Chang Liu

Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning

Yuxuan Ren, Dihan Zheng, Chang Liu, Peiran Jin, Yu Shi, Lin Huang, Jiyan He, Shengjie Luo, Tao Qin, Tie-Yan Liu

2024 Neural Information Processing Systems | October 2024

Publication Preprint

Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models

Zun Wang, Chang Liu, Nianlong Zou, He Zhang, Xinran wei, Lin Huang, Lijun Wu, Bin Shao

NeurIPS 2024 | June 2024

DOI Publication Publication

MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

Han Yang, Chenxi Hu, Yichi Zhou, Xixian Liu, Yu Shi, Jielan Li, Guanzhi Li, Zekun Chen, Shuizhou Chen, Claudio Zeni, Matthew Horton, Robert Pinsler, Andrew Fowler, Daniel Zügner, Tianyidan Xie, Jake Smith, Lixin Sun, Qian Wang, Lingyu Kong, Chang Liu, Hongxia Hao, Ziheng Lu

May 2024

Publication

Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction

He Zhang, Chang Liu, Zun Wang, Xinran wei, Siyuan Liu, Nanning Zheng, Bin Shao, Tie-Yan Liu

ICML 2024 | March 2024

DOI Publication Publication

Overcoming the barrier of orbital-free density functional theory for molecular systems using deep learning

He Zhang, Siyuan Liu, Jiacheng You, Chang Liu, Shuxin Zheng, Ziheng Lu, Tong Wang, Nanning Zheng, Bin Shao

Nature Computational Science | March 2024

DOI Publication PDF Preprint

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning

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

June 2023

Publication Vidéo

Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational Autoencoder

Jia Xing, Siwei Li, Shuxin Zheng, Chang Liu, Xiaochun Wang, Lin Huang, Ge Song, Yihan He, Shuxiao Wang, Shovan Kumar Sahu, Jia Zhang, Jiang Bian, Yun Zhu, Tie-Yan Liu, Jiming Hao

Environmental Science & Technology | July 2022, Vol 56(14): pp. 9903-9914

Publication DOI

PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior

Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu

ICLR 2022 | April 2022

Publication

Sampling with Mirrored Stein Operators

Jiaxin Shi, Chang Liu, Lester Mackey

ICLR 2022 | April 2022

Publication

Mimicking atmospheric photochemical modeling with a deep neural network

Jia Xing, Shuxin Zheng, Siwei Li, Lin Huang, Xiaochun Wang, James T Kelly, Shuxiao Wang, Chang Liu, Carey J. Jang, Yun Zhu, Jia Zhang, Jiang Bian, Tie-Yan Liu, Jiming Hao

Atmospheric Research | December 2021, Vol 265: pp. 105919

DOI Publication

Learning Causal Semantic Representation for Out-of-Distribution Prediction

Chang Liu, Xinwei Sun, Jindong Wang, Haoyue Tang, Tao Li, Tao Qin, Wei Chen, Tie-Yan Liu

NeurIPS 2021 | December 2021

Publication

Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning

Jongjin Park, Younggyo Seo, Chang Liu, Li Zhao, Tao Qin, Jinwoo Shin, Tie-Yan Liu

Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS) | December 2021

Publication Projet

Recovering Latent Causal Factor for Generalization to Distributional Shifts

Xinwei Sun, Botong Wu, Xiangyu Zheng, Chang Liu, Wei Chen, Tao Qin, Tie-Yan Liu

NeurIPS 2021 | December 2021

Publication

Towards Generating Real-World Time Series Data

Hengzhi Pei, Kan Ren, Yuqing Yang, Chang Liu, Tao Qin, Dongsheng Li

ICDM 21 | November 2021

Publication Publication Publication

On the Generative Utility of Cyclic Conditionals

Chang Liu, Haoyue Tang, Tao Qin, Jintao Wang, Tie-Yan Liu

NeurIPS 2021 | June 2021

Publication

Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning

Yuxuan Ren, Dihan Zheng, Chang Liu, Peiran Jin, Yu Shi, Lin Huang, Jiyan He, Shengjie Luo, Tao Qin, Tie-Yan Liu

