Chang Liu

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 31

Top Topics

Diffusion Model

Variational Inference

Weakly Supervised

Imitation Learning

Top Venues

Publications

He Zhang, Siyuan Liu, Jiacheng You, Chang Liu, Shuxin Zheng, Ziheng Lu, Tong Wang, Nanning Zheng, Bin Shao
Overcoming the barrier of orbital-free density functional theory for molecular systems using deep learning. Nat. Comput. Sci. 4 (3) (2024)
Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu
Predicting equilibrium distributions for molecular systems with deep learning. Nat. Mac. Intell. 6 (5) (2024)
Peng Jin, Hao Li, Zesen Cheng, Kehan Li, Runyi Yu, Chang Liu, Xiangyang Ji, Li Yuan, Jie Chen
Local Action-Guided Motion Diffusion Model for Text-to-Motion Generation. CoRR (2024)
Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, Philip S. Yu
Generalizing to Unseen Domains: A Survey on Domain Generalization. IEEE Trans. Knowl. Data Eng. 35 (8) (2023)
Chang Liu, Yinpeng Dong, Wenzhao Xiang, Xiao Yang, Hang Su, Jun Zhu, Yuefeng Chen, Yuan He, Hui Xue, Shibao Zheng
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking. CoRR (2023)
Kehan Li, Yian Zhao, Zhennan Wang, Zesen Cheng, Peng Jin, Xiangyang Ji, Li Yuan, Chang Liu, Jie Chen
Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation. CoRR (2023)
Zesen Cheng, Kehan Li, Peng Jin, Xiangyang Ji, Li Yuan, Chang Liu, Jie Chen
Parallel Vertex Diffusion for Unified Visual Grounding. CoRR (2023)
Wenzhao Xiang, Hang Su, Chang Liu, Yandong Guo, Shibao Zheng
Improving the robustness of adversarial attacks using an affine-invariant gradient estimator. Comput. Vis. Image Underst. 229 (2023)
Qiran Zou, Yu Yang, Wing Yin Cheung, Chang Liu, Xiangyang Ji
ILSGAN: Independent Layer Synthesis for Unsupervised Foreground-Background Segmentation. AAAI (2023)
Peng Jin, Jinfa Huang, Pengfei Xiong, Shangxuan Tian, Chang Liu, Xiangyang Ji, Li Yuan, Jie Chen
Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning. CVPR (2023)
Chang Liu, Xu Tan, Chongyang Tao, Zhenxin Fu, Dongyan Zhao, Tie-Yan Liu, Rui Yan
ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation. ACL (1) (2022)
Yu Yang, Xiaotian Cheng, Chang Liu, Hakan Bilen, Xiangyang Ji
Distilling Representations from GAN Generator via Squeeze and Span. NeurIPS (2022)
Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen, Jiacheng You, Jiyan He, Shengjie Luo, Chang Liu, Di He, Tie-Yan Liu
Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets. CoRR (2022)
Jinhua Zhu, Yingce Xia, Chang Liu, Lijun Wu, Shufang Xie, Yusong Wang, Tong Wang, Tao Qin, Wengang Zhou, Houqiang Li, Haiguang Liu, Tie-Yan Liu
Direct Molecular Conformation Generation. Trans. Mach. Learn. Res. 2022 (2022)
Qiran Zou, Yu Yang, Wing Yin Cheung, Chang Liu, Xiangyang Ji
ILSGAN: Independent Layer Synthesis for Unsupervised Foreground-Background Segmentation. CoRR (2022)
Mingqing Xiao, Shuxin Zheng, Chang Liu, Zhouchen Lin, Tie-Yan Liu
Invertible Rescaling Network and Its Extensions. CoRR (2022)
Zesen Cheng, Pengchong Qiao, Kehan Li, Siheng Li, Pengxu Wei, Xiangyang Ji, Li Yuan, Chang Liu, Jie Chen
Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation. CoRR (2022)
Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu
PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior. ICLR (2022)
Xinwei Sun, Botong Wu, Xiangyu Zheng, Chang Liu, Wei Chen, Tao Qin, Tie-Yan Liu
Recovering Latent Causal Factor for Generalization to Distributional Shifts. NeurIPS (2021)
Chang Liu, Haoyue Tang, Tao Qin, Jintao Wang, Tie-Yan Liu
On the Generative Utility of Cyclic Conditionals. NeurIPS (2021)
Chang Liu, Xinwei Sun, Jindong Wang, Haoyue Tang, Tao Li, Tao Qin, Wei Chen, Tie-Yan Liu
Learning Causal Semantic Representation for Out-of-Distribution Prediction. NeurIPS (2021)
Jongjin Park, Younggyo Seo, Chang Liu, Li Zhao, Tao Qin, Jinwoo Shin, Tie-Yan Liu
Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning. NeurIPS (2021)
Chenyang Tao, Shuyang Dai, Liqun Chen, Ke Bai, Junya Chen, Chang Liu, Ruiyi Zhang, Georgiy V. Bobashev, Lawrence Carin
Variational Annealing of GANs: A Langevin Perspective. ICML (2019)
Chang Liu, Jingwei Zhuo, Jun Zhu
Understanding MCMC Dynamics as Flows on the Wasserstein Space. ICML (2019)
Pengyu Cheng, Chang Liu, Chunyuan Li, Dinghan Shen, Ricardo Henao, Lawrence Carin
Straight-Through Estimator as Projected Wasserstein Gradient Flow. CoRR (2019)
Chang Liu, Jingwei Zhuo, Jun Zhu
Understanding MCMC Dynamics as Flows on the Wasserstein Space. CoRR (2019)
Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu
Understanding and Accelerating Particle-Based Variational Inference. ICML (2019)
Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu, Lawrence Carin
Accelerated First-order Methods on the Wasserstein Space for Bayesian Inference. CoRR (2018)
Chang Liu, Jun Zhu
Riemannian Stein Variational Gradient Descent for Bayesian Inference. AAAI (2018)
Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, Bo Zhang
Message Passing Stein Variational Gradient Descent. ICML (2018)
Chang Liu, Jun Zhu, Yang Song
Stochastic Gradient Geodesic MCMC Methods. NIPS (2016)