Publications [DBLP] [Google Scholar] [Semantic Scholar] [Homepage]

(✉ Corresponding Author)

---

Thesis

1. Mingsheng Long. Transfer Learning: Problems and Methods. 1-127, 2014 [PDF] (In Chinese)

Preprints

1. Timer-XL: Long-Context Transformers for Unified Time Series Forecasting
   Yong Liu, Guo Qin, Xiangdong Huang, Jianmin Wang, Mingsheng Long^✉ [arXiv]

2. Metadata Matters for Time Series: Informative Forecasting with Transformers
   Jiaxiang Dong, Haixu Wu, Yuxuan Wang, Li Zhang, Mingsheng Long^✉ [arXiv]

3. Long-Sequence Recommendation Models Need Decoupled Embeddings
   Zhou Hang, Yuezhou Ma, Haixu Wu, Haowen Wang, Mingsheng Long^✉ [arXiv]

4. Unisolver: PDE-Conditional Transformers Are Universal PDE Solvers
   Ningya Feng, Junwei Pan, Jialong Wu, Baixu Chen, Ximei Wang, Qian Li, Xian Hu, Jie Jiang, Mingsheng Long^✉ [arXiv]

5. CompilerDream: Learning a Compiler World Model for General Code Optimization
   Jialong Wu, Chaoyi Deng, Jianmin Wang, Mingsheng Long^✉ [arXiv]

6. depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers
   Kaichao You, Runsheng Bai, Meng Cao, Jianmin Wang, Ion Stoica, Mingsheng Long^✉ [arXiv] [PyTorch Introduction]

7. Transferability in Deep Learning: A Survey
   Junguang Jiang, Yang Shu, Jianmin Wang, Mingsheng Long^✉ [arXiv]

8. On Localized Discrepancy for Domain Adaptation
   Yuchen Zhang, Mingsheng Long^✉, Jianmin Wang, Michael I. Jordan [arXiv]

Journal Articles

1. Skilful Nowcasting of Extreme Precipitation with NowcastNet
   Yuchen Zhang, Mingsheng Long^✉, Kaiyuan Chen, Lanxiang Xing, Ronghua Jin, Michael I. Jordan^✉, Jianmin Wang^✉
   Nature 619, 526–532, 2023 [Link] [Code]
   ⚑ Hot Paper
   ⚑ WAIC Youth Outstanding Paper Award

2. Interpretable Weather Forecasting for Worldwide Stations with a Unified Deep Model
   Haixu Wu, Hang Zhou, Mingsheng Long^✉, Jianmin Wang^✉
   Nature Machine Intelligence (Nat Mach Intell) 5, 602-611, 2023 [Link] [Code]
   ⚑ Cover Article
   ⚑ WAIC Youth Outstanding Paper Award Honorable Mention

3. One Fits Many: Class Confusion Loss for Versatile Domain Adaptation
   Ying Jin, Zhangjie Cao, Ximei Wang, Jianmin Wang, and Mingsheng Long^✉
   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024 [Link] [Code]

4. Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning
   Yang Shu, Zhangjie Cao, Jinghan Gao, Jianmin Wang, Philip S. Yu, Mingsheng Long^✉
   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 45(12):15275-15291, 2023 [Link] [Code]

5. ModeRNN: Harnessing Spatiotemporal Mode Collapse in Unsupervised Predictive Learning
   Zhiyu Yao, Yunbo Wang, Haixu Wu, Jianmin Wang, Mingsheng Long^✉
   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 45(11):13281-13296, 2023 [Link] [Code]

6. PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning
   Yunbo Wang, Haixu Wu, Jianjin Zhang, Zhifeng Gao, Jianmin Wang, Philip S. Yu, Mingsheng Long^✉
   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 45(2):2208-2225, 2023 [Link] [Code]
   ⚑ Highly Cited Paper

7. From Big to Small: Adaptive Learning to Partial-Set Domains
   Zhangjie Cao, Kaichao You, Ziyang Zhang, Jianmin Wang, Mingsheng Long^✉
   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 45(2):1766-1780, 2023 [Link] [Code]

8. VideoDG: Generalizing Temporal Relations in Videos to Novel Domains
   Zhiyu Yao, Yunbo Wang, Jianmin Wang, Philip S. Yu, Mingsheng Long^✉
   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 44(11):7989-8004, 2022 [Link] [Code]

9. Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs
   Kaichao You, Yong Liu, Ziyang Zhang, Jianmin Wang, Michael I. Jordan, Mingsheng Long^✉
   Journal of Machine Learning Research (JMLR), 23(209):1-47, 2022 [Link] [PDF] [Code]

10. Transferable Representation Learning with Deep Adaptation Networks
    Mingsheng Long, Yue Cao, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 41(12):3071-3085, 2019 [Link] [Code]
    ⚑ Highly Cited Paper

11. Deep Learning of Transferable Representation for Scalable Domain Adaptation
    Mingsheng Long, Jianmin Wang, Yue Cao, Jiaguang Sun, Philip S. Yu
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 28(8):2027-2040, 2016 [Link] [Code]

12. Domain Invariant Transfer Kernel Learning
    Mingsheng Long, Jianmin Wang, Jiaguang Sun, Philip S. Yu
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(6):1519-1532, 2015 [Link] [Code]

13. Transfer Learning with Graph Co-Regularization
    Mingsheng Long, Jianmin Wang, Guiguang Ding, Dou Shen, Qiang Yang
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 26(7):1805-1818, 2014 [Link] [Code]

14. Adaptation Regularization: A General Framework for Transfer Learning
    Mingsheng Long, Jianmin Wang, Guiguang Ding, Sinno Jialin Pan, Philip S. Yu
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 26(5):1076-1089, 2014 [Link] [Code]
    ⚑ Highly Cited Paper

Conference Proceedings

1. RoPINN: Region Optimized Physics-Informed Neural Networks
   Haixu Wu, Huakun Luo, Yuezhou Ma, Jianmin Wang, Mingsheng Long^✉
   Neural Information Processing Systems (NeurIPS), 2024 [arXiv]

2. DeepLag: Discovering Deep Lagrangian Dynamics for Intuitive Fluid Prediction
   Qilong Ma, Haixu Wu, Lanxiang Xing, Jianmin Wang, Mingsheng Long^✉
   Neural Information Processing Systems (NeurIPS), 2024 [arXiv]

3. iVideoGPT: Interactive VideoGPTs are Scalable World Models
   Jialong Wu, Shaofeng Yin, Ningya Feng, Xu He, Dong Li, Jianye Hao, Mingsheng Long^✉
   Neural Information Processing Systems (NeurIPS), 2024 [arXiv]

4. Diffusion Tuning: Transferring Diffusion Models via Chain of Forgetting
   Jincheng Zhong, Xingzhuo Guo, Jiaxiang Dong, Mingsheng Long^✉
   Neural Information Processing Systems (NeurIPS), 2024 [arXiv]

5. TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
   Yuxuan Wang, Haixu Wu, Jiaxiang Dong, Yong Liu, Yunzhong Qiu, Haoran Zhang, Jianmin Wang, Mingsheng Long^✉
   Neural Information Processing Systems (NeurIPS), 2024 [arXiv]

6. AutoTimes: Autoregressive Time Series Forecasters via Large Language Models
   Yong Liu, Guo Qin, Xiangdong Huang, Jianmin Wang, Mingsheng Long^✉
   Neural Information Processing Systems (NeurIPS), 2024 [arXiv]

7. Transolver: A Fast Transformer Solver for PDEs on General Geometries
   Haixu Wu, Huakun Luo, Haowen Wang, Jianmin Wang, Mingsheng Long^✉
   International Conference on Machine Learning (ICML), 2024 [PDF] [arXiv] [Code]
   ⚑ Spotlight Paper

8. HelmFluid: Learning Helmholtz Dynamics for Interpretable Fluid Prediction
   Lanxiang Xing, Haixu Wu, Yuezhou Ma, Jianmin Wang, Mingsheng Long^✉
   International Conference on Machine Learning (ICML), 2024 [PDF] [arXiv] [Code]

9. Timer: Generative Pre-trained Transformers are Large Time Series Models
   Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, Mingsheng Long^✉
   International Conference on Machine Learning (ICML), 2024 [PDF] [arXiv] [Code]

10. TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling
    Jiaxiang Dong, Haixu Wu, Yuxuan Wang, Yunzhong Qiu, Li Zhang, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2024 [PDF] [arXiv] [Code]

11. CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding
    Kaiyuan Chen, Xingzhuo Guo, Yu Zhang, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2024 [PDF] [arXiv]

12. HarmonyDream: Task Harmonization Inside World Models
    Haoyu Ma, Jialong Wu, Ningya Feng, Chenjun Xiao, Dong Li, Jianye Hao, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2024 [PDF] [arXiv] [Code]

13. Mobile Attention: Mobile-Friendly Linear-Attention for Vision Transformers
    Zhiyu Yao, Jian Wang, Haixu Wu, Jingdong Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2024 [PDF] [Code]

14. On the Embedding Collapse when Scaling up Recommendation Models
    Xingzhuo Guo, Junwei Pan, Ximei Wang, Baixu Chen, Jie Jiang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2024 [PDF] [arXiv] [Code]

15. Recommender Transformers with Behavior Pathways
    Zhiyu Yao, Xinyang Chen, Sinan Wang, Qinyan Dai, Yumeng Li, Tanchao Zhu, Mingsheng Long^✉
    International World Wide Web Conference (WWW), 2024 [PDF] [arXiv]

16. iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
    Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, Mingsheng Long^✉
    International Conference on Learning Representations (ICLR), 2024 [PDF] [arXiv] [Code]
    ⚑ Spotlight Paper

17. Efficient ConvBN Blocks for Transfer Learning and Beyond
    Kaichao You, Guo Qin, Anchang Bao, Meng Cao, Ping Huang, Jiulong Shan, Mingsheng Long^✉
    International Conference on Learning Representations (ICLR), 2024 [PDF] [arXiv] [Code]
    ⚑ Spotlight Paper

18. Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning
    Jialong Wu, Haoyu Ma, Chaoyi Deng, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2023 [PDF] [arXiv] [Code]

19. ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning
    Junguang Jiang, Baixu Chen, Junwei Pan, Ximei Wang, Liu Dapeng, Jie Jiang, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2023 [PDF] [arXiv] [Code]

20. Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
    Yong Liu, Chenyu Li, Jianmin Wang, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2023 [PDF] [arXiv] [Code]

21. SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling
    Jiaxiang Dong, Haixu Wu, Haoran Zhang, Li Zhang, Jianmin Wang, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2023 [PDF] [arXiv] [Code]
    ⚑ Spotlight Paper

22. Bi-Tuning: Efficient Transfer from Pre-Trained Models
    Jincheng Zhong, Haoyu Ma, Ximei Wang, Zhi Kou, Mingsheng Long^✉
    European Conference on Machine Learning (ECML), 2023 [PDF] [arXiv] [Code]

23. Solving High-Dimensional PDEs with Latent Spectral Models
    Haixu Wu, Tengge Hu, Huakun Luo, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2023 [PDF] [arXiv] [Code]

24. CLIPood: Generalizing CLIP to Out-of-Distributions
    Yang Shu, Xingzhuo Guo, Jialong Wu, Ximei Wang, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2023 [PDF] [arXiv] [Code]

25. Estimating Heterogeneous Treatment Effects: Mutual Information Bounds and Learning Algorithms
    Xingzhuo Guo, Yuchen Zhang, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2023 [PDF] [Code]

26. TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
    Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, Mingsheng Long^✉
    International Conference on Learning Representations (ICLR), 2023 [PDF] [arXiv] [Code]

27. Debiased Self-Training for Semi-Supervised Learning
    Baixu Chen, Junguang Jiang, Ximei Wang, Pengfei Wan, Jianmin Wang, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2022 [PDF] [arXiv] [Slides] [Code]
    ⚑ Oral Paper

28. Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models
    Yang Shu, Zhangjie Cao, Ziyang Zhang, Jianmin Wang, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2022 [PDF] [arXiv]

29. Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting
    Yong Liu, Haixu Wu, Jianmin Wang, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2022 [PDF] [arXiv] [Code]

30. Supported Policy Optimization for Offline Reinforcement Learning
    Jialong Wu, Haixu Wu, Zihan Qiu, Jianmin Wang, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2022 [PDF] [arXiv] [Code]

