Weinan E

ORCID

Publication Activity (10 Years)

Years Active: 2000-2024
Publications (10 Years): 103

Top Venues

CoRR

MSML

Comput. Phys. Commun.

J. Mach. Learn. Res.

Publications

Pinchen Xie, Yunrui Qiu, Weinan E
Coarse-graining conformational dynamics with multi-dimensional generalized Langevin equation: how, when, and why. CoRR (2024)
Xiaohong Ji, Zhen Wang, Zhifeng Gao, Hang Zheng, Linfeng Zhang, Guolin Ke, Weinan E
Uni-Mol2: Exploring Molecular Pretraining Model at Scale. CoRR (2024)
Mingze Wang, Weinan E
Understanding the Expressive Power and Mechanisms of Transformer for Sequence Modeling. CoRR (2024)
Boshen Zeng, Sian Chen, Xinxin Liu, Changhong Chen, Bin Deng, Xiaoxu Wang, Zhifeng Gao, Yuzhi Zhang, Weinan E, Linfeng Zhang
Uni-ELF: A Multi-Level Representation Learning Framework for Electrolyte Formulation Design. CoRR (2024)
Zhongwang Zhang, Zhiwei Wang, Junjie Yao, Zhangchen Zhou, Xiaolong Li, Weinan E, Zhi-Qin John Xu
Anchor function: a type of benchmark functions for studying language models. CoRR (2024)
Mingze Wang, Haotian He, Jinbo Wang, Zilin Wang, Guanhua Huang, Feiyu Xiong, Zhiyu Li, Weinan E, Lei Wu
Improving Generalization and Convergence by Enhancing Implicit Regularization. CoRR (2024)
Zhi-Qin John Xu, Junjie Yao, Yuxiao Yi, Liangkai Hang, Weinan E, Yaoyu Zhang, Tianhan Zhang
Solving multiscale dynamical systems by deep learning. CoRR (2024)
Hongkang Yang, Zehao Lin, Wenjin Wang, Hao Wu, Zhiyu Li, Bo Tang, Wenqiang Wei, Jinbo Wang, Zeyun Tang, Shichao Song, Chenyang Xi, Yu Yu, Kai Chen, Feiyu Xiong, Linpeng Tang, Weinan E
: Language Modeling with Explicit Memory. CoRR (2024)
Zeping Min, Qian Ge, Zhong Li, Weinan E
AMP: A unified framework boosting low resource automatic speech recognition. CoRR (2023)
Guanhua Huang, Runxin Xu, Ying Zeng, Jiaze Chen, Zhouwang Yang, Weinan E
An Iteratively Parallel Generation Method with the Pre-Filling Strategy for Document-level Event Extraction. EMNLP (2023)
Wei Hu, Yue Zhao, Weinan E, Jiequn Han, Jihao Long
Learning Free Terminal Time Optimal Closed-loop Control of Manipulators. CoRR (2023)
Yixiao Chen, Linfeng Zhang, Han Wang, Weinan E
DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models. Comput. Phys. Commun. 282 (2023)
Jun Zhang, Xiaohan Lin, Weinan E, Yi Qin Gao
Machine-Learned Invertible Coarse Graining for Multiscale Molecular Modeling. CoRR (2023)
Jingrun Chen, Weinan E, Yixin Luo
The Random Feature Method for Time-dependent Problems. CoRR (2023)
Zhiwei Wang, Yaoyu Zhang, Yiguang Ju, Weinan E, Zhi-Qin John Xu, Tianhan Zhang
A deep learning-based model reduction (DeePMR) method for simplifying chemical kinetics. CoRR (2022)
Tianhan Zhang, Yuxiao Yi, Yifan Xu, Zhi X. Chen, Yaoyu Zhang, Weinan E, Zhi-Qin John Xu
A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kinetics. CoRR (2022)
Jingrun Chen, Xurong Chi, Weinan E, Zhouwang Yang
Bridging Traditional and Machine Learning-based Algorithms for Solving PDEs: The Random Feature Method. CoRR (2022)
Zhong Li, Jiequn Han, Weinan E, Qianxiao Li
Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks. J. Mach. Learn. Res. 23 (2022)
Yaohua Zang, Jihao Long, Xuanxi Zhang, Wei Hu, Weinan E, Jiequn Han
A Machine Learning Enhanced Algorithm for the Optimal Landing Problem. MSML (2022)
Weinan E, Yajun Zhou
A Mathematical Model for Universal Semantics. IEEE Trans. Pattern Anal. Mach. Intell. 44 (3) (2022)
Dongdong Wang, Yanze Wang, Junhan Chang, Linfeng Zhang, Han Wang, Weinan E
Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics. Nat. Comput. Sci. 2 (1) (2022)
Weinan E, Stephan Wojtowytsch
Some observations on high-dimensional partial differential equations with Barron data. MSML (2021)
Hongkang Yang, Weinan E
Generalization and Memorization: The Bias Potential Model. MSML (2021)
Zhong Li, Jiequn Han, Weinan E, Qianxiao Li
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis. ICLR (2021)
Chao Ma, Lei Wu, Weinan E
A Qualitative Study of the Dynamic Behavior for Adaptive Gradient Algorithms. MSML (2021)
Lidong Fang, Pei Ge, Lei Zhang, Huan Lei, Weinan E
: A deep learning-based non-Newtonian hydrodynamic model. CoRR (2021)
Jihao Long, Jiequn Han, Weinan E
Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation. CoRR (2021)
Weinan E, Stephan Wojtowytsch
On the emergence of simplex symmetry in the final and penultimate layers of neural network classifiers. MSML (2021)
Jiequn Han, Yucheng Yang, Weinan E
DeepHAM: A Global Solution Method for Heterogeneous Agent Models with Aggregate Shocks. CoRR (2021)
Hongkang Yang, Weinan E
Generalization Error of GAN from the Discriminator's Perspective. CoRR (2021)
Denghui Lu, Han Wang, Mohan Chen, Lin Lin, Roberto Car, Weinan E, Weile Jia, Linfeng Zhang
accuracy. Comput. Phys. Commun. 259 (2021)
Hongkang Yang, Weinan E
Generalization and Memorization: The Bias Potential Model. CoRR (2020)
Zhong Li, Jiequn Han, Weinan E, Qianxiao Li
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis. CoRR (2020)
Weinan E, Stephan Wojtowytsch
A priori estimates for classification problems using neural networks. CoRR (2020)
Yucheng Yang, Yue Pang, Guanhua Huang, Weinan E
The Knowledge Graph for Macroeconomic Analysis with Alternative Big Data. CoRR (2020)
Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven Chu-Hong Hoi, Weinan E
Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning. NeurIPS (2020)
Yixiao Chen, Linfeng Zhang, Han Wang, Weinan E
DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory. CoRR (2020)
Weinan E, Stephan Wojtowytsch
Kolmogorov Width Decay and Poor Approximators in Machine Learning: Shallow Neural Networks, Random Feature Models and Neural Tangent Kernels. CoRR (2020)
Yucheng Yang, Zhong Zheng, Weinan E
Interpretable Neural Networks for Panel Data Analysis in Economics. CoRR (2020)
Stephan Wojtowytsch, Weinan E
Can Shallow Neural Networks Beat the Curse of Dimensionality? A Mean Field Training Perspective. IEEE Trans. Artif. Intell. 1 (2) (2020)
Stephan Wojtowytsch, Weinan E
Can Shallow Neural Networks Beat the Curse of Dimensionality? A mean field training perspective. CoRR (2020)
Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang, Weinan E
DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models. Comput. Phys. Commun. 253 (2020)
Weinan E, Chao Ma, Stephan Wojtowytsch, Lei Wu
Towards a Mathematical Understanding of Neural Network-Based Machine Learning: what we know and what we don't. CoRR (2020)
Huan Lei, Lei Wu, Weinan E
Machine learning based non-Newtonian fluid model with molecular fidelity. CoRR (2020)
Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven C. H. Hoi, Weinan E
Towards Theoretically Understanding Why SGD Generalizes Better Than ADAM in Deep Learning. CoRR (2020)
Weinan E
Machine Learning and Computational Mathematics. CoRR (2020)
Chao Ma, Lei Wu, Weinan E
The Quenching-Activation Behavior of the Gradient Descent Dynamics for Two-layer Neural Network Models. CoRR (2020)
Weinan E, Stephan Wojtowytsch
Representation formulas and pointwise properties for Barron functions. CoRR (2020)
