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Wei Cheng

36 papers hereh-index 13601 citations51 works total

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

author position
  • middle author32
  • last author4

Across the 36 of 36 papers where every author was matched, so the position is known.

fields
  • cs.CL15
  • cs.LG14
  • cs.AI5
  • cs.CV1
  • cs.DC1
same name
  • Wei Cheng — 51 papers, h 80
  • Wei Cheng — 26 papers, h 10
  • Wei Cheng — 12 papers, h 4
  • Wei Cheng — 11 papers, h 4
  • Wei Cheng — 9 papers, h 27
  • Wei Cheng — 9 papers, h 4

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
20232026
most citedA Survey on Detection of LLMs-Generated Content

7 citations · 39 across the 33 of their papers we have counts for

collaborators
Showing 2023 · cs.LGShow all

4 papers · 2 filters

cs.LG2023

POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning

Junxiang Wang, Guangji Bai, Wei Cheng +3

Time series domain adaptation stands as a pivotal and intricate challenge with diverse applications, including but not limited to human activity recognition, sleep stage classifica…

cs.LG2023★ 4 cited

DyExplainer: Explainable Dynamic Graph Neural Networks

Tianchun Wang, Dongsheng Luo, Wei Cheng +2

Graph Neural Networks (GNNs) resurge as a trending research subject owing to their impressive ability to capture representations from graph-structured data. However, the black-box…

cs.LG2023★ 4 cited

Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks

Xu Zheng, Farhad Shirani, Tianchun Wang +5

Graph Neural Networks (GNNs) are neural models that leverage the dependency structure in graphical data via message passing among the graph nodes. GNNs have emerged as pivotal arch…

cs.LG2023★ 3 cited

Interpretable Imitation Learning with Dynamic Causal Relations

Tianxiang Zhao, Wenchao Yu, Suhang Wang +6

Imitation learning, which learns agent policy by mimicking expert demonstration, has shown promising results in many applications such as medical treatment regimes and self-driving…

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