Yang Yang @ NJUST-CS
志於道,據於德,依於仁,遊於藝
Yang Yang

Yang Yang - 杨杨


Yang Yang, Professor

School of Computer Science and Engineering, Nanjing University of Science & Engineering


Address : Xiaolingwei 200#, Nanjing 210094, P.R.China

Office : 4061, Computer Science Building

Email : yyang@njust.edu.cn

GitHub : njustkmg

NJUST

Biography

Yang Yang received the B.E. degrees from Nanjing Normal University, China, in 2013, and M.E. degree from Nanjing Universityin 2016.  In 2019, he obtained the PhD degree in computer science from the LAMDA Group led by professor Zhi-Hua Zhou, under the supervision of Prof. Yuan Jiang  and Prof. De-Chuan Zhan. From Oct. 2018 to Oct. 2019, he visited Prof. Hui Xiong's group in Rutgers University.
From Jan. 2020, He became a Professor at PCA Lab (led by professor Jian Yang ) and Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education , School of Computer Science and EngineeringNanjing University of Science and Technology.

Updates

● Long-term recruitment of outstanding interns, joint training with Baidu.

● Recruit postgraduates.

Research Interests

Multi-modal Learning

● Illustrate multi-modal problem in machine learning.

Incremental Learning

● Evolutive Incremental Leaning, i.e., distribution drift, new class detection, feature evolution.

Model Reuse

● Learning with pre-trained models.

Publications

Journal Articles

  • Yang Yang, Hongchen Wei, Zhen-Qiang Sun, Guang-Yu Li, Yuanchun Zhou, Hui Xiong, Jian Yang. S2OSC: A Holistic Semi-Supervised Approach for Open Set Classification. ACM Transactions on Knowledge Discovery from Data (TKDD), 2021. (CCF-B). [Code]
  • Yang Yang, Jia-Qi Yang, Ran Bao, De-Chuan Zhan, Hengshu Zhu, Xiao-Ru Gao, Hui Xiong, Jian Yang. Corporate Relative Valuation using Heterogeneous Multi-Modal Graph Neural Network. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2021. (CCF-A). [Code]
  • Yang Yang, De-Chuan Zhan, Yuan Jiang, Hui Xiong. Reliable Multi-Modal Learning: A Survey. Ruan Jian Xue Bao/Journal of Software, 2019 (in Chinese), DOI:10.13328/j.cnki.jos.006167. (CCF-A)
  • Yang Yang, De-Chuan Zhan, Yi-Feng Wu, Zhi-Bin Liu, Hui Xiong, and Yuan Jiang. Semi-Supervised Multi-Modal Clustering and Classification with Incomplete Modalities. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), DOI:10.1109/TKDE.2019.2932742. (CCF-A)
  • Yang Yang, Zhao-Yang Fu, De-Chuan Zhan, Zhi-Bin Liu, Yuan Jiang. Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), DOI:10.1109/TKDE.2019.2932666. (CCF-A)
  • Xiao Zhang, Hongzheng Yu, Yang Yang, Jingjing Gu, Yujun Li, Fuzhen Zhuang, Dongxiao Yu, Zhaochun Ren. HarMI: Human Activity Recognition via Multi-Modality Incremental Learning. IEEE Journal of Biomedical and Health Informatics(IEEE JBHI).