2024 Neural Information Processing Systems | October 2024

Publication Preprint

Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models

Zun Wang, Chang Liu, Nianlong Zou, He Zhang, Xinran wei, Lin Huang, Lijun Wu, Bin Shao

NeurIPS 2024 | June 2024

DOI Publication Publication

MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

Han Yang, Chenxi Hu, Yichi Zhou, Xixian Liu, Yu Shi, Jielan Li, Guanzhi Li, Zekun Chen, Shuizhou Chen, Claudio Zeni, Matthew Horton, Robert Pinsler, Andrew Fowler, Daniel Zügner, Tianyidan Xie, Jake Smith, Lixin Sun, Qian Wang, Lingyu Kong, Chang Liu, Hongxia Hao, Ziheng Lu

May 2024

Publication

Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction

He Zhang, Chang Liu, Zun Wang, Xinran wei, Siyuan Liu, Nanning Zheng, Bin Shao, Tie-Yan Liu

ICML 2024 | March 2024

DOI Publication Publication

Overcoming the barrier of orbital-free density functional theory for molecular systems using deep learning

He Zhang, Siyuan Liu, Jiacheng You, Chang Liu, Shuxin Zheng, Ziheng Lu, Tong Wang, Nanning Zheng, Bin Shao

Nature Computational Science | March 2024

DOI Publication PDF Preprint

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning

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

June 2023

Publication Vidéo

PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior

Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu

ICLR 2022 | April 2022

Publication

Sampling with Mirrored Stein Operators

Jiaxin Shi, Chang Liu, Lester Mackey

ICLR 2022 | April 2022

Publication

Mimicking atmospheric photochemical modeling with a deep neural network

Jia Xing, Shuxin Zheng, Siwei Li, Lin Huang, Xiaochun Wang, James T Kelly, Shuxiao Wang, Chang Liu, Carey J. Jang, Yun Zhu, Jia Zhang, Jiang Bian, Tie-Yan Liu, Jiming Hao

Atmospheric Research | December 2021, Vol 265: pp. 105919

DOI Publication

Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning

Jongjin Park, Younggyo Seo, Chang Liu, Li Zhao, Tao Qin, Jinwoo Shin, Tie-Yan Liu

Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS) | December 2021

Publication Projet

Learning Causal Semantic Representation for Out-of-Distribution Prediction

Chang Liu, Xinwei Sun, Jindong Wang, Haoyue Tang, Tao Li, Tao Qin, Wei Chen, Tie-Yan Liu

NeurIPS 2021 | December 2021

Publication

Recovering Latent Causal Factor for Generalization to Distributional Shifts

Xinwei Sun, Botong Wu, Xiangyu Zheng, Chang Liu, Wei Chen, Tao Qin, Tie-Yan Liu

NeurIPS 2021 | December 2021

Publication

On the Generative Utility of Cyclic Conditionals

Chang Liu, Haoyue Tang, Tao Qin, Jintao Wang, Tie-Yan Liu

NeurIPS 2021 | June 2021

Publication

Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational Autoencoder

Jia Xing, Siwei Li, Shuxin Zheng, Chang Liu, Xiaochun Wang, Lin Huang, Ge Song, Yihan He, Shuxiao Wang, Shovan Kumar Sahu, Jia Zhang, Jiang Bian, Yun Zhu, Tie-Yan Liu, Jiming Hao

Environmental Science & Technology | July 2022, Vol 56(14): pp. 9903-9914

Publication DOI

Mimicking atmospheric photochemical modeling with a deep neural network

Jia Xing, Shuxin Zheng, Siwei Li, Lin Huang, Xiaochun Wang, James T Kelly, Shuxiao Wang, Chang Liu, Carey J. Jang, Yun Zhu, Jia Zhang, Jiang Bian, Tie-Yan Liu, Jiming Hao

