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Min Wu

4 papers hereh-index 282k citations69 works total

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

author position
  • middle author4

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

fields
  • cs.LG2
  • cs.CV1
  • cs.IR1
same name
  • Min Wu — 28 papers, h 39
  • Min Wu — 19 papers, h 16
  • Min Wu — 16 papers, h 9
  • Min Wu — 11 papers, h 7
  • Min Wu — 11 papers, h 7
  • Min Wu — 10 papers, h 10

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 citedSelf-supervised Autoregressive Domain Adaptation for Time Series Data

85 citations · 100 across the 4 of their papers we have counts for

collaborators

4 papers

cs.LG2021★ 85 cited

Self-supervised Autoregressive Domain Adaptation for Time Series Data

Mohamed Ragab, Emadeldeen Eldele, Zhenghua Chen +3

Unsupervised domain adaptation (UDA) has successfully addressed the domain shift problem for visual applications. Yet, these approaches may have limited performance for time series…

cs.CV2021★ 5 cited

Multi-Source Video Domain Adaptation with Temporal Attentive Moment Alignment

Yuecong Xu, Jianfei Yang, Haozhi Cao +4

Multi-Source Domain Adaptation (MSDA) is a more practical domain adaptation scenario in real-world scenarios. It relaxes the assumption in conventional Unsupervised Domain Adaptati…

cs.LG2021★ 1 cited

ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training

Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +4

Sleep staging is of great importance in the diagnosis and treatment of sleep disorders. Recently, numerous data-driven deep learning models have been proposed for automatic sleep s…

cs.IR2016★ 9 cited

Learning Optimal Social Dependency for Recommendation

Yong Liu, Peilin Zhao, Xin Liu +2

Social recommender systems exploit users' social relationships to improve the recommendation accuracy. Intuitively, a user tends to trust different subsets of her social friends, r…

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