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researcher

Dawei Cheng

10 papers hereh-index 8527 citations12 works total

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

author position
  • middle author6
  • last author2

Across the 8 of 10 papers where every author was matched, so the position is known.

fields
  • cs.LG6
  • cs.CL2
  • cs.MA2
same name
  • Dawei Cheng — 8 papers, h 7
  • Dawei Cheng — 6 papers, h 10
  • Dawei Cheng — 4 papers, h 4
  • Dawei Cheng — 4 papers, h 3
  • Dawei Cheng — 4 papers, h 3
  • Dawei Cheng — 1 paper, h 1

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
20242026
most citedMemory in the Age of AI Agents

1 citations · 1 across the 2 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.LG2024

Fast Track to Winning Tickets: Repowering One-Shot Pruning for Graph Neural Networks

Yanwei Yue, Guibin Zhang, Haoran Yang +1

Graph Neural Networks (GNNs) demonstrate superior performance in various graph learning tasks, yet their wider real-world application is hindered by the computational overhead when…

cs.LG2024

GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning

Guibin Zhang, Haonan Dong, Yuchen Zhang +7

Training high-quality deep models necessitates vast amounts of data, resulting in overwhelming computational and memory demands. Recently, data pruning, distillation, and coreset s…

cs.MA2024

Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems

Guibin Zhang, Yanwei Yue, Zhixun Li +6

Recent advancements in large language model (LLM)-powered agents have shown that collective intelligence can significantly outperform individual capabilities, largely attributed to…

cs.LG2024

Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness

Guibin Zhang, Yanwei Yue, Kun Wang +7

Graph Neural Networks (GNNs) excel in various graph learning tasks but face computational challenges when applied to large-scale graphs. A promising solution is to remove non-essen…

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