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Qiang Yang

KAUST

4 papers hereh-index 242.4k citations77 works total

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

author position
  • middle author2
  • last author1

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.AI1
  • cs.CL1
  • cs.CR1
  • cs.LG1
affiliations
  • KAUST
same name
  • Qiang Yang — 60 papers, h 58
  • Qiang Yang — 40 papers, h 16
  • Qiang Yang — 37 papers, h 110
  • Qiang Yang — 9 papers
  • Qiang Yang — 7 papers, h 6
  • Qiang Yang — 6 papers, h 3

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
20202023
most citedGraph Random Neural Network for Semi-Supervised Learning on Graphs

23 citations · 26 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CL2023

A Topic-aware Summarization Framework with Different Modal Side Information

Xiuying Chen, Mingzhe Li, Shen Gao +5

Automatic summarization plays an important role in the exponential document growth on the Web. On content websites such as CNN.com and WikiHow.com, there often exist various kinds…

cs.CR2022★ 3 cited

Cloud-Edge Collaborative Data Anomaly Detection in Industrial Sensor Networks

Tao Yang, Xuefeng Jiang, Wei Li +4

Existing research on sensor data anomaly detection for industrial sensor networks still has several inherent limitations. First, most detection models usually consider centralized…

cs.AI2020

PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation

Guangneng Hu, Qiang Yang

Transfer learning is an effective technique to improve a target recommender system with the knowledge from a source domain. Existing research focuses on the recommendation performa…

cs.LG2020★ 23 cited

Graph Random Neural Network for Semi-Supervised Learning on Graphs

Wenzheng Feng, Jie Zhang, Yuxiao Dong +6

We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored. However, most existing GNNs inherently suffer fro…

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