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Pan Zhou   周 攀

Currently, I am a senior Research Scientist in Sea AI Lab of Sea group. Before, I worked in Salesforce as a research scientist during 2019 to 2021. I completed my Ph.D. degree in 2019 at the National University of Singapore (NUS), fortunately advised by Prof. Jiashi Feng and Prof. Shuicheng Yan. Before studying in NUS, I graduated from Peking University (PKU) in 2016 and during this period, I was fortunately directed by Prof. Zhouchen Lin and Prof. Chao Zhang in ZERO Lab. During the research period, I also work closely with Prof. Xiaotong Yuan. I also spend several wonderful months in 2018 at Georgia Tech as visiting student hosted by Prof. Huan Xu.

 

Research Topics

Deep learning theory and applications, noncovex/convex optimization, compressed sensing, etc.

 

Available Intern Positions at both Beijing and Singapore:

  • (1) self-supervised learning, semi-supervised learning, and their theory
  • (2) next-generation model architechure design, and their theory
  • (3) deep learning optimization, and other theory, e.g. generalization, explanation, approximation
For intern position, please send email to zhoupan@sea.com
  panzhou3 AT gmail DOT com                     Google Scholar                        Curriculum Vitae

News

  • Three papers were accepted by NeurIPS 2020, one oral paper and two poster papers.
  • One paper was accepted by ICML 2020.
  • One paper was accepted as a spotlight paper in NeurIPS 2019.

Publications

2022

Inception Transformer
Chenyang Si*, Weihao Yu*, Pan Zhou, Yichen Zhou, Xinchao Wang, Shuicheng Yan (* equal contribution)
Neural Information Processing Systems (NeurIPS), 2022
[Axriv] [Code]

DualFormer: Local-Global Stratified Transformer for Efficient Video Recognition
Yuxuan Liang, Pan Zhou, Roger Zimmermann, Shuicheng Yan
European Conference on Computer Vision (ECCV), 2022

Video Graph Transformer for Video Question Answering
Junbin Xiao, Pan Zhou, Tat-Seng Chua, Shuicheng Yan
European Conference on Computer Vision (ECCV), 2022

Self-Promoted Supervision for Few-Shot Transformer
Bowen Dong, Pan Zhou, Shuicheng Yan, Wangmeng Zuo
European Conference on Computer Vision (ECCV), 2022

MetaFormer is Actually What You Need for Vision
Weihao Yu, Mi Luo, Pan Zhou, Chenyang Si, Yichen Zhou, Xinchao Wang, Jiashi Feng, Shuicheng Yan
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (oral)

Prototypical Graph Contrastive Learning
Lin Shuai, Liu Chen, Pan Zhou, Hu Zi-yuan, Wang Shuojia, Zhao Ruihui, Zheng Yefeng, Lin Liang, Xing Eric, Liang Xiaodan
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022

2021

A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning
Pan Zhou, Caiming Xiong, Xiaotong Yuan, Steven Hoi
Neural Information Processing Systems (NeurIPS), 2021 (spotlight)
[PDF] [SUPP] [Axriv] [Bibtex] [Code] [Slides] [Poster]

Towards Understanding Why Lookahead Generalizes Better Than SGD and Beyond
Pan Zhou, Hanshu Yan, Xiaotong Yuan, Jiashi Feng, Shuicheng Yan
Neural Information Processing Systems (NeurIPS), 2021
[PDF] [SUPP] [Bibtex] [Code will be released here] [Slides] [Poster]

A Hybrid Stochastic-Deterministic Minibatch Proximal Gradient Method for Efficient Optimization and Generalization
Pan Zhou, XiaoTong Yuan, Zhouchen Lin, and Steven Hoi
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
[PDF] [SUPP] [Bibtex]

Task Similarity Aware Meta Learning: Theory-inspired Improvement on MAML
Pan Zhou, Yingtian Zou, XiaoTong Yuan, Jiashi Feng, Caiming Xiong, and Steven Hoi
International Conference on Uncertainty in Artificial Intelligence (UAI), 2021 (NeurIPS'20 Meta Learning Workshop Paper)
[PDF] [SUPP] [Code]

Wav-BERT: Cooperative Acoustic and Linguistic Representation Learning for Low-Resource Speech Recognition
Guolin Zheng, Yubei Xiao, Ke Gong, Pan Zhou, Xiaodan Liang, and Liang Lin
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021 (Findings)
[Axriv] [Code will be released soon!]

