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Weiqiang Wang

4 papers hereh-index 213 citations9 works total

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

author position
  • middle author3
  • last author1

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

fields
  • cs.LG2
  • cs.AI1
  • cs.CL1
same name
  • Weiqiang Wang — 47 papers, h 35
  • Weiqiang Wang — 14 papers
  • Weiqiang Wang — 13 papers
  • Weiqiang Wang — 7 papers, h 28
  • Weiqiang Wang — 5 papers
  • Weiqiang Wang — 4 papers

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

most citedImproved Personalized Headline Generation via Denoising Fake Interests from Implicit Feedback

1 citations · 1 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CL2025★ 1 cited

Improved Personalized Headline Generation via Denoising Fake Interests from Implicit Feedback

Kejin Liu, Junhong Lian, Xiang Ao +5

Accurate personalized headline generation hinges on precisely capturing user interests from historical behaviors. However, existing methods neglect personalized-irrelevant click no…

cs.AI2024

AIGT: AI Generative Table Based on Prompt

Mingming Zhang, Zhiqing Xiao, Guoshan Lu +5

Tabular data, which accounts for over 80% of enterprise data assets, is vital in various fields. With growing concerns about privacy protection and data-sharing restrictions, gener…

cs.LG2024

Beyond Tree Models: A Hybrid Model of KAN and gMLP for Large-Scale Financial Tabular Data

Mingming Zhang, Jiahao Hu, Pengfei Shi +8

Tabular data plays a critical role in real-world financial scenarios. Traditionally, tree models have dominated in handling tabular data. However, financial datasets in the industr…

cs.LG2024

Ultra-imbalanced classification guided by statistical information

Yin Jin, Ningtao Wang, Ruofan Wu +3

Imbalanced data are frequently encountered in real-world classification tasks. Previous works on imbalanced learning mostly focused on learning with a minority class of few samples…

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