31. Out-of-Dynamics Imitation Learning from Multimodal Demonstrations
    Yiwen Qiu, Zhangjie Cao, Jialong Wu, Mingsheng Long^✉
    Conference on Robot Learning (CoRL), 2022 [PDF] [arXiv] [Code]

32. Flowformer: Linearizing Transformers with Conservation Flows
    Haixu Wu, Jialong Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2022 [PDF] [arXiv] [Code]

33. Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
    Jiehui Xu, Haixu Wu, Jianmin Wang, Mingsheng Long^✉
    International Conference on Learning Representations (ICLR), 2022 [PDF] [arXiv] [Code]
    ⚑ Spotlight Paper

34. Decoupled Adaptation for Cross-Domain Object Detection
    Junguang Jiang, Baixu Chen, Jianmin Wang, Mingsheng Long^✉
    International Conference on Learning Representations (ICLR), 2022 [PDF] [arXiv] [Code]

35. X-model: Improving Data Efficiency in Deep Learning with A Minimax Model
    Ximei Wang, Xinyang Chen, Jianmin Wang, Mingsheng Long^✉
    International Conference on Learning Representations (ICLR), 2022 [PDF] [arXiv] [Code]

36. Continual Predictive Learning from Videos
    Geng Chen, Wendong Zhang, Han Lu, Siyu Gao, Yunbo Wang, Mingsheng Long, Xiaokang Yang
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022 [arXiv]

37. Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
    Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2021 [PDF] [Appendix] [arXiv] [Code]
    ⚑ Ranks 10th in NeurIPS 2021

38. Cycle Self-Training for Domain Adaptation
    Hong Liu, Jianmin Wang, Mingsheng Long^✉
    Neural Information Processing Systems (NeurIPS), 2021 [PDF] [Appendix] [arXiv] [Code]

39. LogME: Practical Assessment of Pre-trained Models for Transfer Learning
    Kaichao You, Yong Liu, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2021 [PDF] [Appendix] [Code]

40. Zoo-Tuning: Adaptive Transfer from A Zoo of Models
    Yang Shu, Zhi Kou, Zhangjie Cao, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2021 [PDF] [Appendix] [Code]

41. Self-Tuning for Data-Efficient Deep Learning
    Ximei Wang, Jinghan Gao, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2021 [PDF] [Appendix] [Code]

42. Representation Subspace Distance for Domain Adaptation Regression
    Xinyang Chen, Sinan Wang, Jianmin Wang, Mingsheng Long^✉
    International Conference on Machine Learning (ICML), 2021 [PDF] [Appendix] [Code]

43. MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions
    Haixu Wu, Zhiyu Yao, Jianmin Wang, Mingsheng Long^✉
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021 [PDF] [Appendix]

44. Regressive Domain Adaptation for Unsupervised Keypoint Detection
    Junguang Jiang, Yifei Ji, Ximei Wang, Yufeng Liu, Jianmin Wang, Mingsheng Long^✉
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021 [PDF] [Code]

45. Transferable Query Selection for Active Domain Adaptation
    Bo Fu, Zhangjie Cao, Jianmin Wang, Mingsheng Long^✉
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021 [PDF] [Appendix] [Code]
    ⚑ Oral Paper

46. Open Domain Generalization with Domain-Augmented Meta-Learning
    Yang Shu, Zhangjie Cao, Chenyu Wang, Jianmin Wang, Mingsheng Long^✉
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021 [PDF] [Appendix] [Code]

47. MetaSets: Meta-Learning on Point Sets for Generalizable Representations
    Chao Huang, Zhangjie Cao, Yunbo Wang, Jianmin Wang, Mingsheng Long^✉
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021 [PDF] [Appendix] [Code]

48. Transferable Calibration with Lower Bias and Variance in Domain Adaptation
    Ximei Wang, Mingsheng Long^✉, Jianmin Wang, Michael I. Jordan
    Neural Information Processing Systems (NeurIPS), 2020 [PDF] [Appendix] [Code]

49. Co-Tuning for Transfer Learning
    Kaichao You, Zhi Kou, Mingsheng Long^✉, Jianmin Wang
    Neural Information Processing Systems (NeurIPS), 2020 [PDF] [Code]

50. Stochastic Normalization
    Zhi Kou, Kaichao You, Mingsheng Long^✉, Jianmin Wang
    Neural Information Processing Systems (NeurIPS), 2020 [PDF] [Code]

