GeoMFormer: A General Architecture for Geometric Molecular Representation Learning
Tianlang Chen, Shengjie Luo, Di He, Shuxin Zheng, Tie-Yan Liu, Liwei Wang
ICML 2024 | June 2024
Shuxin Zheng
Principal Researcher
Publications
Publications by Year
-
Inverse Design of Vitrimeric Polymers by Molecular Dynamics and Generative Modeling
Yiwen Zheng, Prakash Thakolkaran, Jake Smith, Ziheng Lu, Shuxin Zheng, Bichlien Nguyen, Siddhant Kumar, Aniruddh Vashisth
March 2024
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
-
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
-
Quantized Training of Gradient Boosting Decision Trees
Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu
2022 Neural Information Processing Systems | November 2022
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
Quantized Training of Gradient Boosted Decision Trees
Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu
2022 Neural Information Processing Systems | June 2022
Your Transformer May Not be as Powerful as You Expect
Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He
2022 Neural Information Processing Systems | May 2022
-
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
Do Transformers Really Perform Badly for Graph Representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu
NeurIPS 2021 | December 2021
Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding
Shengjie Luo, Shanda Li, Tianle Cai, Di He, Dinglan Peng, Shuxin Zheng, Guolin Ke, Liwei Wang, Tie-Yan Liu
NeurIPS 2021 | December 2021
How could Neural Networks understand Programs
Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu
2021 International Conference on Machine Learning | July 2021
Cross-Iteration Batch Normalization
Zhuliang Yao, Yue Cao, Shuxin Zheng, Gao Huang, Stephen Lin
CVPR 2021 | June 2021
-
Invertible Image Rescaling
Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang, Di He, Guolin Ke, Jiang Bian, Zhouchen Lin, Tie-Yan Liu
16th European Conference Computer Vision (ECCV 2020) | August 2020
On Layer Normalization in the Transformer Architecture
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, Tie-Yan Liu
ICML 2020 | July 2020
Deep learning for prediction of the air quality response to emission changes
Jia Xing, Shuxin Zheng, Dian Ding, James T Kelly, Shuxiao Wang, Siwei Li, Tao Qin, Mingyuan Ma, Zhaoxin Dong, Carey J. Jang, Yun Zhu, Haotian Zheng, Lu Ren, Tie-Yan Liu, Jiming Hao
Environmental Science & Technology | June 2020, Vol 54(14): pp. 8589-8600
MC-BERT: Efficient Language Pre-Training via a Meta Controller
Zhenhui Xu, Linyuan Gong, Guolin Ke, Di He, Shuxin Zheng, Liwei Wang, Jiang Bian, Tie-Yan Liu
June 2020
-
G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space.
Qi Meng, Shuxin Zheng, Huishuai Zhang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Nenghai Yu, Tie-Yan Liu
2018 International Conference on Learning Representations | September 2018
Publications by Research Area
-
GeoMFormer: A General Architecture for Geometric Molecular Representation Learning
Tianlang Chen, Shengjie Luo, Di He, Shuxin Zheng, Tie-Yan Liu, Liwei Wang
ICML 2024 | June 2024
Inverse Design of Vitrimeric Polymers by Molecular Dynamics and Generative Modeling
Yiwen Zheng, Prakash Thakolkaran, Jake Smith, Ziheng Lu, Shuxin Zheng, Bichlien Nguyen, Siddhant Kumar, Aniruddh Vashisth
March 2024
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
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
Quantized Training of Gradient Boosting Decision Trees
Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu
2022 Neural Information Processing Systems | November 2022
Quantized Training of Gradient Boosted Decision Trees
Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu
2022 Neural Information Processing Systems | June 2022
Your Transformer May Not be as Powerful as You Expect
Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He
2022 Neural Information Processing Systems | May 2022
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
Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding
Shengjie Luo, Shanda Li, Tianle Cai, Di He, Dinglan Peng, Shuxin Zheng, Guolin Ke, Liwei Wang, Tie-Yan Liu
NeurIPS 2021 | December 2021
Do Transformers Really Perform Badly for Graph Representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu
NeurIPS 2021 | December 2021
How could Neural Networks understand Programs
Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu
2021 International Conference on Machine Learning | July 2021
Cross-Iteration Batch Normalization
Zhuliang Yao, Yue Cao, Shuxin Zheng, Gao Huang, Stephen Lin
CVPR 2021 | June 2021
On Layer Normalization in the Transformer Architecture
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, Tie-Yan Liu
ICML 2020 | July 2020
Deep learning for prediction of the air quality response to emission changes
Jia Xing, Shuxin Zheng, Dian Ding, James T Kelly, Shuxiao Wang, Siwei Li, Tao Qin, Mingyuan Ma, Zhaoxin Dong, Carey J. Jang, Yun Zhu, Haotian Zheng, Lu Ren, Tie-Yan Liu, Jiming Hao
Environmental Science & Technology | June 2020, Vol 54(14): pp. 8589-8600
MC-BERT: Efficient Language Pre-Training via a Meta Controller
Zhenhui Xu, Linyuan Gong, Guolin Ke, Di He, Shuxin Zheng, Liwei Wang, Jiang Bian, Tie-Yan Liu
June 2020
G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space.
