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Fei Wang

4 papers here

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

author position
  • middle author1
  • last author1

Across the 2 of 4 papers where every author was matched, so the position is known.

fields
  • cs.AI1
  • cs.CL1
  • cs.LG1
  • cs.SI1
ORCID 0000-0001-9459-9461
same name
  • Fei Wang — 157 papers, h 137
  • Fei Wang — 52 papers, h 19
  • Fei Wang — 42 papers, h 50
  • Fei Wang — 33 papers, h 20
  • Fei Wang — 19 papers, h 16
  • Fei Wang — 18 papers, h 7

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
20192023
most citedPatchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources

45 citations · 61 across the 4 of their papers we have counts for

collaborators

4 papers

cs.AI2023★ 4 cited

A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises

Hejie Cui, Jiaying Lu, Ran Xu +14

This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs), including their construction, utilization models, and applications…

cs.LG2023★ 45 cited

Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources

Suraj Rajendran, Weishen Pan, Mert R. Sabuncu +3

Machine learning (ML) in healthcare presents numerous opportunities for enhancing patient care, population health, and healthcare providers' workflows. However, the real-world clin…

cs.CL2020★ 5 cited

CODER: Knowledge infused cross-lingual medical term embedding for term normalization

Zheng Yuan, Zhengyun Zhao, Haixia Sun +3

This paper proposes CODER: contrastive learning on knowledge graphs for cross-lingual medical term representation. CODER is designed for medical term normalization by providing clo…

cs.SI2019★ 7 cited

Neural Dynamics on Complex Networks

Chengxi Zang, Fei Wang

Learning continuous-time dynamics on complex networks is crucial for understanding, predicting and controlling complex systems in science and engineering. However, this task is ver…

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