Mingsheng Long

龙明盛

Associate Professor, Ph.D Supervisor
School of Software, Tsinghua University

Machine Learning Group
National Engineering Lab for Big Data Software

longmingsheng@gmail.com, mingsheng@tsinghua.edu.cn

Room 11-413, East Main Building, Tsinghua University, Beijing, China

Reminder to prospective students: My PhD and Master positions for class 2020 have been filled.

Biography

My current research spans machine learning theories, algorithms, and systems, with special interests in transfer learning, deep learning, and predictive learning. In particular, my research is persistently dedicated to advancing all areas of transfer learning, including domain adaptation, inductive transfer learning, multi-task learning, few-shot learning, and learning to learn.

I am an associate professor and Ph.D supervisor in the School of Software, Tsinghua University. I am leading the Machine Learning Group in the National Engineering Lab for Big Data Software. I am looking for highly motivated graduate or undergraduate students to work on machine learning and its applications in AI and data science. Please email me with curriculum vitae if you are interested.

My Quote

“Everything should be made as simple as possible, but no simpler.” --Albert Einstein

Research

Machine Learning: Transfer Learning, Deep Learning, Predictive Learning

Education

Ph.D. in Computer Science, 2008-2014
Department of Computer Science and Technology, Tsinghua University, Beijing, China
Advisor: Prof. Jianmin Wang
B.E. in Electrical Engineering, 2004-2008
Department of Electrical Engineering, Tsinghua University, Beijing, China

Research Experience

Visiting Researcher, 2014-2015
Department of Computer Science, UC Berkeley
Advisor: Prof. Michael I. Jordan
Postdoc Researcher, 2014-2016
School of Software, Tsinghua University, Beijing, China
Advisor: Prof. Jianmin Wang

Selected Publications [Full List] [DBLP] [Google Scholar]

(* Corresponding Author)

Thesis

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

Preprints

Towards Understanding the Transferability of Deep Representations
Hong Liu, Mingsheng Long*, Jianmin Wang, Michael I. Jordan [arXiv]
Less Confusion More Transferable: Minimum Class Confusion for Versatile Domain Adaptation
Ying Jin, Ximei Wang, Mingsheng Long*, Jianmin Wang [arXiv]
Adversarial Pyramid Network for Video Domain Generalization
Zhiyu Yao, Yunbo Wang, Xingqiang Du, Mingsheng Long*, Jianmin Wang [arXiv]

Journal Articles

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]
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]
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]
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]
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] (ESI Highly Cited Paper)

Conference Proceedings

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 (NIPS), 2019 [PDF] [Code]
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks
Ximei Wang, Ying Jin, Mingsheng Long*, Jianmin Wang, Michael I. Jordan
Neural Information Processing Systems (NIPS), 2019 [PDF] [Code]
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]
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]
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]
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]
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]
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]
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]
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]
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]
Transferable Attention for Domain Adaptation
Ximei Wang, Liang Li, Weirui Ye, Mingsheng Long*, Jianmin Wang
AAAI Conference on Artificial Intelligence (AAAI), 2019 [PDF]
Conditional Adversarial Domain Adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
Neural Information Processing Systems (NIPS), 2018 [PDF] [Poster] [Code]
Generalized Zero-Shot Learning with Deep Calibration Network
Shichen Liu, Mingsheng Long*, Jianmin Wang, Michael I. Jordan
Neural Information Processing Systems (NIPS), 2018 [PDF] [Code]
Partial Adversarial Domain Adaptation
Zhangjie Cao, Lijia Ma, Mingsheng Long*, Jianmin Wang
European Conference on Computer Vision (ECCV), 2018 [PDF] [Code]
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]
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]
Multi-Adversarial Domain Adaptation
Zhongyi Pei, Zhangjie Cao, Mingsheng Long*, Jianmin Wang
AAAI Conference on Artificial Intelligence (AAAI), 2018 [PDF] [Code]
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 (NIPS), 2017 [PDF]
Learning Multiple Tasks with Multilinear Relationship Networks
Mingsheng Long, Zhangjie Cao, Jianmin Wang, Philip S. Yu
Neural Information Processing Systems (NIPS), 2017 [PDF] [Poster] [Code]
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]
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]
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]
Unsupervised Domain Adaptation with Residual Transfer Networks
Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan
Neural Information Processing Systems (NIPS), 2016 [PDF] [Poster] [Code]
Deep Hashing Network for Efficient Similarity Retrieval
Han Zhu, Mingsheng Long*, Jianmin Wang, Yue Cao
AAAI Conference on Artificial Intelligence (AAAI), 2016 [PDF] [Code]
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] (1000+ Citations)
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]
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]
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]
Dual Transfer Learning
Mingsheng Long, Jianmin Wang, Guiguang Ding, Wei Cheng, Xiang Zhang, Wei Wang
SIAM International Conference on Data Mining (SDM), 2012 [PDF] (Nominee of Best Paper Award)