Atmospheric Research | December 2021, Vol 265: pp. 105919

DOI Publication

Towards Generating Real-World Time Series Data

Hengzhi Pei, Kan Ren, Yuqing Yang, Chang Liu, Tao Qin, Dongsheng Li

ICDM 21 | November 2021

Publication Publication Publication

Towards Generating Real-World Time Series Data

Hengzhi Pei, Kan Ren, Yuqing Yang, Chang Liu, Tao Qin, Dongsheng Li

ICDM 21 | November 2021

Publication Publication Publication

On the Generative Utility of Cyclic Conditionals

Chang Liu, Haoyue Tang, Tao Qin, Jintao Wang, Tie-Yan Liu

NeurIPS 2021 | June 2021

Publication

Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning

Yuxuan Ren, Dihan Zheng, Chang Liu, Peiran Jin, Yu Shi, Lin Huang, Jiyan He, Shengjie Luo, Tao Qin, Tie-Yan Liu

2024 Neural Information Processing Systems | October 2024

Publication Preprint

Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models

Zun Wang, Chang Liu, Nianlong Zou, He Zhang, Xinran wei, Lin Huang, Lijun Wu, Bin Shao

NeurIPS 2024 | June 2024

DOI Publication Publication

Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction

He Zhang, Chang Liu, Zun Wang, Xinran wei, Siyuan Liu, Nanning Zheng, Bin Shao, Tie-Yan Liu

ICML 2024 | March 2024

DOI Publication Publication

PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior

Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu

ICLR 2022 | April 2022

Publication

Sampling with Mirrored Stein Operators

Jiaxin Shi, Chang Liu, Lester Mackey

ICLR 2022 | April 2022

Publication

Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning

Jongjin Park, Younggyo Seo, Chang Liu, Li Zhao, Tao Qin, Jinwoo Shin, Tie-Yan Liu

Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS) | December 2021

Publication Projet

Learning Causal Semantic Representation for Out-of-Distribution Prediction

Chang Liu, Xinwei Sun, Jindong Wang, Haoyue Tang, Tao Li, Tao Qin, Wei Chen, Tie-Yan Liu

NeurIPS 2021 | December 2021

Publication

Recovering Latent Causal Factor for Generalization to Distributional Shifts

Xinwei Sun, Botong Wu, Xiangyu Zheng, Chang Liu, Wei Chen, Tao Qin, Tie-Yan Liu

NeurIPS 2021 | December 2021

Publication

Towards Generating Real-World Time Series Data

Hengzhi Pei, Kan Ren, Yuqing Yang, Chang Liu, Tao Qin, Dongsheng Li

ICDM 21 | November 2021

Publication Publication Publication

On the Generative Utility of Cyclic Conditionals

Chang Liu, Haoyue Tang, Tao Qin, Jintao Wang, Tie-Yan Liu

NeurIPS 2021 | June 2021

Publication

MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

Han Yang, Chenxi Hu, Yichi Zhou, Xixian Liu, Yu Shi, Jielan Li, Guanzhi Li, Zekun Chen, Shuizhou Chen, Claudio Zeni, Matthew Horton, Robert Pinsler, Andrew Fowler, Daniel Zügner, Tianyidan Xie, Jake Smith, Lixin Sun, Qian Wang, Lingyu Kong, Chang Liu, Hongxia Hao, Ziheng Lu

May 2024

Publication

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning

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

June 2023

Publication Vidéo

Overcoming the barrier of orbital-free density functional theory for molecular systems using deep learning

He Zhang, Siyuan Liu, Jiacheng You, Chang Liu, Shuxin Zheng, Ziheng Lu, Tong Wang, Nanning Zheng, Bin Shao

Nature Computational Science | March 2024

DOI Publication PDF Preprint

Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational Autoencoder

Jia Xing, Siwei Li, Shuxin Zheng, Chang Liu, Xiaochun Wang, Lin Huang, Ge Song, Yihan He, Shuxiao Wang, Shovan Kumar Sahu, Jia Zhang, Jiang Bian, Yun Zhu, Tie-Yan Liu, Jiming Hao

Environmental Science & Technology | July 2022, Vol 56(14): pp. 9903-9914

Publication DOI

Mimicking atmospheric photochemical modeling with a deep neural network

Jia Xing, Shuxin Zheng, Siwei Li, Lin Huang, Xiaochun Wang, James T Kelly, Shuxiao Wang, Chang Liu, Carey J. Jang, Yun Zhu, Jia Zhang, Jiang Bian, Tie-Yan Liu, Jiming Hao

Atmospheric Research | December 2021, Vol 265: pp. 105919

DOI Publication