Can Ma

421 citations
11 papers · 288 · 1 hit paper · h-index 6

Impact in

Papers in

Can Ma

8 papers receiving 288 citations

Can Ma's Hit Papers

A Survey on Model Compression for Large Language Models 2024 · 74 citations
740+1Years since publication204060

Peers

Can Ma
Comparison fields: 5 of 62
  • Water Science and Technology 78
  • Renewable Energy, Sustainability and the Environment 89
  • Electronic, Optical and Magnetic Materials 32
  • Computer Vision and Pattern Recognition 32
  • Electrical and Electronic Engineering 85
Replace Yixuan Qiao with:
Yixuan Qiao China
Haiyue Wang China
Mingkun Jiang China
Dezhi Fang China
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Citations per field
00.5×10×16×
Yixuan Qiao · 1×
Citations per year

Countries citing papers authored by Can Ma

Since Specialization
Citations

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

Fields of papers citing papers by Can Ma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

11 of 11 papers shown
#Work
1 2019132
2
A Survey on Model Compression for Large Language Models
Hit paper breakdown →
202474
3 202141
4 201813
5 201810
6 202310
7 20225
8 20123
9 20240
10 20250
11 20250

About Can Ma

Can Ma is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Renewable Energy, Sustainability and the Environment, Artificial Intelligence and Mechanical Engineering, having authored 11 papers that have together received 288 indexed citations. Recurring topics across this work include Advancements in Battery Materials (4 papers), Advanced Battery Materials and Technologies (3 papers), Iron oxide chemistry and applications (3 papers), Multimodal Machine Learning Applications (2 papers), Extraction and Separation Processes (2 papers), Advanced Image and Video Retrieval Techniques (2 papers), Natural Language Processing Techniques (2 papers) and Human Pose and Action Recognition (2 papers). The work is most often cited by research in Water Science and Technology (78 citations), Renewable Energy, Sustainability and the Environment (89 citations), Electronic, Optical and Magnetic Materials (32 citations), Computer Vision and Pattern Recognition (32 citations) and Electrical and Electronic Engineering (85 citations). Can Ma has collaborated with scholars based in China, Portugal and Saudi Arabia. Frequent co-authors include Jinming Zhou, Sen Wang, Shuo Feng, Hui Liu, Yu Wei, Rufen Chen, Yong Liu, Weiping Wang, Jian Li and Meng Zhang. Their work appears in journals such as Rare Metals, Chemical Communications, Materials Letters, Electrochimica Acta and Transactions of the Association for Computational Linguistics.

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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