Publications

You can also find my articles on my Google Scholar page or by year.

Conferences & Journals

• CATRO: Channel Pruning via Class-Aware Trace Ratio Optimization
  Wenzheng Hu, Zhengping Che, Ning Liu, Mingyang Li, Jian Tang, Changshui Zhang, and Jianqiang Wang
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
  [Paper on arXiv]
• CP³: Channel Pruning Plug-in for Point Cloud Network
  Yaomin Huang*, Ning Liu*, Zhengping Che, Zhiyuan Xu, Chaomin Shen, Yaxin Peng, Guixu Zhang, Xinmei Liu, Feifei Feng, and Jian Tang (*equal contributions)
  To Appear in the 36th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
  [Paper on arXiv]
• CMG-Net: An End-to-End Contact-based Multi-Finger Dexterous Grasping Network
  Mingze Wei*, Yaomin Huang*, Zhiyuan Xu, Ning Liu, Zhengping Che, Xinyu Zhang, Chaomin Shen, Feifei Feng, Chun Shan, and Jian Tang (*equal contributions)
  To Appear in the 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023
  [Paper on arXiv] [Video Online]
• Alleviating Data Sparsity Problems in Estimated Time of Arrival via Auxiliary Metric Learning
  Yiwen Sun, Wenzheng Hu, Donghua Zhou, Baichuan Mo, Kun Fu, Zhengping Che, Zheng Wang, Shenhao Wang, Jinhua Zhao, Jieping Ye, Jian Tang, and Changshui Zhang
  IEEE Transactions on Intelligent Transportation Systems (T-ITS), 23(12):23231-23243, 2022
  [Paper on IEEE DL]
• SRRNet: A Semantic Representation Refinement Network for Image Segmentation
  Xiaofeng Ding, Tieyong Zeng, Jian Tang, Zhengping Che, and Yaxin Peng
  IEEE Transactions on Multimedia (TMM)
  [Paper on IEEE DL]
• Label-Guided Auxiliary Training Improves 3D Object Detector
  Yaomin Huang*, Xinmei Liu*, Yichen Zhu, Zhiyuan Xu, Chaomin Shen, Zhengping Che, Guixu Zhang, Yaxin Peng, Feifei Feng, and Jian Tang (*equal contributions)
  Proceedings of the 17th European Conference on Computer Vision (ECCV), 2022
  [Paper on ECVA] [Paper on arXiv] [Code on GitHub]
• Generalization and Computation for Policy Classes of Generative Adversarial Imitation Learning
  Yirui Zhou, Yangchun Zhang, Xiaowei Liu, Wanying Wang, Zhengping Che, Zhiyuan Xu, Jian Tang, and Yaxin Peng
  Proceedings of the 17th International Conference on Parallel Problem Solving from Nature (PPSN), 2022
  [Paper on Springer]
• RGB-Depth Fusion GAN for Indoor Depth Completion
  Haowen Wang, Mingyuan Wang, Zhengping Che, Zhiyuan Xu, Xiuquan Qiao, Mengshi Qi, Feifei Feng, and Jian Tang
  Proceedings of the 35th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
  [Paper on CVF] [Paper on arXiv]
• DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator Search
  Yushuo Guan, Ning Liu, Pengyu Zhao, Zhengping Che, Kaigui Bian, Yanzhi Wang, and Jian Tang
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
  [Paper on IEEE DL] [Paper on arXiv]
• CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban Driving
  Yinuo Zhao*, Kun Wu*, Zhiyuan Xu, Zhengping Che, Qi Lu, Jian Tang, and Chi Harold Liu (*equal contributions)
  Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI), 2022
  [Paper on AAAI DL] [Paper on arXiv] [Code on GitHub]
• I-SEA: Importance Sampling and Expected Alignment-based Deep Distance Metric Learning for Time Series Analysis and Embedding
  Sirisha Rambhatla, Zhengping Che, and Yan Liu
  Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI), 2022
  [Paper on AAAI DL] [Code on GitHub]
• Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction
  Xuanzhao Wang, Zhengping Che†, Bo Jiang, Ning Xiao, Ke Yang, Jian Tang, Jieping Ye, Jingyu Wang†, and Qi Qi (†corresponding authors)
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 33(6):2301-2312, 2022
