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Chengqi Zhang

57 papers hereh-index 5327.7k citations172 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author8
  • last author47

Across the 55 of 57 papers where every author was matched, so the position is known.

fields
  • cs.LG28
  • cs.CL8
  • cs.CV5
  • cs.IR5
  • cs.SI5
  • cs.DS3
same name
  • Chengqi Zhang — 11 papers, h 3
  • Chengqi Zhang — 7 papers, h 8
  • Chengqi Zhang — 2 papers, h 2
  • Chengqi Zhang — 2 papers, h 0
  • Chengqi Zhang — 1 paper, h 1
  • Chengqi Zhang — 1 paper, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20142026
most citedA Comprehensive Survey on Graph Neural Networks

10.1k citations · 12.1k across the 55 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.LG2023★ 10 cited

Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey

Shengchao Chen, Guodong Long, Jing Jiang +2

As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric sciences is increasingly adopting data-driven models, powered by progressive develop…

cs.LG2023★ 1 cited

Does Continual Learning Equally Forget All Parameters?

Haiyan Zhao, Tianyi Zhou, Guodong Long +2

Distribution shift (e.g., task or domain shift) in continual learning (CL) usually results in catastrophic forgetting of neural networks. Although it can be alleviated by repeatedl…

cs.LG2023

Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks

Haiyan Zhao, Tianyi Zhou, Guodong Long +2

As a few large-scale pre-trained models become the major choices of various applications, new challenges arise for model pruning, e.g., can we avoid pruning the same model from scr…

cs.IR2023

Dual Personalization on Federated Recommendation

Chunxu Zhang, Guodong Long, Tianyi Zhou +4

Federated recommendation is a new Internet service architecture that aims to provide privacy-preserving recommendation services in federated settings. Existing solutions are used t…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.