Kaixuan Ji

1.1k citations
15 papers · 461 indexed · 1 hit paper · h-index 7
Topics
Cloud Computing and Resource Management (10 papers)IoT and Edge/Fog Computing (6 papers)Caching and Content Delivery (3 papers)
Partner nations
ChinaSpainUnited States

In The Last Decade

Kaixuan Ji

15 papers receiving 445 citations

Hit Papers

P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning ...20222026202320242022100200300

Peers

Kaixuan Ji
Comparison fields: 5 of 79
  • Artificial Intelligence 294
  • Information Systems 112
  • Computer Vision and Pattern Recognition 97
  • Computer Networks and Communications 61
  • Electrical and Electronic Engineering 43
Replace Damai Dai with:
Damai Dai China
Cheng Ji China
Qiben Yan United States
Dongfang Li China
Matthew Jagielski United States
Xueming Si China
Li Hu China
Mengkai Song China
Woosuk Kwon South Korea
Kaixuan Ji relative to Damai Dai China Damai Dai's profile →
Citations per field
00.5×3.1×
Damai Dai · 1×
Citations per year

Countries citing papers authored by Kaixuan Ji

Since Specialization
Citations

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

Fields of papers citing papers by Kaixuan Ji

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kaixuan Ji

This figure shows the co-authorship network connecting the top 25 collaborators of Kaixuan Ji. A scholar is included among the top collaborators of Kaixuan Ji 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 Kaixuan Ji. Kaixuan Ji is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

15 of 15 papers shown
#WorkIndexed citations
1 6
2
P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasksbreakdown →
369
3 6
4 5
5 5
6 24
7 3
8 3
9 8
10 2
11 2
12 18
13 1
14 3
15 6

About Kaixuan Ji

Kaixuan Ji is a scholar working on Information Systems, Computer Networks and Communications and Hardware and Architecture, having authored 15 papers that have together received 461 indexed citations. Recurring topics across this work include Cloud Computing and Resource Management (10 papers), IoT and Edge/Fog Computing (6 papers) and Caching and Content Delivery (3 papers). The work is most often cited by research in Artificial Intelligence (294 citations), Health Informatics (12 citations) and Information Systems (112 citations). Kaixuan Ji has collaborated with scholars based in China, Spain and United States. Frequent co-authors include Zhengxiao Du, Zhilin Yang, Jie Tang, Yicheng Fu, Xiao Liu, Fa Zhang, Xiaogang Li, Dehui Qiu, Shikui Zhang and Zhaocheng Wang. Their work appears in journals such as Energies, Nanoscale Advances and Sustainable Computing Informatics and Systems.

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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