  [Paper on IEEE DL] [Paper on arXiv]
• Hierarchical Graph Attention Network for Few-shot Visual-Semantic Learning
  Chengxiang Yin, Kun Wu, Zhengping Che, Bo Jiang, Zhiyuan Xu, and Jian Tang
  Proceedings of the 18th IEEE/CVF International Conference on Computer Vision (ICCV), 2021
  [Paper on CVF]
• Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?
  Ning Liu*, Geng Yuan*, Zhengping Che, Xuan Shen, Xiaolong Ma, Qing Jin, Jian Ren, Jian Tang, Sijia Liu, and Yanzhi Wang (*equal contributions)
  Proceedings of the 38th International Conference on Machine Learning (ICML), 2021
  [Paper on PMLR] [Paper on arXiv]
• Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control
  Zhiyuan Xu, Kun Wu, Zhengping Che, Jian Tang, and Jieping Ye
  Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS), 2020
  [Paper on NeurIPS] [Paper on arXiv] [Code on GitHub]
• Generative Attention Networks for Multi-Agent Behavioral Modeling
  Guangyu Li*, Bo Jiang*, Hao Zhu, Zhengping Che, and Yan Liu (*equal contributions)
  Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), 2020
  [Paper] [Paper on AAAI DL]
• Benchmarking Deep Learning Models on Large Healthcare Datasets
  Sanjay Purushotham*, Chuizheng Meng*, Zhengping Che, and Yan Liu (*equal contributions)
  Journal of Biomedical Informatics (JBI), 83(1):112-134, 2018
  [Paper on ScienceDirect] [Code on GitHub] [Earlier version on arXiv]
• Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series
  Zhengping Che*, Sanjay Purushotham*, Guangyu Li*, Bo Jiang, and Yan Liu (*equal contributions)
  Proceedings of the 35th International Conference on Machine Learning (ICML), 2018
  [Paper] [Paper on PMLR] [Supplementary Materials] [Slides] [Poster]
• Recurrent Neural Networks for Multivariate Time Series with Missing Values
  Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu
  Scientific Reports (SREP), 8(1):6085, 2018 [SRep Journal Top 100 in 2018]
  [Paper on Nature] [Code on GitHub] [Earlier version on arXiv]
• Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records
  Zhengping Che*, Yu Cheng*, Shuangfei Zhai, Zhaonan Sun, and Yan Liu (*equal contributions)
  Proceedings of the IEEE 17th International Conference on Data Mining (ICDM), 2017
  [Paper] [Paper on IEEE DL] [Full Paper on arXiv] [Slides]
• Deep Learning Solutions for Classifying Patients on Opioid Use
  Zhengping Che, Jennifer St. Sauver, Hongfang Liu, and Yan Liu
  Proceedings of the American Medical Informatics Association Annual Symposium (AMIA), 2017
  [Paper] [Paper on NCBI] [Slides] [Media Coverage on USC Viterbi News]
• Interpretable Deep Models for ICU Outcome Prediction
  Zhengping Che, Sanjay Purushotham, Robinder Khemani, and Yan Liu
  Proceedings of the American Medical Informatics Association Annual Symposium (AMIA), 2016
  [Paper] [Paper on NCBI] [Slides]
• Causal Phenotype Discovery via Deep Networks
  David C. Kale, Zhengping Che, Mohammad Taha Bahadori, Wenzhe Li, Yan Liu, and Randall Wetzel
  Proceedings of the American Medical Informatics Association Annual Symposium (AMIA), 2015
  [Paper] [Paper on NCBI]
• Deep Computational Phenotyping
  Zhengping Che*, David C. Kale*, Wenzhe Li, Mohammad Taha Bahadori, and Yan Liu (*equal contributions)
  Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), 2015
  [Paper] [Paper on ACM DL]
• An Examination of Multivariate Time Series Hashing with Applications to Health Care
  David C. Kale*, Dian Gong*, Zhengping Che*, Gerard Medioni, Randall Wetzel, Patrick Ross, and Yan Liu (*equal contributions)
  Proceedings of the IEEE 14th International Conference on Data Mining (ICDM), 2014
  [Paper] [Paper on IEEE DL]