How Important is the Train-Validation Split in Meta-Learning?
Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham Kakade, Huan Wang, Caiming Xiong
International Conference on Machine Learning (ICML), 2021
[Axriv]

Prototypical Contrastive Learning of Unsupervised Representations
Junnan Li, Pan Zhou, Caiming Xiong, Richard Socher, and Steven Hoi
International Conference on Learning Representations (ICLR), 2021
[Axriv] [Bibtex] [Blog] [Code]

Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation
Shuai Lin, Pan Zhou, Xiaodan Liang, Jianheng Tang, Ruihui Zhao, Ziliang Chen and Liang Lin
Association for the Advancement of Artificial Intelligence (AAAI), 2021
[Axriv] [Bibtex] [Code]

Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition
Yubei Xiao, Ke Gong, Pan Zhou, Guolin Zheng, Xiaodan Liang and Liang Lin
Association for the Advancement of Artificial Intelligence (AAAI), 2021
[Axriv] [Bibtex] [Code]

Efficient Gradient Support Pursuit with Less Hard Thresholding for Cardinality-Constrained Learning
Fanhua Shang, Bingkun Wei, Hongying Liu, Yuanyuan Liu, Pan Zhou, and Maoguo Gong
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021

2020

Theory-Inspired Path-Regularized Differential Network Architecture Search
Pan Zhou, Caiming Xiong, Richard Socher, and Steven Hoi
Neural Information Processing Systems (NeurIPS), 2020 (oral)
[PDF] [SUPP] [Axriv] [Bibtex] [Blog] [Code] [Slides] [Poster]

Towards Theoretically Understanding Why SGD Generalizes Better Than ADAM in Deep Learning
Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven Hoi, and Weinan E
Neural Information Processing Systems (NeurIPS), 2020
[PDF] [SUPP] [Axriv] [Bibtex] [Code] [Slides] [Poster]

Improving GAN Training with Probability Ratio Clipping and Sample Reweighting
Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric Xing, and Zhiting Hu
Neural Information Processing Systems (NeurIPS), 2020
[PDF] [Axriv] [Bibtex] [Codes]

Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization
Pan Zhou and Xiaotong Yuan
International Conference on Machine Learning (ICML), 2020
[PDF] [Axriv] [Bibtex]

2019

Efficient Meta Learning via Minibatch Proximal Update
Pan Zhou, Xiaotong Yuan, Huan Xu, Shuicheng Yan, Jiashi Feng
Neural Information Processing Systems (NeurIPS), 2019 (spotlight)
[PDF] [SUPP] [Bibtex] [Codes] [Slides] [Poster]

Tensor Low-rank Representation for Data Recovery and Clustering
Pan Zhou, Canyi Lu, Jiashi Feng, Zhouchen Lin, Shuicheng Yan
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019
[PDF] [SUPP] [Bibtex] [Codes]

Faster First-Order Methods for Stochastic Non-Convex Optimization on Riemannian Manifolds
Pan Zhou, Xiaotong Yuan, Shuicheng Yan, Jiashi Feng
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019
[PDF] [Bibtex]

Generalized Majorization-Minimization for Non-Convex Optimization
Hu Zhang, Pan Zhou, Yi Yang, Jiashi Feng
International Joint Conference on Artificial Intelligence (IJCAI), 2019
[PDF] [Bibtex]