Denghui Lu, Han Wang, Mohan Chen, Jiduan Liu, Lin Lin, Roberto Car, Weinan E, Weile Jia, Linfeng Zhang
86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy. CoRR (2020)
Weinan E, Stephan Wojtowytsch
Some observations on partial differential equations in Barron and multi-layer spaces. CoRR (2020)
Weinan E, Jiequn Han, Arnulf Jentzen
Algorithms for Solving High Dimensional PDEs: From Nonlinear Monte Carlo to Machine Learning. CoRR (2020)
Chao Ma, Lei Wu, Weinan E
The Slow Deterioration of the Generalization Error of the Random Feature Model. CoRR (2020)
Weinan E, Jiequn Han, Linfeng Zhang
Integrating Machine Learning with Physics-Based Modeling. CoRR (2020)
Weinan E, Stephan Wojtowytsch
On the Banach spaces associated with multi-layer ReLU networks: Function representation, approximation theory and gradient descent dynamics. CoRR (2020)
Weinan E, Stephan Wojtowytsch
On the emergence of tetrahedral symmetry in the final and penultimate layers of neural network classifiers. CoRR (2020)
Pinchen Xie, Weinan E
Coarse-grained spectral projection (CGSP): A scalable and parallelizable deep learning-based approach to quantum unitary dynamics. CoRR (2020)
Tianhan Zhang, Yaoyu Zhang, Weinan E, Yiguang Ju
A deep learning-based ODE solver for chemical kinetics. CoRR (2020)
Haijun Yu, Xinyuan Tian, Weinan E, Qianxiao Li
OnsagerNet: Learning Stable and Interpretable Dynamics using a Generalized Onsager Principle. CoRR (2020)
Chao Ma, Lei Wu, Weinan E
A Qualitative Study of the Dynamic Behavior of Adaptive Gradient Algorithms. CoRR (2020)
Weile Jia, Han Wang, Mohan Chen, Denghui Lu, Lin Lin, Roberto Car, Weinan E, Linfeng Zhang
accuracy to 100 million atoms with machine learning. SC (2020)
Chao Ma, Lei Wu, Weinan E
The Slow Deterioration of the Generalization Error of the Random Feature Model. MSML (2020)
Zehao Don, Weinan E, Chao Ma
A Priori Estimates of the Generalization Error for Autoencoders. ICASSP (2020)
Weinan E, Yajun Zhou
A Mathematical Model for Linguistic Universals. CoRR (2019)
Qianxiao Li, Cheng Tai, Weinan E
Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations. J. Mach. Learn. Res. 20 (2019)
Weinan E, Chao Ma, Lei Wu
Barron Spaces and the Compositional Function Spaces for Neural Network Models. CoRR (2019)
Jiequn Han, Linfeng Zhang, Weinan E
Solving many-electron Schrödinger equation using deep neural networks. J. Comput. Phys. 399 (2019)
Weinan E, Chao Ma, Lei Wu
On the Generalization Properties of Minimum-norm Solutions for Over-parameterized Neural Network Models. CoRR (2019)
Weinan E, Chao Ma, Lei Wu
Machine Learning from a Continuous Viewpoint. CoRR (2019)
Weinan E, Chao Ma, Qingcan Wang, Lei Wu
Analysis of the Gradient Descent Algorithm for a Deep Neural Network Model with Skip-connections. CoRR (2019)
Weinan E, Chao Ma, Qingcan Wang
A Priori Estimates of the Population Risk for Residual Networks. CoRR (2019)
Weinan E, Martin Hutzenthaler, Arnulf Jentzen, Thomas Kruse
On Multilevel Picard Numerical Approximations for High-Dimensional Nonlinear Parabolic Partial Differential Equations and High-Dimensional Nonlinear Backward Stochastic Differential Equations. J. Sci. Comput. 79 (3) (2019)
Weinan E, Chao Ma, Lei Wu
A Comparative Analysis of the Optimization and Generalization Property of Two-layer Neural Network and Random Feature Models Under Gradient Descent Dynamics. CoRR (2019)
Christian Beck, Weinan E, Arnulf Jentzen