Conference Papers

  • Yang Yang, Chubing Zhang, Yi-Chu Xu, Dianhai Yu, De-Chuan Zhan, Jian Yang. Rethinking Label-Wise Cross-Modal Retrieval from A Semantic Sharing Perspective. Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-2021), Montreal, Canada, 2021. (CCF-A). [Supplementary]
  • Da-Wei Zhou, Yang Yang, De-Chuan Zhan. Detecting Sequentially Novel Classes with Stable Generalization Ability. Proceedings of the 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021), Delhi, India, 2021.
  • Zixuan Yuan, Hao Liu, Junming Liu, Yanchi Liu, Yang Yang, Renjun Hu, Hui Xiong. Incremental Spatio-Temporal Graph Learning for Online Query-POI Matching. Proceedings of International World Wide Web Conference (WWW), 2021. (CCF-A)
  • Farid Razzak, Fei Yi, Yang Yang, Hui Xiong. An Integrated Multimodal Attention-BasedApproach for Bank Stress Test Prediction. Proceedings of the IEEE International Conference on Data Mining (ICDM), 2019. (CCF-B)
  • Neng-Jun Zhu, Jian Cao, Yan-Chi Liu, Yang Yang, Hao-Chao Ying, Hui Xiong. Sequential Modeling of Hierarchical User Intention and Preference for Next-item Recommendation. In Proceedings of the International Conference on Web Search and Data Mining (WSDM), 2019. (CCF-B)
  • Yang Yang, Da-Wei Zhou, De-Chuan Zhan, Hui Xiong, Yuan Jiang. Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability. The 25rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2019) , Anchorage, Alaska, USA, 2019. [Code]
  • Yang Yang, Ke-Tao Wang, De-Chuan Zhan, Hui Xiong, Yuan Jiang. Comprehensive Semi-Supervised Multi-Modal Learning. Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI-2019) , Macao, China, 2019.
  • Yang Yang, Yi-Feng Wu, De-Chuan Zhan, Zhi-Bin Liu, Yuan Jiang. Deep Robust Unsupervised Multi-Modal Network. Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI-2019) , Honolulu, Hawaii, 2019.
  • Xuan Huo, Yang Yang, Ming Li, De-Chuan Zhan. Learning Semantic Features for Software Defect Prediction by Code Comments Embedding. Proceedings of the IEEE International Conference on Data Mining , Singapore, 2018.
  • Yang Yang, Yi-Feng Wu, De-Chuan Zhan, Yuan Jiang. Deep Multi-modal Learning with Cascade Consensus. Proceedings of the Pacific Rim International Conference on Artificial Intelligence (PRICAI-2018) , Nanjing, China, 2018.
  • Yang Yang, Yi-Feng Wu, De-Chuan Zhan, Zhi-Bin Liu, Yuan Jiang. Complex Object Classification: A Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport. Proceedings of the Annual Conference on ACM SIGKDD (KDD-2018) , London, UK, 2018. [Code]
  • Yang Yang, De-Chuan Zhan, Xiang-Rong Sheng, Yuan Jiang. Semi-Supervised Multi-Modal Learning with Incomplete Modalities. Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI-2018) , Stockholm, Sweden, 2018.
  • Yang Yang, Yi-Feng Wu, De-Chuan Zhan, Yuan Jiang. Multi-Network User Identification via Graph-Aware Embedding. Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2018) , Melbourne, Australia, 2018.
  • Yang Yang, De-Chuan Zhan, Ying Fan, and Yuan Jiang. Instance Specific Discriminative Modal Pursuit: A Serialized Approach. Proceedings of the 9th Asian Conference on Machine Learning (ACML-2017) , Seoul, Korea, 2017. [Best Paper] [Code]
  • Yang Yang, De-Chuan Zhan, Xiang-Yu Guo, and Yuan Jiang. Modal Consistency based Pre-trained Multi-Model Reuse. Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-2017) , Melbourne, Australia, 2017.
  • Yang Yang, De-Chuan Zhan, Yin Fan, Yuan Jiang, and Zhi-Hua Zhou. Deep Learning for Fixed Model Reuse. Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI-2017), San Francisco, CA. 2017.
  • Yang Yang, De-Chuan Zhan and Yuan Jiang. Learning by Actively Querying Strong Modal Features. Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI-2016), New York, NY. 2016, Page: 1033-1039.
  • Yang Yang, Han-Jia Ye, De-Chuan Zhan and Yuan Jiang. Auxiliary Information Regularized Machine for Multiple Modality Feature Learning. Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI-2015), Buenos Aires, Argentina, 2015, Page: 1033-1039.
  • Yang Yang, De-Chuan Zhan, Yuan Jiang. Which One Will be Next? An Analysis of Talent Demission. Proceedings of the International WorkShop on Organizational Behavior and Talent Analytics, London, UK, 2018.

Contests

  • The first runner-up in the Data Mining Competition (in association with CCDM 2014), 2014.

Services

Journal Reviewer: TNNLS, PR

Conference Reviewer: ACML, CIKM, AAAI, IJCAI, KDD, PAKDD, ICDM, ECML

Awards

  • 2020: Excellent doctoral Thesis of JSAI
  • 2018: Artificial Intelligence Scholarship of Nanjing University
  • 2017: Best Paper Award of ACML
  • 2020:JSAI优秀博士论文
  • 2018:南京大学人工智能奖学金
  • 2017:亚洲机器学习国际会议 ACML 最佳论文奖

Code

Our Codes are available on our GitHub account njustkmg.

Demo

Welcome to visit our Ysneaker website. You can upload some photos of your sneakers and our website is able to identify whether it is genuine and what brand it is.

Ysneaker

A Semi-Supervised Attention Model for Genuine Sneaker Identification

To protect consumers and the products they enjoy, it is important to develop convenient tools to help consumers to identify a genuine product from a counterfeit one. Fortunately, we develop a novel Semi-Supervised Attention (SSA) model, which can identify various brands of sneakers. With the multiple images provided by users, SSA can automatically exploit the importance of different images, and give the final identification result.

You can visit http://www.ysneaker.com/about for more details.

Students

Graduated Students

  • Ke-Tao Wang (co-supervised with Prof. De-Chuan Zhan; one IJCAI-19 paper; Now at Huawei);
  • Zhao-Yang Fu (co-supervised with Prof. De-Chuan Zhan; one TKDE-19 paper; Now at Huawei); 

Current Students

  • Jin-Yi Guo
  • Hong-Chen Wei (TKDD'21);
  • Chu-Bing Zhang (IJCAI'21);
Last modified by Yang Yang on February 10, 2021. 皖ICP备2020018519号-2