Xuefei Ning

1.6k citations
44 papers · 737 indexed · h-index 13

Xuefei Ning

35 papers receiving 721 citations

Peers

Xuefei Ning
Comparison fields: 5 of 70
  • Hardware and Architecture 123
  • Computer Vision and Pattern Recognition 184
  • Artificial Intelligence 281
  • Electrical and Electronic Engineering 420
  • Industrial and Manufacturing Engineering 36
Replace Muhammad Abdullah Hanif with:
Muhammad Abdullah Hanif Austria
Azalia Mirhoseini United States
Alberto Marchisio United Arab Emirates
Faiq Khalid Austria
Anna Goldie United States
Divya Mahajan United States
Yingyan Lin United States
Bert Moons Belgium
Saketh Rama United States
Mingfu Xue China
Xuefei Ning relative to Muhammad Abdullah Hanif Austria Muhammad Abdullah Hanif's profile →
Citations per field
00.5×2.8×
Muhammad Abdullah Hanif · 1×
Citations per year

Countries citing papers authored by Xuefei Ning

Since Specialization
Citations

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

Fields of papers citing papers by Xuefei Ning

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Xuefei Ning, 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 Xuefei Ning Line = papers co-authored together Xuefei Ning links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3 20251
4 20250
5 20241
6 20240
7 20245
8 20240
9 20241
10 202449
11 20241
12 20231
13 20236
14 20239
15 20221
16 202274
17 20227
18 2021180
19 20185
20 201844

About Xuefei Ning

Xuefei Ning is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Hardware and Architecture, having authored 44 papers that have together received 737 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (15 papers), Advanced Memory and Neural Computing (9 papers), CCD and CMOS Imaging Sensors (7 papers), Ferroelectric and Negative Capacitance Devices (6 papers), Adversarial Robustness in Machine Learning (5 papers), Advanced Image and Video Retrieval Techniques (4 papers), Topic Modeling (3 papers) and Autonomous Vehicle Technology and Safety (3 papers). The work is most often cited by research in Hardware and Architecture (123 citations), Computer Vision and Pattern Recognition (184 citations) and Artificial Intelligence (281 citations). Xuefei Ning has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Yu Wang, Huazhong Yang, Mengyun Liu, Krishnendu Chakrabarty, Lixue Xia, Yu Wang, Jincheng Yu, Kai Zhong, Juejian Wu and Hai Li. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Neurocomputing and IEEE Transactions on Circuits and Systems for Video Technology.

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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