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researcher

Wei Wang

16 papers hereh-index 161.5k citations30 works total

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

author position
  • middle author5
  • last author11

Across the 16 of 16 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • cs.AI4
  • cs.CL2
  • cs.CE1
  • cs.SE1
  • cs.SI1
same name
  • Wei Wang — 41 papers, h 34
  • Wei Wang — 40 papers
  • Wei Wang — 40 papers, h 8
  • Wei Wang — 23 papers, h 53
  • Wei Wang — 23 papers, h 4
  • Wei Wang — 22 papers, h 55

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
20182024
most citedUnsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity

19 citations · 49 across the 9 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.LG2022

A Mobility-Aware Deep Learning Model for Long-Term COVID-19 Pandemic Prediction and Policy Impact Analysis

Danfeng Guo, Zijie Huang, Junheng Hao +3

Pandemic(epidemic) modeling, aiming at disease spreading analysis, has always been a popular research topic especially following the outbreak of COVID-19 in 2019. Some representati…

cs.CL2022

Introducing Semantics into Speech Encoders

Derek Xu, Shuyan Dong, Changhan Wang +10

Recent studies find existing self-supervised speech encoders contain primarily acoustic rather than semantic information. As a result, pipelined supervised automatic speech recogni…

cs.AI2022★ 17 cited

Dual-Geometric Space Embedding Model for Two-View Knowledge Graphs

Roshni G. Iyer, Yunsheng Bai, Wei Wang +1

Two-view knowledge graphs (KGs) jointly represent two components: an ontology view for abstract and commonsense concepts, and an instance view for specific entities that are instan…

cs.AI2022

Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment

Zijie Huang, Zheng Li, Haoming Jiang +6

Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from complete. Due to labor-intensive human labeling, this phenomenon deteriorates when handling…

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