Machine Learning Approximation Algorithms for High-Dimensional Fully Nonlinear Partial Differential Equations and Second-order Backward Stochastic Differential Equations. J. Nonlinear Sci. 29 (4) (2019)
Qianxiao Li, Cheng Tai, Weinan E
Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations. CoRR (2018)
Han Wang, Linfeng Zhang, Jiequn Han, Weinan E
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. Comput. Phys. Commun. 228 (2018)
Weinan E, Jiequn Han, Qianxiao Li
A Mean-Field Optimal Control Formulation of Deep Learning. CoRR (2018)
Chao Ma, Jianchun Wang, Weinan E
Model Reduction with Memory and the Machine Learning of Dynamical Systems. CoRR (2018)
Weinan E, Qingcan Wang
Exponential Convergence of the Deep Neural Network Approximation for Analytic Functions. CoRR (2018)
Linfeng Zhang, De-Ye Lin, Han Wang, Roberto Car, Weinan E
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation. CoRR (2018)
Linfeng Zhang, Jiequn Han, Han Wang, Wissam Saidi, Roberto Car, Weinan E
End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems. NeurIPS (2018)
Lei Wu, Zhanxing Zhu, Cheng Tai, Weinan E
Understanding and Enhancing the Transferability of Adversarial Examples. CoRR (2018)
Lei Wu, Chao Ma, Weinan E
How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective. NeurIPS (2018)
Linfeng Zhang, Weinan E, Lei Wang
Monge-Ampère Flow for Generative Modeling. CoRR (2018)
Weinan E, Chao Ma, Lei Wu
A Priori Estimates of the Generalization Error for Two-layer Neural Networks. CoRR (2018)
Linfeng Zhang, Han Wang, Weinan E
Reinforced dynamics for enhanced sampling in large atomic and molecular systems. I. Basic Methodology. CoRR (2017)
Rui Yan, Dongyan Zhao, Weinan E
Joint Learning of Response Ranking and Next Utterance Suggestion in Human-Computer Conversation System. SIGIR (2017)
Qianxiao Li, Long Chen, Cheng Tai, Weinan E
Maximum Principle Based Algorithms for Deep Learning. CoRR (2017)
Jiequn Han, Arnulf Jentzen, Weinan E
Overcoming the curse of dimensionality: Solving high-dimensional partial differential equations using deep learning. CoRR (2017)
Christian Beck, Weinan E, Arnulf Jentzen
Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations. CoRR (2017)
Weinan E, Bing Yu
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems. CoRR (2017)
Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, Weinan E
Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics. CoRR (2017)
Qianxiao Li, Cheng Tai, Weinan E
Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms. ICML (2017)
Han Wang, Linfeng Zhang, Jiequn Han, Weinan E
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. CoRR (2017)
Qianxiao Li, Long Chen, Cheng Tai, Weinan E
Maximum Principle Based Algorithms for Deep Learning. J. Mach. Learn. Res. 18 (2017)
Weinan E, Jiequn Han, Arnulf Jentzen
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations. CoRR (2017)
Lei Wu, Zhanxing Zhu, Weinan E
Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes. CoRR (2017)
Chu Wang, Qianxiao Li, Weinan E, Bernard Chazelle
Noisy Hegselmann-Krause systems: Phase transition and the 2R-conjecture. CDC (2016)
Cheng Tai, Weinan E
Multiscale Adaptive Representation of Signals: I. The Basic Framework. J. Mach. Learn. Res. 17 (2016)
Jiequn Han, Weinan E
Deep Learning Approximation for Stochastic Control Problems. CoRR (2016)
Cheng Tai, Tong Xiao, Xiaogang Wang, Weinan E
Convolutional neural networks with low-rank regularization. ICLR (Poster) (2016)

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Publication Activity (10 Years)

Top Topics

Top Venues

Weinan E