Faster First-Order Methods for Stochastic Non-Convex Optimization on Riemannian Manifolds
Pan Zhou, Xiaotong Yuan, Jiashi Feng
International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
[PDF] [Bibtex]

2018

Efficient Stochastic Gradient Hard Thresholding
Pan Zhou, Xiaotong Yuan, Jiashi Feng
Neural Information Processing Systems (NeurIPS), 2018
[PDF] [Bibtex] [Codes]

New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity
Pan Zhou, Xiaotong Yuan, Jiashi Feng
Neural Information Processing Systems (NeurIPS), 2018
[PDF] [Bibtex]

Understanding Generalization and Optimization Performance of Deep CNNs
Pan Zhou, Jiashi Feng
International Conference on Machine Learning (ICML), 2018
[PDF] [Axriv] [Bibtex]

Deep Adversarial Subspace Clustering
Pan Zhou, Yunqing Hou, Jiashi Feng
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
[PDF] [Codes] [Bibtex]

Empirical Risk Landscape Analysis for Understanding Deep Neural Networks
Pan Zhou, Jiashi Feng
International Conference on Learning Representations (ICLR), 2018
[PDF] [Axriv] [Bibtex]

Task Relation Networks
Jianshu Li, Pan Zhou, Yunpeng Chen, Jian Zhao, Sujoy Roy, Yan Shuicheng, Jiashi Feng, and Terence Sim
IEEE Winter Conference on Applications of Computer Vision (WACV), 2019

2017

Outlier-Robust Tensor PCA
Pan Zhou, Jiashi Feng
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
[PDF] [SUPP] [Codes] [Bibtex]

Tensor Factorization for Low-Rank Tensor Completion
Pan Zhou, Canyi Lu, Zhouchen Lin, Chao Zhang
IEEE Transactions on Image Processing (TIP), 2017
[PDF] [SUPP] [Codes] [Bibtex]

Dictionary Learning with Structured Noise
Pan Zhou, Cong Fang, Zhouchen Lin, Chao Zhang, Edward Y. Chang
Neurocomputing, 2017
[PDF] [Bibtex]

Feature Learning via Partial Differential Equation with Applications to Face Recognition
Cong Fang, Zhenyu Zhao, Pan Zhou, Zhouchen Lin
Pattern Recognition (PR), 2017
[PDF] [Codes] [Bibtex]

2016

Bilevel Model Based Discriminative Dictionary Learning for Recognition
Pan Zhou, Chao Zhang, Zhouchen Lin
IEEE Transactions on Image Processing (TIP), 2016
[PDF] [SUPP] [Bibtex]

Integrated Low-Rank-Based Discriminative Feature Learning for Recognition
Pan Zhou, Zhouchen Lin, Chao Zhang
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2016
[PDF] [SUPP] [Codes] [Bibtex]


Selected Award

  • CVPR 2020 Outstanding Reviewer Award
  • 2019 Chinese Government Award for Outstanding Self-Financed Students Abroad
  • 2018 Microsoft Research Asia Fellowship Award (11 Ph.D. students in Asia)
  • 2015 The Award for Scientific Research
  • 2014 The Model Student of Academic Records
  • 2013 Outstanding Graduate
  • 2011 The Second Prize in 2011 China Robot Contest
  • 2011 National Scholarship

Academic Service

Journal refereeing: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), International Journal of Computer Vision (IJCV), IEEE Transactions on Image Processing (TIP), IEEE Trans. on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), IEEE Signal Processing Letters (SPL).

Conference refereeing: International Conference on Machine Learning (ICML, 2020/2019), Neural Information Processing Systems (NIPS, 2020/20192018), Association for Uncertainty in Artificial Intelligence (UAI, 2020/2019), IEEE Conference on Computer Vision and Pattern Recognition (CVPR, 2020/2019/2018), International Conference on Computer Vision (ICCV, 2019), Association for the Advancement of Artificial Intelligence (AAAI, 2019), Asian Conference on Computer Vision (ACCV, 2018).