World's Best Scientists 2026 revealed!
Jianmin Wang

Jianmin Wang

D-Index & Metrics

Computer Science

D-Index
65
Citations
24445
World Ranking
2405
National Ranking
326

Overview

Jianmin Wang is affiliated with Tsinghua University in China. Their research primarily spans the field of Computer Science, with a focus on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Computer Networks and Communications, and Information Systems.

The scientist's main topics of work include:

  • Time Series Analysis and Forecasting
  • Domain Adaptation and Few-Shot Learning
  • Advanced Database Systems and Queries
  • Multimodal Machine Learning Applications
  • Topic Modeling
  • Data Management and Algorithms
  • Data Stream Mining Techniques

Their publication record includes a number of recent papers:

  • Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting (2021, arXiv (Cornell University))
  • PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning (2022, IEEE Transactions on Pattern Analysis and Machine Intelligence)
  • TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis (2022, arXiv (Cornell University))
  • Skilful nowcasting of extreme precipitation with NowcastNet (2023, Nature)

Jianmin Wang has collaborated frequently with a number of co-authors, including:

  • Mingsheng Long (65 coauthored works)
  • Shaoxu Song (27 coauthored works)
  • Haixu Wu (23 coauthored works)
  • Xiangdong Huang (16 coauthored works)
  • Zhangjie Cao (10 coauthored works)

They have published work in several prominent academic venues, with frequent contributions to:

  • arXiv (Cornell University) - 60 publications
  • Proceedings of the VLDB Endowment - 9 publications
  • IEEE Transactions on Pattern Analysis and Machine Intelligence - 6 publications
  • Proceedings of the ACM on Management of Data - 5 publications
  • SSRN Electronic Journal - 5 publications

Best Publications

  • Learning Transferable Features with Deep Adaptation Networks

    Mingsheng Long;Mingsheng Long;Yue Cao;Jianmin Wang;Michael Jordan

  • Transfer Feature Learning with Joint Distribution Adaptation

    Mingsheng Long;Jianmin Wang;Guiguang Ding;Jiaguang Sun

  • Process Mining Manifesto

    Wil van der Aalst;Wil van der Aalst;Arya Adriansyah;Ana Karla Alves de Medeiros;Franco Arcieri

  • Unsupervised domain adaptation with residual transfer networks

    Mingsheng Long;Han Zhu;Jianmin Wang;Michael I. Jordan

  • Transfer Joint Matching for Unsupervised Domain Adaptation

    Mingsheng Long;Jianmin Wang;Guiguang Ding;Jiaguang Sun

  • Deep Hashing Network for efficient similarity retrieval

    Han Zhu;Mingsheng Long;Jianmin Wang;Yue Cao

  • Adaptation Regularization: A General Framework for Transfer Learning

    Mingsheng Long;Jianmin Wang;Guiguang Ding;Sinno Jialin Pan

  • PredRNN: recurrent neural networks for predictive learning using spatiotemporal LSTMs

    Yunbo Wang;Mingsheng Long;Jianmin Wang;Zhifeng Gao

  • Transferable Representation Learning with Deep Adaptation Networks

    Mingsheng Long;Yue Cao;Zhangjie Cao;Jianmin Wang

  • Semantics-preserving hashing for cross-view retrieval

    Zijia Lin;Guiguang Ding;Mingqing Hu;Jianmin Wang

  • Universal Domain Adaptation

    Kaichao You;Mingsheng Long;Zhangjie Cao;Jianmin Wang

  • Mining process models with non-free-choice constructs

    Lijie Wen;Wil M. Aalst;Jianmin Wang;Jiaguang Sun

  • Transfer Learning with Graph Co-Regularization

    Mingsheng Long;Jianmin Wang;Guiguang Ding;Dou Shen

  • Deep Cauchy Hashing for Hamming Space Retrieval

    Yue Cao;Mingsheng Long;Bin Liu;Jianmin Wang

  • Deep Visual-Semantic Quantization for Efficient Image Retrieval

    Yue Cao;Mingsheng Long;Jianmin Wang;Shichen Liu

  • Transferable Attention for Domain Adaptation

    Ximei Wang;Liang Li;Weirui Ye;Mingsheng Long

  • Deep Visual-Semantic Hashing for Cross-Modal Retrieval

    Yue Cao;Mingsheng Long;Jianmin Wang;Qiang Yang

  • Separate to Adapt: Open Set Domain Adaptation via Progressive Separation

    Hong Liu;Zhangjie Cao;Mingsheng Long;Jianmin Wang

  • Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation

    Xinyang Chen;Sinan Wang;Mingsheng Long;Jianmin Wang

  • A novel approach for process mining based on event types

    Lijie Wen;Jianmin Wang;Wil M. Aalst;Biqing Huang

  • Deep Quantization Network for efficient image retrieval

    Yue Cao;Mingsheng Long;Jianmin Wang;Han Zhu

Frequent Co-Authors

Mingsheng Long
Mingsheng Long Tsinghua University
Philip S. Yu
Philip S. Yu University of Illinois at Chicago
Guiguang Ding
Guiguang Ding Tsinghua University
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Lin Liu
Lin Liu Chinese Academy of Sciences
Arthur H. M. ter Hofstede
Arthur H. M. ter Hofstede Queensland University of Technology
Xuemin Lin
Xuemin Lin Shanghai Jiao Tong University
Marcello La Rosa
Marcello La Rosa University of Melbourne
Qiang Yang
Qiang Yang Hong Kong University of Science and Technology
Akhil Kumar
Akhil Kumar Pennsylvania State University

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