Book Chapter

• Time Series Feature Learning with Applications to Health Care
  Zhengping Che, Sanjay Purushotham, David C. Kale, Wenzhe Li, Mohammad Taha Bahadori, Robinder Khemani, and Yan Liu
  In Mobile Health: Sensors, Analytic Methods, and Applications, edited by James M. Rehg, Susan A. Murphy, and Santosh Kumar
  [Fulltext on Springer]

Workshops & Preprints

• A Minimalist Ensemble Method for Generalizable Offline Deep Reinforcement Learning
  Kun Wu, Yinuo Zhao, Zhiyuan Xu, Zhen Zhao, Pei Ren, Zhengping Che, Chi Harold Liu, Feifei Feng, and Jian Tang
  ICLR Workshop on Generalizable Policy Learning in the Physical World (GPLPW), 2022
  [Code on GitHub] [Paper Online]
• SCARF: A Semantic Constrained Attention Refinement Network for Semantic Segmentation
  Xiaofeng Ding, Chaomin Shen, Zhengping Che, Tieyong Zeng, and Yaxin Peng
  Proceedings of ICCV Workshop on Autonomous Vehicle Vision (AVVision), 2021 [Best Student Paper Award]
  [Paper on CVF] [Paper Online]
• Multi-Stage Fusion for Multi-Class 3D Lidar Detection
  Zejie Wang, Zhen Zhao, Zhao Jin, Zhengping Che, Jian Tang, Chaomin Shen, and Yaxin Peng
  Proceedings of ICCV Workshop on Autonomous Vehicle Vision (AVVision), 2021
  [Paper on CVF] [Paper Online]
• Personalized and Environment-Aware Battery Prediction for Electric Vehicles
  Dongyue Li*, Guangyu Li*, Bo Jiang*, Zhengping Che, and Yan Liu (*equal contributions)
  KDD Workshop on Mining and Learning from Time Series (MiLeTS), 2021
  [Paper Online]
• Human Pose Transfer with Augmented Disentangled Feature Consistency
  Kun Wu, Chengxiang Yin, Zhengping Che, Bo Jiang, Jian Tang, Zheng Guan, and Gangyi Ding
  arXiv Preprint arXiv:2107.10984, 2021
  [Paper on arXiv]
• Fast Object Detection with Latticed Multi-Scale Feature Fusion
  Yue Shi*, Bo Jiang*, Zhengping Che, and Jian Tang (*equal contributions)
  arXiv Preprint arXiv:2011.02780, 2020
  [Paper on arXiv]
• DBUS: Human Driving Behavior Understanding System
  Max Guangyu Li*, Bo Jiang*, Zhengping Che, Xuefeng Shi, Mengyao Liu, Yiping Meng, Jieping Ye, and Yan Liu (*equal contributions)
  Proceedings of ICCV Workshop on Autonomous Driving (ADW), 2019
  [Paper on CVF] [Paper on IEEE DL] [Slides] [Poster]
• D²-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios
  Zhengping Che, Guangyu Li, Tracy Li, Bo Jiang, Xuefeng Shi, Xinsheng Zhang, Ying Lu, Guobin Wu, Yan Liu, and Jieping Ye
  arXiv Preprint arXiv:1904.01975, 2019
  [Paper on arXiv]
• Deep Multi-Instance Learning for Concept Annotation from Medical Time Series Data
  Sanjay Purushotham, Zhengping Che, Bo Jiang, and Yan Liu
  NIPS Workshop on Machine Learning for Health (NIPS-ML4H), 2017
  [Extended Paper Online]
• Deep Learning Solutions to Computational Phenotyping in Health Care
  Zhengping Che and Yan Liu
  Proceedings of ICDM PhD Forum (ICDMW), 2017
  [Paper] [Paper on IEEE DL]
• DECADE: A Deep Metric Learning Model for Multivariate Time Series
  Zhengping Che, Xinran He, Ke Xu, and Yan Liu
  KDD Workshop on Mining and Learning from Time Series (MiLeTS), 2017
  [Paper Online] [Poster]
• Exploiting Convolutional Neural Network for Risk Prediction with Medical Feature Embedding
  Zhengping Che, Yu Cheng, Zhaonan Sun, and Yan Liu
  NIPS Workshop on Machine Learning for Health (NIPS-ML4HC), 2016
  [Abstract on arXiv]
• Distilling Knowledge from Deep Networks with Applications to Computational Phenotyping
  Zhengping Che, Sanjay Purushotham, and Yan Liu
  NSF Workshop on Data Science, Learning and Applications to Biomedical and Health Sciences (DSLA-BHS), 2016
  [Paper Online (pg.1 - pg.6)]
• Distilling Knowledge from Deep Networks with Applications to Healthcare Domain
  Zhengping Che, Sanjay Purushotham, and Yan Liu
  NIPS Workshop on Machine Learning for Healthcare (NIPS-MLHC), 2015
  [Extended Abstract on arXiv] [Poster]
• Computational Discovery of Physiomes in Critically Ill Children Using Deep Learning
  David C. Kale, Zhengping Che, and Yan Liu
  AMIA Workshop on Data Mining for Medical Informatics: Electronic Phenotyping (AMIA-DMMI), 2014
  [Abstract]

Last Updated: Mar 2023