2.3k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Shuguang Cui5 profiles29 · h 63
- Arnulf Jentzen22 · h 49
- Z. Luo18 · h 89
- Guangxu Zhu5 profiles17 · h 33
- Xiaoguang Han2 profiles15 · h 42
- Hang Li2 profiles13 · h 18
- Tsung-Hui Chang11 · h 45
- Qingjiang Shi3 profiles9 · h 36
- H. Poor2 profiles7 · h 141
- Rui Zhang3 profiles7 · h 137
- Shiyin Li7 · h 21
- Xiaoyang Li2 profiles7 · h 9
- Chinese University of Hong Kong, ShenzhenCN158 papers
- University of MünsterDE21 papers
- Chinese University of Hong KongHK16 papers
- University of Hong KongHK15 papers
- ETH ZurichCH14 papers
- Sun Yat-sen UniversityCN13 papers
- National University of SingaporeSG12 papers
- Peng Cheng LaboratoryCN10 papers
- Princeton UniversityUS10 papers
- Tongji UniversityCN10 papers
- Beijing University of Posts and TelecommunicationsCN9 papers
- Chinese Academy of SciencesCN9 papers
43 papers · 1 filter
Strong overall error analysis for the training of artificial neural networks via random initializations
Arnulf Jentzen, Adrian Riekert
Although deep learning based approximation algorithms have been applied very successfully to numerous problems, at the moment the reasons for their performance are not entirely und…
Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion
Xu Yan, Jiantao Gao, Jie Li +4
LiDAR point cloud analysis is a core task for 3D computer vision, especially for autonomous driving. However, due to the severe sparsity and noise interference in the single sweep…
Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks
Ye Hu, Mingzhe Chen, Walid Saad +2
In this paper, the problem of the trajectory design for a group of energy-constrained drones operating in dynamic wireless network environments is studied. In the considered model,…
An overview on deep learning-based approximation methods for partial differential equations
Christian Beck, Martin Hutzenthaler, Arnulf Jentzen +1
It is one of the most challenging problems in applied mathematics to approximatively solve high-dimensional partial differential equations (PDEs). Recently, several deep learning-b…
High order steady-state diffusion approximations
Anton Braverman, J. G. Dai, Xiao Fang
We derive and analyze new diffusion approximations of stationary distributions of Markov chains that are based on second- and higher-order terms in the expansion of the Markov chai…
Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment
Haoran Sun, Wenqiang Pu, Minghe Zhu +3
There has been a growing interest in developing data-driven and in particular deep neural network (DNN) based methods for modern communication tasks. For a few popular tasks such a…
Distributed Stochastic Consensus Optimization with Momentum for Nonconvex Nonsmooth Problems
Zhiguo Wang, Jiawei Zhang, Tsung-Hui Chang +2
While many distributed optimization algorithms have been proposed for solving smooth or convex problems over the networks, few of them can handle non-convex and non-smooth problems…
Transfer Learning and Meta Learning Based Fast Downlink Beamforming Adaptation
Yi Yuan, Gan Zheng, Kai-Kit Wong +2
This paper studies fast adaptive beamforming optimization for the signal-to-interference-plus-noise ratio balancing problem in a multiuser multiple-input single-output downlink sys…
Multi-Modal Active Learning for Automatic Liver Fibrosis Diagnosis based on Ultrasound Shear Wave Elastography
Lufei Gao, Ruisong Zhou, Changfeng Dong +4
With the development of radiomics, noninvasive diagnosis like ultrasound (US) imaging plays a very important role in automatic liver fibrosis diagnosis (ALFD). Due to the noisy dat…
Pushing The Limit of Type I Codebook For FDD Massive MIMO Beamforming: A Channel Covariance Reconstruction Approach
Kai Li, Ying Li, Lei Cheng +2
There is a fundamental trade-off between the channel representation resolution of codebooks and the overheads of feedback communications in the fifth generation new radio (5G NR) f…
On the Adversarial Robustness of LASSO Based Feature Selection