Software

Xlearn: An Open-Source Package for Deep Transfer Learning [Code]
DeepHash: An Open-Source Package for Deep Learning to Hash [Code]

Honors and Awards

First Prize, Technical Invention Award, Ministry of Education (MOE), 2018
Distinguished Dissertation Award, China Association for Artificial Intelligence (CAAI), 2016
Distinguished Dissertation Award Nominee, China Computer Federation (CCF), 2014
Tsinghua University Outstanding Ph.D. Graduate, 2014
Beijing Municipal Government Award for Outstanding Ph.D. Graduates, 2014
Academic Rising Star, School of Software, Tsinghua University, 2014
Zhong Shi-Mo Outstanding Graduate Scholarship, Tsinghua University, 2013
Intel Fellowship, Tsinghua University, 2012
Nominee of Best Paper Award, SIAM SDM, 2012
Outstanding Contribution Award, Big Data Group, Tsinghua University, 2012
Student Travel Award, IEEE ICDM, 2010

Professional Activities

Invited Talks

Transfer Learning: Theories and Algorithms, Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019 [PDF]
Transfer Learning: Theories and Algorithms, CCF Conference on Artificial Intelligence, CCFAI 2019 [PDF]
Transfer Learning: From Algorithms to Theories and Back, Vision And Learning SEminar, VALSE 2019 [PDF]
Transfer Learning: Generalizing Deep Learning across Domains and Tasks, Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018 [PDF]
Predictive Learning for Video Prediction, Chinese Conference on Data Mining, CCDM 2018 [PDF]
Deep Transfer Learning, Google AI China Center Machine Learning Day, 2018 [PDF]
Recent Advances in Transfer Learning, Samsung Machine Learning Workshop, 2017 [PDF]
Deep Hashing for Multimedia Retrieval, International Conference on Data Science, 2017 [PDF]

PC Chair

Transferring and Adapting Source Knowledge in Computer Vision Workshop and Challenge (TASK-CV), ICCV, 2017
Transfer and Multi-Task Learning Workshop, NIPS, 2015

Area Chair

International Conference on Learning Representations (ICLR) 2020
IEEE International Conference on Multimedia and Expo (ICME) 2020

Senior PC Member

AAAI Conference on Artificial Intelligence (AAAI) 2020
International Joint Conference on Artificial Intelligence (IJCAI) 2019, 2020

