradient Optimization Techniques (5 papers)Sparse and Compressive Sensing Techniques (3 papers)Neural Networks and Applications (3 papers)
- Journals
- Transportation Research Part B MethodologicalSIAM Journal on OptimizationarXiv (Cornell University)
- Partner nations
- United StatesNetherlandsIndia
In The Last Decade
Hongzhou Lin
8 papers receiving 41 citations
Peers
Comparison fields: 5 of 35
- Artificial Intelligence 17
- Computer Vision and Pattern Recognition 10
- Computational Mechanics 9
- Statistical and Nonlinear Physics 6
- Numerical Analysis 5
Countries citing papers authored by Hongzhou Lin
This map shows the geographic impact of Hongzhou Lin'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 Hongzhou Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hongzhou Lin more than expected).
Fields of papers citing papers by Hongzhou Lin
This network shows the impact of papers produced by Hongzhou Lin. 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 Hongzhou Lin. The network helps show where Hongzhou Lin may publish in the future.
Co-authorship network of co-authors of Hongzhou Lin
This figure shows the co-authorship network connecting the top 25 collaborators of Hongzhou Lin. A scholar is included among the top collaborators of Hongzhou Lin 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 Hongzhou Lin. Hongzhou Lin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | Stochastic Optimization with Non-stationary Noise: The Power of Moment Estimation | 1 |
| 3 | Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions | 4 |
| 4 | IDEAL: Inexact decentralized accelerated augmented Lagrangian method | 1 |
| 5 | 3 | |
| 6 | Perceptual Regularization: Visualizing and Learning Generalizable Representations | 1 |
| 7 | ResNet with one-neuron hidden layers is a Universal Approximator | 25 |
| 8 | QuickeNing: A Generic Quasi-Newton Algorithm for Faster Gradient-Based Optimization | 1 |
About Hongzhou Lin
Hongzhou Lin is a scholar working on Numerical Analysis, Artificial Intelligence and Transportation, having authored 8 papers that have together received 42 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (5 papers), Sparse and Compressive Sensing Techniques (3 papers) and Neural Networks and Applications (3 papers). The work is most often cited by research in Acoustics and Ultrasonics (1 citation), Numerical Analysis (5 citations) and Transportation (5 citations). Hongzhou Lin has collaborated with scholars based in United States, Netherlands and India. Frequent co-authors include Stefanie Jegelka, Qingyi Wang, Joan L. Walker, Ali Jadbabaie, Xiaohu Zhang, Shenhao Wang, Jingzhao Zhang, Julien Mairal, Zaïd Harchaoui and Jinhua Zhao. Their work appears in journals such as Transportation Research Part B Methodological, SIAM Journal on Optimization and arXiv (Cornell University).
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.