3.7k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Peking UniversityCN33 papers
- Princeton UniversityUS25 papers
- Renmin University of ChinaCN21 papers
- Chinese Academy of SciencesCN12 papers
- Tsinghua UniversityCN12 papers
- University of Chinese Academy of SciencesCN10 papers
- Institute of Applied Physics and Computational MathematicsCN9 papers
- Aerospace Information Research InstituteCN4 papers
- CAEP Software Center for High Performance Numerical SimulationCN4 papers
- International Research Center of Big Data for Sustainable Development GoalsCN4 papers
- Central South UniversityCN3 papers
- ETH ZurichCH3 papers
17 papers · 1 filter
Towards Sharper Risk Bounds for Minimax Problems
Bowei Zhu, Shaojie Li, Yong Liu
Minimax problems have achieved success in machine learning such as adversarial training, robust optimization, reinforcement learning. For theoretical analysis, current optimal exce…
Federated learning-outcome prediction with multi-layer privacy protection
Yupei Zhang, Yuxin Li, Yifei Wang +3
Learning-outcome prediction (LOP) is a long-standing and critical problem in educational routes. Many studies have contributed to developing effective models while often suffering…
Structure-Aware DropEdge Towards Deep Graph Convolutional Networks
Jiaqi Han, Wenbing Huang, Yu Rong +3
It has been discovered that Graph Convolutional Networks (GCNs) encounter a remarkable drop in performance when multiple layers are piled up. The main factor that accounts for why…
Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics
Xin-Yang Liu, Min Zhu, Lu Lu +2
Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed…
Generalization Error of GAN from the Discriminator's Perspective
Hongkang Yang, Weinan E
The generative adversarial network (GAN) is a well-known model for learning high-dimensional distributions, but the mechanism for its generalization ability is not understood. In p…
PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs
Pu Ren, Chengping Rao, Yang Liu +2
Partial differential equations (PDEs) play a fundamental role in modeling and simulating problems across a wide range of disciplines. Recent advances in deep learning have shown th…
Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators
Peiyu Liu, Ze-Feng Gao, Wayne Xin Zhao +3
This paper presents a novel pre-trained language models (PLM) compression approach based on the matrix product operator (short as MPO) from quantum many-body physics. It can decomp…
Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification
Guoqiang Wu, Ruobing Zheng, Yingjie Tian +1
Multi-label classification studies the task where each example belongs to multiple labels simultaneously. As a representative method, Ranking Support Vector Machine (Rank-SVM) aims…
Sparse-Dense Subspace Clustering
Shuai Yang, Wenqi Zhu, Yuesheng Zhu
Subspace clustering refers to the problem of clustering high-dimensional data into a union of low-dimensional subspaces. Current subspace clustering approaches are usually based on…
A Comparative Analysis of the Optimization and Generalization Property of Two-layer Neural Network and Random Feature Models Under Gradient Descent Dynamics
Weinan E, Chao Ma, Lei Wu
A fairly comprehensive analysis is presented for the gradient descent dynamics for training two-layer neural network models in the situation when the parameters in both layers are…
Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics
Yunbo Wang, Jianjin Zhang, Hongyu Zhu +3
Natural spatiotemporal processes can be highly non-stationary in many ways, e.g. the low-level non-stationarity such as spatial correlations or temporal dependencies of local pixel…
Model Reduction with Memory and the Machine Learning of Dynamical Systems
Chao Ma, Jianchun Wang, Weinan E
The well-known Mori-Zwanzig theory tells us that model reduction leads to memory effect. For a long time, modeling the memory effect accurately and efficiently has been an importan…
Robustifying Models Against Adversarial Attacks by Langevin Dynamics
Vignesh Srinivasan, Arturo Marban, Klaus-Robert Müller +2
Adversarial attacks on deep learning models have compromised their performance considerably. As remedies, a lot of defense methods were proposed, which however, have been circumven…
Nostalgic Adam: Weighting more of the past gradients when designing the adaptive learning rate
Haiwen Huang, Chang Wang, Bin Dong
First-order optimization algorithms have been proven prominent in deep learning. In particular, algorithms such as RMSProp and Adam are extremely popular. However, recent works hav…
The Expressive Power of Neural Networks: A View from the Width
Zhou Lu, Hongming Pu, Feicheng Wang +2
The expressive power of neural networks is important for understanding deep learning. Most existing works consider this problem from the view of the depth of a network. In this pap…
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
Bing Yu, Haoteng Yin, Zhanxing Zhu
Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy…
Stochastic modified equations and adaptive stochastic gradient algorithms
Qianxiao Li, Cheng Tai, Weinan E
We develop the method of stochastic modified equations (SME), in which stochastic gradient algorithms are approximated in the weak sense by continuous-time stochastic differential…