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Zheng Wang

3 papers here

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

fields
  • cs.AI1
  • cs.CV1
  • cs.LG1
ORCID 0000-0002-4814-1115
same name
  • Zheng Wang — 32 papers, h 61
  • Zheng Wang — 14 papers
  • Zheng Wang — 12 papers
  • Zheng Wang — 9 papers, h 30
  • Zheng Wang — 8 papers, h 8
  • Zheng Wang — 8 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 citedContributing Dimension Structure of Deep Feature for Coreset Selection

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

collaborators

4 papers

cs.CV2025

Enhancing WSI-Based Survival Analysis with Report-Auxiliary Self-Distillation

Zheng Wang, Hong Liu, Danyi Li +4

Survival analysis based on Whole Slide Images (WSIs) is crucial for evaluating cancer prognosis, as they offer detailed microscopic information essential for predicting patient out…

cs.CV2025

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection

Zhijing Wan, Zhixiang Wang, Zheng Wang +2

One-shot subset selection serves as an effective tool to reduce deep learning training costs by identifying an informative data subset based on the information extracted by an info…

cs.AI2025

Single-Node Trigger Backdoor Attacks in Graph-Based Recommendation Systems

Runze Li, Di Jin, Xiaobao Wang +3

Graph recommendation systems have been widely studied due to their ability to effectively capture the complex interactions between users and items. However, these systems also exhi…

cs.LG2024★ 1 cited

Contributing Dimension Structure of Deep Feature for Coreset Selection

Zhijing Wan, Zhixiang Wang, Yuran Wang +3

Coreset selection seeks to choose a subset of crucial training samples for efficient learning. It has gained traction in deep learning, particularly with the surge in training data…

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