Fuwei Li, Lifeng Lai, Shuguang Cui
In this paper, we investigate the adversarial robustness of feature selection based on the regularized linear regression model, namely LASSO. In the considered model, ther…
Three-Dimensional Lip Motion Network for Text-Independent Speaker Recognition
Jianrong Wang, Tong Wu, Shanyu Wang +4
Lip motion reflects behavior characteristics of speakers, and thus can be used as a new kind of biometrics in speaker recognition. In the literature, lots of works used two-dimensi…
Scalable Deep Reinforcement Learning for Ride-Hailing
Jiekun Feng, Mark Gluzman, J. G. Dai
Ride-hailing services, such as Didi Chuxing, Lyft, and Uber, arrange thousands of cars to meet ride requests throughout the day. We consider a Markov decision process (MDP) model o…
Online Alternate Generator against Adversarial Attacks
Haofeng Li, Yirui Zeng, Guanbin Li +2
The field of computer vision has witnessed phenomenal progress in recent years partially due to the development of deep convolutional neural networks. However, deep learning models…
UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation
Yuanfeng Ji, Ruimao Zhang, Zhen Li +3
Aggregating multi-level feature representation plays a critical role in achieving robust volumetric medical image segmentation, which is important for the auxiliary diagnosis and t…
A general framework for decentralized optimization with first-order methods
Ran Xin, Shi Pu, Angelia Nedić +1
Decentralized optimization to minimize a finite sum of functions over a network of nodes has been a significant focus within control and signal processing research due to its natur…
Algorithms for Solving High Dimensional PDEs: From Nonlinear Monte Carlo to Machine Learning
Weinan E, Jiequn Han, Arnulf Jentzen
In recent years, tremendous progress has been made on numerical algorithms for solving partial differential equations (PDEs) in a very high dimension, using ideas from either nonli…
Ultrasound Liver Fibrosis Diagnosis using Multi-indicator guided Deep Neural Networks
Jiali Liu, Wenxuan Wang, Tianyao Guan +3
Accurate analysis of the fibrosis stage plays very important roles in follow-up of patients with chronic hepatitis B infection. In this paper, a deep learning framework is presente…
Edge Learning with Unmanned Ground Vehicle: Joint Path, Energy and Sample Size Planning
Dan Liu, Shuai Wang, Zhigang Wen +3
Edge learning (EL), which uses edge computing as a platform to execute machine learning algorithms, is able to fully exploit the massive sensing data generated by Internet of Thing…
Over-the-Air Computing for Wireless Data Aggregation in Massive IoT
Guangxu Zhu, Jie Xu, Kaibin Huang +1
Wireless data aggregation (WDA), referring to aggregating data distributed at devices (e.g., sensors and smartphone), is a common operation in 5G-and-beyond machine-type communicat…
Queueing Network Controls via Deep Reinforcement Learning
J. G. Dai, Mark Gluzman
Novel advanced policy gradient (APG) methods, such as Trust Region policy optimization and Proximal policy optimization (PPO), have become the dominant reinforcement learning algor…
Cooperative Interference Management for Over-the-Air Computation Networks
Xiaowen Cao, Guangxu Zhu, Jie Xu +1
This paper considers a multi-cell AirComp network and investigates the optimal power control policies over multiple cells to regulate the effect of inter-cell interference. First,…
Demand Estimation from Sales Transaction Data -- Practical Extensions
Norbert Remenyi, Xiaodong Luo
In this paper we discuss practical limitations of the standard choice-based demand models used in the literature to estimate demand from sales transaction data. We present modifica…
UVeQFed: Universal Vector Quantization for Federated Learning
Nir Shlezinger, Mingzhe Chen, Yonina C. Eldar +2
Traditional deep learning models are trained at a centralized server using labeled data samples collected from end devices or users. Such data samples often include private informa…