51. Learning to Adapt to Evolving Domains
    Hong Liu, Mingsheng Long^✉, Jianmin Wang, Yu Wang
    Neural Information Processing Systems (NeurIPS), 2020 [PDF] [Code]

52. Minimum Class Confusion for Versatile Domain Adaptation
    Ying Jin, Ximei Wang, Mingsheng Long^✉, Jianmin Wang
    European Conference on Computer Vision (ECCV), 2020 [PDF] [Code]

53. Learning to Detect Open Classes for Universal Domain Adaptation
    Bo Fu, Zhangjie Cao, Mingsheng Long^✉, Jianmin Wang
    European Conference on Computer Vision (ECCV), 2020 [PDF] [Code]

54. Negative Margin Matters: Understanding Margin in Few-shot Classification
    Bin Liu, Yue Cao, Yutong Lin, Qi Li, Zheng Zhang, Mingsheng Long, Han Hu
    European Conference on Computer Vision (ECCV), 2020 [PDF] [Code]

55. Unsupervised Transfer Learning for Spatiotemporal Predictive Networks
    Zhiyu Yao, Yunbo Wang, Mingsheng Long^✉, Jianmin Wang
    International Conference on Machine Learning (ICML), 2020 [PDF] [Code]

56. Probabilistic Video Prediction from Noisy Data with a Posterior Confidence
    Yunbo Wang, Jiajun Wu, Mingsheng Long, Joshua Tenenbaum
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020 [PDF] [Code]

57. Progressive Adversarial Networks for Fine-Grained Domain Adaptation
    Sinan Wang, Xinyang Chen, Yunbo Wang, Mingsheng Long^✉, Jianmin Wang
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020 [PDF] [Code]

58. Simultaneous Learning of Pivots and Representations for Cross-Domain Sentiment Classification
    Liang Li, Weirui Ye, Mingsheng Long^✉, Yateng Tang, Jin Xu, Jianmin Wang
    AAAI Conference on Artificial Intelligence (AAAI), 2020 [PDF]

59. Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning
    Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long^✉, Jianmin Wang
    Neural Information Processing Systems (NeurIPS), 2019 [PDF] [Code]

60. Transferable Normalization: Towards Improving Transferability of Deep Neural Networks
    Ximei Wang, Ying Jin, Mingsheng Long^✉, Jianmin Wang, Michael I. Jordan
    Neural Information Processing Systems (NeurIPS), 2019 [PDF] [Code]

61. Bridging Theory and Algorithm for Domain Adaptation
    Yuchen Zhang, Tianle Liu, Mingsheng Long^✉, Michael I. Jordan
    International Conference on Machine Learning (ICML), 2019 [PDF] [Appendix] [Code]
    ⚑ Long Oral Paper

62. Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers
    Hong Liu, Mingsheng Long^✉, Jianmin Wang, Michael I. Jordan
    International Conference on Machine Learning (ICML), 2019 [PDF] [Code]
    ⚑ Long Oral Paper

63. Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
    Kaichao You, Ximei Wang, Mingsheng Long^✉, Michael I. Jordan
    International Conference on Machine Learning (ICML), 2019 [PDF] [Code]

64. Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation
    Xinyang Chen, Sinan Wang, Mingsheng Long^✉, Jianmin Wang
    International Conference on Machine Learning (ICML), 2019 [PDF] [Code]

65. Maximum-Margin Hamming Hashing
    Rong Kang, Yue Cao, Mingsheng Long^✉, Jianmin Wang, Philip S. Yu
    IEEE International Conference on Computer Vision (ICCV), 2019 [PDF] [Code]

66. Universal Domain Adaptation
    Kaichao You, Mingsheng Long^✉, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 [PDF] [Code]

67. Separate to Adapt: Open Set Domain Adaptation via Progressive Separation
    Hong Liu, Zhangjie Cao, Mingsheng Long^✉, Jianmin Wang, Qiang Yang
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 [PDF] [Code]

68. Learning to Transfer Examples for Partial Domain Adaptation
    Zhangjie Cao, Kaichao You, Mingsheng Long^✉, Jianmin Wang, Qiang Yang
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 [PDF] [Code]

69. Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics
    Yunbo Wang, Jianjin Zhang, Hongyu Zhu, Mingsheng Long^✉, Jianmin Wang, Philip S. Yu
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 [PDF] [Code]

70. Eidetic 3D LSTM: A Model for Video Prediction and Beyond
    Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Mingsheng Long, Li Fei-Fei
    International Conference on Learning Representations (ICLR), 2019 [PDF] [Code]

71. Transferable Curriculum for Weakly-Supervised Domain Adaptation
    Yang Shu, Zhangjie Cao, Mingsheng Long^✉, Jianmin Wang
    AAAI Conference on Artificial Intelligence (AAAI), 2019 [PDF] [Code]

72. Transferable Attention for Domain Adaptation
    Ximei Wang, Liang Li, Weirui Ye, Mingsheng Long^✉, Jianmin Wang
    AAAI Conference on Artificial Intelligence (AAAI), 2019 [PDF]

73. Conditional Adversarial Domain Adaptation
    Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
    Neural Information Processing Systems (NeurIPS), 2018 [PDF] [Poster] [Code]
    ⚑ Ranks 6th in NeurIPS 2018

74. Generalized Zero-Shot Learning with Deep Calibration Network
    Shichen Liu, Mingsheng Long^✉, Jianmin Wang, Michael I. Jordan
    Neural Information Processing Systems (NeurIPS), 2018 [PDF] [Code]

75. Partial Adversarial Domain Adaptation
    Zhangjie Cao, Lijia Ma, Mingsheng Long^✉, Jianmin Wang
    European Conference on Computer Vision (ECCV), 2018 [PDF] [Code]

76. Cross-Modal Hamming Hashing
    Yue Cao, Bin Liu, Mingsheng Long^✉, Jianmin Wang
    European Conference on Computer Vision (ECCV), 2018 [PDF]

77. PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
    Yunbo Wang, Zhifeng Gao, Mingsheng Long^✉, Jianmin Wang, Philip S. Yu
    International Conference on Machine Learning (ICML), 2018 [PDF] [Code]

78. PredCNN: Predictive Learning with Cascade Convolutions
    Ziru Xu, Yunbo Wang, Mingsheng Long^✉, Jianmin Wang
    International Joint Conference on Artificial Intelligence (IJCAI), 2018 [PDF] [Code]

79. Partial Transfer Learning with Selective Adversarial Networks
    Zhangjie Cao, Mingsheng Long^✉, Jianmin Wang, Michael I. Jordan
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 [PDF] [Slides] [Code]

80. HashGAN: Deep Learning to Hash with Pair Conditional Wasserstein GAN
    Yue Cao, Bin Liu, Mingsheng Long^✉, Jianmin Wang
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 [PDF]

81. Deep Cauchy Hashing for Hamming Space Retrieval
    Yue Cao, Mingsheng Long^✉, Bin Liu, Jianmin Wang
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 [PDF] [Code]

82. Multi-Adversarial Domain Adaptation
    Zhongyi Pei, Zhangjie Cao, Mingsheng Long^✉, Jianmin Wang
    AAAI Conference on Artificial Intelligence (AAAI), 2018 [PDF] [Code]
    ⚑ Ranks 15th in AAAI 2018

83. Unsupervised Domain Adaptation with Distribution Matching Machines
    Yue Cao, Mingsheng Long^✉, Jianmin Wang
    AAAI Conference on Artificial Intelligence (AAAI), 2018 [PDF]

84. Transfer Adversarial Hashing for Hamming Space Retrieval
    Zhangjie Cao, Mingsheng Long^✉, Chao Huang, Jianmin Wang
    AAAI Conference on Artificial Intelligence (AAAI), 2018 [PDF] [Code]

85. PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
    Yunbo Wang, Mingsheng Long^✉, Jianmin Wang, Zhifeng Gao, Philip S. Yu
    Neural Information Processing Systems (NeurIPS), 2017 [PDF] [Code]

86. Learning Multiple Tasks with Multilinear Relationship Networks
    Mingsheng Long, Zhangjie Cao, Jianmin Wang, Philip S. Yu
    Neural Information Processing Systems (NeurIPS), 2017 [PDF] [Poster] [Code]