PC Member | Reviewer

International Conference on Machine Learning (ICML) 2014, 2017, 2018, 2019
Neural Information Processing Systems (NIPS) 2016, 2017, 2018, 2019
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2018, 2019
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018, 2019
IEEE International Conference on Computer Vision (ICCV) 2019
European Conference on Computer Vision (ECCV) 2018
International Joint Conference on Artificial Intelligence (IJCAI) 2017, 2018
AAAI Conference on Artificial Intelligence (AAAI) 2018, 2019
ACM Multimedia Conference (ACMMM) 2017, 2018
ACM Conference on Information and Knowledge Management (CIKM) 2017, 2018
SIAM International Conference on Data Mining (SDM) 2018
Artificial Intelligence Journal (AIJ) 2016, 2018
Journal of Machine Learning Research (JMLR) 2018
International Journal of Computer Vision (IJCV) 2019
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2016, 2017, 2018, 2019
IEEE Transactions on Knowledge and Data Engineering (TKDE) 2014, 2015, 2016, 2017, 2018, 2019
IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2016, 2017
IEEE Transactions on Image Processing (TIP) 2014, 2015, 2016, 2017, 2018
IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) 2018
IEEE Transactions on Multimedia (TMM) 2018
IEEE Transactions on Cybernetics 2018
ACM Computing Surveys 2019
ACM Transactions on Knowedge Discovery from Data (TKDD) 2016
ACM Transactions on Intelligent Systems and Technology (TIST) 2017
ACM Transactions on Multimedia Computing, Communications and Applications (TOMM) 2014, 2015
Machine Learning Journal (MLJ) 2016
Data Mining and Knowledge Discovery (DMKD) 2012
Information Science (IS) 2016, 2017
Pattern Recognition (PR) 2016
Neural Networks 2019
Nature Communications 2019

Research Grants

Principal Investigator, Intelligent Severe Weather Nowcasting System, China Meteorological Administration, 2019-2020
Principal Investigator, Few-Shot Learning Algorithms, Kuaishou Research, 2019-2020
Principal Investigator, Transfer Learning Algorithms, Kuaishou Research, 2019-2020
Principal Investigator, Recommendation Algorithm based on Deep Learning, Alibaba Innovative Research (AIR), 2018-2019
Principal Investigator, Spatiotemporal Deep Learning for Severe Weather Nowcasting, National Key R&D Plan, 2018-2022
Principal Investigator, Spatiotemporal Deep Learning: Algorithms and Applications, Natural Science Foundation of China, 2018-2021
Principal Investigator, Safe Transfer Learning for Big Data Analytics, Natural Science Foundation of China, 2016-2018
Principal Investigator, Scalable Transfer Learning: Theory and Algorithms, China Postdoc Science Foundation, 2015-2016

Teaching

Instructor: Deep Learning, 2018-Present
Instructor: Introduction to Machine Learning, 2019-Present
Instructor: Introduction to Data Science, 2018-Present
Co-Instructor: Introduction to Big Data Technology, 2017-Present
Co-Instructor: Foundations of Big Data Systems (B), 2015-Present
Co-Instructor: Foundations of Big Data Systems (A), 2016-Present

Students

Graduates

Class 2019: Zhiyu Yao (PhD), Chao Huang, Zhi Kou, Jincheng Zhong, Jinghan Gao
Class 2018: Yang Shu (PhD), Bin Liu, Ying Jin, Xingqiang Du, Bo Fu
Class 2017: Ximei Wang (PhD), Xinyang Chen (PhD), Yuchen Zhang, Jintao Liu, Sinan Wang, Liang Li
Class 2016: Ziru Xu (Alimama), Jianjin Zhang (Microsoft), Zhifeng Gao (Alimama)
Class 2015: Yunbo Wang (PhD), Zhongyi Pei (PhD), Qingfu Wen (SenseTime), Jue Wang (Government)
Class 2014: Yue Cao (MSRA), Han Zhu (Alimama, A-Star), Feng Li (Alimama), Qiaoan Chen (Tencent)
Class 2013: Yateng Tang (Tencent)
Class 2012: Wei Li (Google)
Class 2011: Wu Xiang (Hulu), Hao Hu (Google)

Undergraduates

Class 2018: Qi Li, Jialong Wu
Class 2017: Hong Liu, Yong Liu, Yifei Ji, Shijie Wang, Haoyu Niu, Jianyu Tang, Yuning Chen
Class 2016: Kaichao You, Tianle Liu, Haixu Wu, Weirui Ye, Ziping Sun
Class 2015: Zhiyu Yao (THU->THU PhD), Chao Huang (THU->THU MS), Zhi Kou (THU-THU MS), Lijia Ma (THU->UW PhD), Hongyu Zhu (THU->CMU MS)
Class 2014: Zhangjie Cao (THU->Stanford PhD), Shichen Liu (THU->USC PhD), Bin Liu (THU->THU MS)