Guoping Long

944 citations
30 papers · 467 · h-index 11

Impact in

Papers in

Guoping Long

29 papers receiving 451 citations

Peers

Guoping Long
Comparison fields: 5 of 49
  • Hardware and Architecture 196
  • Computational Mathematics 16
  • Computer Vision and Pattern Recognition 164
  • Computer Networks and Communications 155
  • Artificial Intelligence 209
Replace Jenq‐Kuen Lee with:
Jenq‐Kuen Lee Taiwan
Guohao Dai China
Ammar Ahmad Awan United States
Gabriel Weisz United States
Lingxiao Ma China
Neal Crago United States
Jiawen Liu China
Prasanth Chatarasi United States
Ravi Teja Mullapudi United States
Zongwei Zhou United States
Guoping Long relative to Jenq‐Kuen Lee Taiwan Jenq‐Kuen Lee's profile →
Citations per field
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Jenq‐Kuen Lee · 1×
Citations per year

Countries citing papers authored by Guoping Long

Since Specialization
Citations

This map shows the geographic impact of Guoping Long'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 Guoping Long with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guoping Long more than expected).

Fields of papers citing papers by Guoping Long

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Guoping Long. 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 Guoping Long. The network helps show where Guoping Long may publish in the future.

Co-authors

The 25 scholars most cited alongside Guoping Long, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Guoping Long Line = papers co-authored together Guoping Long links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2021107
2 201762
3 201360
4 202244
5 200926
6 201021
7 202020
8 201318
9 201316
10 202014
11 202212
12
Online Bayesian max-margin subspace multi-view learning
201610
13 20138
14 20116
15 20166
16 20166
17 20195
18 20174
19 20094
20 20093

About Guoping Long

Guoping Long is a scholar working on Computer Networks and Communications, Hardware and Architecture, Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 30 papers that have together received 467 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (12 papers), Advanced Data Storage Technologies (6 papers), Distributed and Parallel Computing Systems (5 papers), Advanced Neural Network Applications (5 papers), Advanced Image and Video Retrieval Techniques (4 papers), Stochastic Gradient Optimization Techniques (3 papers), Domain Adaptation and Few-Shot Learning (3 papers) and Distributed systems and fault tolerance (3 papers). The work is most often cited by research in Hardware and Architecture (196 citations), Computational Mathematics (16 citations), Computer Vision and Pattern Recognition (164 citations), Computer Networks and Communications (155 citations) and Artificial Intelligence (209 citations). Guoping Long has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Yunquan Zhang, Shengen Yan, Wei Lin, Chuan Wu, Zhen Zheng, Shiqing Fan, Shuzi Niu, Xiaoyu Shen, Yi Rong and Wenjie Li. Their work appears in journals such as ACM SIGPLAN Notices, Machine Learning, Journal of Computer Science and Technology, Chinese Journal of Computers and Wuhan University Journal of Natural Sciences.

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.

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