87. HashNet: Deep Learning to Hash by Continuation
    Zhangjie Cao, Mingsheng Long^✉, Jianmin Wang, Philip S. Yu
    IEEE International Conference on Computer Vision (ICCV), 2017 [PDF] [Code]

88. Deep Transfer Learning with Joint Adaptation Networks
    Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan
    International Conference on Machine Learning (ICML), 2017 [PDF] [Slides] [Poster] [Code]
    ⚑ Ranks 10th in ICML 2017

89. Spatiotemporal Pyramid Network for Video Action Recognition
    Yunbo Wang, Mingsheng Long^✉, Jianmin Wang, Philip S. Yu
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 [PDF] [Slides] [Code]

90. Deep Visual-Semantic Quantization for Efficient Image Retrieval
    Yue Cao, Mingsheng Long^✉, Jianmin Wang, Shichen Liu
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 [PDF] [Code]

91. Transitive Hashing Network for Heterogeneous Multimedia Retrieval
    Zhangjie Cao, Mingsheng Long^✉, Jianmin Wang, Qiang Yang
    AAAI Conference on Artificial Intelligence (AAAI), 2017 [PDF]

92. Collective Deep Quantization for Efficient Cross-Modal Retrieval
    Yue Cao, Mingsheng Long^✉, Jianmin Wang, Shichen Liu
    AAAI Conference on Artificial Intelligence (AAAI), 2017 [PDF] [Code]

93. Unsupervised Domain Adaptation with Residual Transfer Networks
    Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan
    Neural Information Processing Systems (NeurIPS), 2016 [PDF] [Poster] [Code]

94. Deep Visual-Semantic Hashing for Cross-Modal Retrieval
    Yue Cao, Mingsheng Long^✉, Jianmin Wang, Qiang Yang, Philip S. Yu
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016 [PDF]

95. Composite Correlation Quantization for Efficient Multimodal Retrieval
    Mingsheng Long, Yue Cao, Jianmin Wang, Philip S. Yu
    ACM SIGIR Conference on Information Retrieval (SIGIR), 2016 [PDF] [Slides] [Code]
    ⚑ Ranks 12nd in SIGIR 2016

96. Deep Quantization Network for Efficient Image Retrieval
    Yue Cao, Mingsheng Long^✉, Jianmin Wang, Han Zhu, Qingfu Wen
    AAAI Conference on Artificial Intelligence (AAAI), 2016 [PDF] [Slides] [Code]

97. Deep Hashing Network for Efficient Similarity Retrieval
    Han Zhu, Mingsheng Long^✉, Jianmin Wang, Yue Cao
    AAAI Conference on Artificial Intelligence (AAAI), 2016 [PDF] [Code]
    ⚑ Ranks 13th in AAAI 2016

98. Learning Transferable Features with Deep Adaptation Networks
    Mingsheng Long, Yue Cao, Jianmin Wang, Michael I. Jordan
    International Conference on Machine Learning (ICML), 2015 [PDF] [Slides] [Poster] [Code]
    ⚑ Ranks 5th in ICML 2015
    ⚑ Test of Time Award at FTL-IJCAI 2021

99. Transfer Joint Matching for Unsupervised Domain Adaptation
    Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, Philip S. Yu
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 [PDF] [Code]

100. Transfer Feature Learning with Joint Distribution Adaptation
     Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, Philip S. Yu
     IEEE International Conference on Computer Vision (ICCV), 2013 [PDF] [Code]
     ⚑ Ranks 2nd in ICCV 2013

101. Transfer Sparse Coding for Robust Image Representation
     Mingsheng Long, Guiguang Ding, Jianmin Wang, Jiaguang Sun, Philip S. Yu
     IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 [PDF] [Code]

102. Transfer Learning with Graph Co-Regularization
     Mingsheng Long, Jianmin Wang, Guiguang Ding, Dou Shen, Qiang Yang
     AAAI Conference on Artificial Intelligence (AAAI), 2012 [PDF] [Code]
     ⚑ Ranks 10th in AAAI 2012

103. Dual Transfer Learning
     Mingsheng Long, Jianmin Wang, Guiguang Ding, Wei Cheng, Xiang Zhang, Wei Wang
     SIAM International Conference on Data Mining (SDM), 2012 [PDF]
     ⚑ Best Paper Nominee

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