Publications
You can also find my publications on my Google Scholar page.
2024
ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning
Kun Wu, Yinuo Zhao, Zhiyuan Xu, Zhengping Che, Chengxiang Yin, Chi Harold Liu, Qinru Qiu, Feifei Feng, and Jian Tang
IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
[Paper on IEEE DL] [Earlier Version Online]
RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion
Haowen Wang*, Zhengping Che*, Yufan Yang, Mingyuan Wang, Zhiyuan Xu, Xiuquan Qiao, Mengshi Qi, Feifei Feng, and Jian Tang (*equal contributions)
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 46(11):7088-7101, 2024
[Paper on IEEE DL] [Paper on arXiv] [Code on GitHub]
A Survey on Robotics with Foundation Models: toward Embodied AI
Zhiyuan Xu*, Kun Wu*, Junjie Wen, Jinming Li, Ning Liu, Zhengping Che, and Jian Tang (*equal contributions)
arXiv Preprint arXiv:2402.02385, 2024
[Paper on arXiv]
SM3: Self-supervised Multi-task Modeling with Multi-view 2D Images for Articulated Objects
Haowen Wang, Zhen Zhao, Zhao Jin, Zhengping Che, Liang Qiao, Yakun Huang, Zhipeng Fan, Xiuquan Qiao, and Jian Tang
Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024
[Paper on IEEE DL] [Paper on arXiv]
Language-Conditioned Robotic Manipulation with Fast and Slow Thinking
Minjie Zhu*, Yichen Zhu*, Jinming Li, Junjie Wen, Zhiyuan Xu, Zhengping Che, Chaomin Shen, Yaxin Peng, Dong Liu, Feifei Feng, and Jian Tang (*equal contributions)
Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024
[Paper on IEEE DL] [Paper on arXiv]
Object-Centric Instruction Augmentation for Robotic Manipulation
Junjie Wen*, Yichen Zhu*, Minjie Zhu, Jinming Li, Zhiyuan Xu, Zhengping Che, Chaomin Shen, Yaxin Peng, Dong Liu, Feifei Feng, and Jian Tang (*equal contributions)
Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024
[Paper on IEEE DL] [Paper on arXiv]
An Efficient Generalizable Framework for Visuomotor Policies via Control-aware Augmentation and Privilege-guided Distillation
Yinuo Zhao*, Kun Wu*, Tianjiao Yi, Zhiyuan Xu, Zhengping Che, Qinru Qiu, Chi Harold Liu, and Jian Tang (*equal contributions)
arXiv Preprint arXiv:2401.09258, 2024
[Paper on arXiv]
Visual Robotic Manipulation with Depth-Aware Pretraining
Wanying Wang*, Jinming Li*, Yichen Zhu*, Zhiyuan Xu, Zhengping Che, Yaxin Peng, Chaomin Shen, Dong Liu, Feifei Feng, and Jian Tang (*equal contributions)
arXiv Preprint arXiv:2401.09038, 2024
[Paper on arXiv]
SWBT: Similarity Weighted Behavior Transformer with the Imperfect Demonstration for Robotic Manipulation
Kun Wu, Ning Liu, Zhen Zhao, Di Qiu, Jinming Li, Zhengping Che, Zhiyuan Xu, Qinru Qiu, and Jian Tang
arXiv Preprint arXiv:2401.08957, 2024
[Paper on arXiv]
EPSD: Early Pruning with Self-Distillation for Efficient Model Compression
Dong Chen*, Ning Liu*, Yichen Zhu, Zhengping Che, Rui Ma, Fachao Zhang, Xiaofeng Mou, Yi Chang, and Jian Tang (*equal contributions)
Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI), 2024
[Paper on AAAI DL] [Paper on arXiv]
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), 35(8):11595-11607, 2024
[Paper on IEEE DL] [Paper on arXiv]
2023
Multi-Clue Reasoning with Memory Augmentation for Knowledge-based Visual Question Answering
Chengxiang Yin, Zhengping Che, Kun Wu, Zhiyuan Xu, and Jian Tang
arXiv Preprint arXiv:2312.12723, 2023
[Paper on arXiv]
Cross-Modal Reasoning with Event Correlation for Video Question Answering
Chengxiang Yin, Zhengping Che, Kun Wu, Zhiyuan Xu, Qinru Qiu, and Jian Tang
arXiv Preprint arXiv:2312.12721, 2023
[Paper on arXiv]
Human Pose Transfer with Augmented Disentangled Feature Consistency
Kun Wu, Chengxiang Yin, Zhengping Che, Bo Jiang, Jian Tang, Zheng Guan, and Gangyi Ding
ACM Transactions on Intelligent Systems and Technology (TIST), 15(1):1-22, 2023
[Paper on ACM DL] [Paper on arXiv]
DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field
Haowen Wang*, Zhipeng Fan*, Zhen Zhao, Zhengping Che, Zhiyuan Xu, Dong Liu, Feifei Feng, Yakun Huang, Xiuquan Qiao, and Jian Tang (*equal contributions)
Proceedings of the 31st ACM International Conference on Multimedia (ACM MM), 2023
[Paper on ACM DL] [Paper on arXiv]
Distributional Generative Adversarial Imitation Learning with Reproducing Kernel Generalization
Yirui Zhou, Mengxiao Lu, Xiaowei Liu, Zhengping Che, Zhiyuan Xu, Jian Tang, Yangchun hang, Yan Peng, and Yaxin Peng
Neural Networks (NEUNET), 165:43-59, 2023
[Paper on ScienceDirect]
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)
Proceedings of the 36th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
[Paper on CVF] [Paper on arXiv] [Code on GitHub]
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)
Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023
[Paper on IEEE DL] [Paper on arXiv] [Code on GitHub] [Video Online]
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), 25:5720-5732, 2023
[Paper on IEEE DL]
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), 34(12):9847-9858, 2023
[Paper on IEEE DL] [Paper on arXiv]
2022
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]
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]
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]
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] [Code on GitHub]
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]
2021
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]
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] [Paper on IEEE DL]
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]
2020
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]
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]
2019
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]
2018
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]
2017
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]
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]
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]
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]
2016
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]
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]
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)]
2015
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]
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]
2014
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]
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]
Last Updated: Dec 2024