Qi Meng, Shuxin Zheng, Huishuai Zhang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Nenghai Yu, Tie-Yan Liu
2018 International Conference on Learning Representations | September 2018
-
Inverse Design of Vitrimeric Polymers by Molecular Dynamics and Generative Modeling
Yiwen Zheng, Prakash Thakolkaran, Jake Smith, Ziheng Lu, Shuxin Zheng, Bichlien Nguyen, Siddhant Kumar, Aniruddh Vashisth
March 2024
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
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
-
Deep learning for prediction of the air quality response to emission changes
Jia Xing, Shuxin Zheng, Dian Ding, James T Kelly, Shuxiao Wang, Siwei Li, Tao Qin, Mingyuan Ma, Zhaoxin Dong, Carey J. Jang, Yun Zhu, Haotian Zheng, Lu Ren, Tie-Yan Liu, Jiming Hao
Environmental Science & Technology | June 2020, Vol 54(14): pp. 8589-8600
-
How could Neural Networks understand Programs
Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu
2021 International Conference on Machine Learning | July 2021
-
How could Neural Networks understand Programs
Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu
2021 International Conference on Machine Learning | July 2021
-
Cross-Iteration Batch Normalization
Zhuliang Yao, Yue Cao, Shuxin Zheng, Gao Huang, Stephen Lin
CVPR 2021 | June 2021
Invertible Image Rescaling
Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang, Di He, Guolin Ke, Jiang Bian, Zhouchen Lin, Tie-Yan Liu
16th European Conference Computer Vision (ECCV 2020) | August 2020
Publications by Type
-
GeoMFormer: A General Architecture for Geometric Molecular Representation Learning
Tianlang Chen, Shengjie Luo, Di He, Shuxin Zheng, Tie-Yan Liu, Liwei Wang
ICML 2024 | June 2024
Quantized Training of Gradient Boosting Decision Trees
Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu
2022 Neural Information Processing Systems | November 2022
Quantized Training of Gradient Boosted Decision Trees
Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu
2022 Neural Information Processing Systems | June 2022
Your Transformer May Not be as Powerful as You Expect
Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He
2022 Neural Information Processing Systems | May 2022
Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding
Shengjie Luo, Shanda Li, Tianle Cai, Di He, Dinglan Peng, Shuxin Zheng, Guolin Ke, Liwei Wang, Tie-Yan Liu
NeurIPS 2021 | December 2021
Do Transformers Really Perform Badly for Graph Representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu
NeurIPS 2021 | December 2021
How could Neural Networks understand Programs
Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu
2021 International Conference on Machine Learning | July 2021
Cross-Iteration Batch Normalization
Zhuliang Yao, Yue Cao, Shuxin Zheng, Gao Huang, Stephen Lin
CVPR 2021 | June 2021
Invertible Image Rescaling
Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang, Di He, Guolin Ke, Jiang Bian, Zhouchen Lin, Tie-Yan Liu
16th European Conference Computer Vision (ECCV 2020) | August 2020
On Layer Normalization in the Transformer Architecture
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, Tie-Yan Liu
ICML 2020 | July 2020
G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space.
Qi Meng, Shuxin Zheng, Huishuai Zhang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Nenghai Yu, Tie-Yan Liu
2018 International Conference on Learning Representations | September 2018
-
Inverse Design of Vitrimeric Polymers by Molecular Dynamics and Generative Modeling
Yiwen Zheng, Prakash Thakolkaran, Jake Smith, Ziheng Lu, Shuxin Zheng, Bichlien Nguyen, Siddhant Kumar, Aniruddh Vashisth
March 2024
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
-
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
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
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
Deep learning for prediction of the air quality response to emission changes
Jia Xing, Shuxin Zheng, Dian Ding, James T Kelly, Shuxiao Wang, Siwei Li, Tao Qin, Mingyuan Ma, Zhaoxin Dong, Carey J. Jang, Yun Zhu, Haotian Zheng, Lu Ren, Tie-Yan Liu, Jiming Hao
Environmental Science & Technology | June 2020, Vol 54(14): pp. 8589-8600
-
MC-BERT: Efficient Language Pre-Training via a Meta Controller
Zhenhui Xu, Linyuan Gong, Guolin Ke, Di He, Shuxin Zheng, Liwei Wang, Jiang Bian, Tie-Yan Liu
June 2020