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
23 papers · 1 filter
Efficiency Maximization for UAV-Enabled Mobile Relaying Systems with Laser Charging
Ming-Min Zhao, Qingjiang Shi, Min-Jian Zhao
This work studies the joint problem of power and trajectory optimization in an unmanned aerial vehicle (UAV)-enabled mobile relaying system. In the considered system, in order to p…
Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser Communications: Framework, Algorithms, and Analysis
Zhaorui Wang, Liang Liu, Shuguang Cui
In intelligent reflecting surface (IRS) assisted communication systems, the acquisition of channel state information (CSI) is a crucial impediment for achieving the beamforming gai…
Self-Enhanced Convolutional Network for Facial Video Hallucination
Chaowei Fang, Guanbin Li, Xiaoguang Han +1
As a domain-specific super-resolution problem, facial image hallucination has enjoyed a series of breakthroughs thanks to the advances of deep convolutional neural networks. Howeve…
Jacobi-type algorithm for low rank orthogonal approximation of symmetric tensors and its convergence analysis
Jianze Li, Konstantin Usevich, Pierre Comon
In this paper, we propose a Jacobi-type algorithm to solve the low rank orthogonal approximation problem of symmetric tensors. This algorithm includes as a special case the well-kn…
Full error analysis for the training of deep neural networks
Christan Beck, Arnulf Jentzen, Benno Kuckuck
Deep learning algorithms have been applied very successfully in recent years to a range of problems out of reach for classical solution paradigms. Nevertheless, there is no complet…
A Joint Learning and Communications Framework for Federated Learning over Wireless Networks
Mingzhe Chen, Zhaohui Yang, Walid Saad +3
In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execu…
Deep neural network approximations for Monte Carlo algorithms
Philipp Grohs, Arnulf Jentzen, Diyora Salimova
Recently, it has been proposed in the literature to employ deep neural networks (DNNs) together with stochastic gradient descent methods to approximate solutions of PDEs. There are…
On the Adversarial Robustness of Subspace Learning
Fuwei Li, Lifeng Lai, Shuguang Cui
In this paper, we study the adversarial robustness of subspace learning problems. Different from the assumptions made in existing work on robust subspace learning where data sample…
Introducing Hypergraph Signal Processing: Theoretical Foundation and Practical Applications
Songyang Zhang, Zhi Ding, Shuguang Cui
Signal processing over graphs has recently attracted significant attentions for dealing with structured data. Normal graphs, however, only model pairwise relationships between node…
Optimal Energy Allocation and Task Offloading Policy for Wireless Powered Mobile Edge Computing Systems
Feng Wang, Jie Xu, Shuguang Cui
This paper studies a wireless powered mobile edge computing (MEC) system with fluctuating channels and dynamic task arrivals over time. We jointly optimize the transmission energy…
Efficient and Effective Context-Based Convolutional Entropy Modeling for Image Compression
Mu Li, Kede Ma, Jane You +2
Precise estimation of the probabilistic structure of natural images plays an essential role in image compression. Despite the recent remarkable success of end-to-end optimized imag…
Optimized Power Control for Over-the-Air Computation in Fading Channels
Xiaowen Cao, Guangxu Zhu, Jie Xu +1
In this paper, we study the power control problem for Over-the-air computation (AirComp) over fading channels. Our objective is to minimize the computation error by jointly optimiz…
Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approach
Yue Xu, Wenjun Xu, Zhi Wang +2
In this paper, we propose a deep reinforcement learning (DRL) based mobility load balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load balanc…
Quadratic Matrix Inequality Approach to Robust Adaptive Beamforming for General-Rank Signal Model
Yongwei Huang, Sergiy A. Vorobyov, Zhi-Quan Luo
The worst-case robust adaptive beamforming problem for general-rank signal model is considered. This is a nonconvex problem, and an approximate version of it (obtained by introduci…
Approximate matrix and tensor diagonalization by unitary transformations: convergence of Jacobi-type algorithms
Konstantin Usevich, Jianze Li, Pierre Comon
We propose a gradient-based Jacobi algorithm for a class of maximization problems on the unitary group, with a focus on approximate diagonalization of complex matrices and tensors…
Inexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex Optimization
Yang Yang, Marius Pesavento, Zhi-Quan Luo +1
In this paper, we propose an inexact block coordinate descent algorithm for large-scale nonsmooth nonconvex optimization problems. At each iteration, a particular block variable is…
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series
Feng Yin, Lishuo Pan, Xinwei He +4
Gaussian processes (GP) for machine learning have been studied systematically over the past two decades and they are by now widely used in a number of diverse applications. However…
On the strong regularity of degenerate additive noise driven stochastic differential equations with respect to their initial values
Arnulf Jentzen, Benno Kuckuck, Thomas Müller-Gronbach +1
Recently in [M. Hairer, M. Hutzenthaler, and A. Jentzen, Ann. Probab. 43, 2 (2015), 468--527] and [A. Jentzen, T. Müller-Gronbach, and L. Yaroslavtseva, Commun. Math. Sci. 14, 6 (2…
Signal Demodulation with Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset and Algorithms
Shuai Ma, Jiahui Dai, Songtao Lu +4
In this paper, we investigate the design and implementation of machine learning (ML) based demodulation methods in the physical layer of visible light communication (VLC) systems.…
Two-phase Hair Image Synthesis by Self-Enhancing Generative Model
Haonan Qiu, Chuan Wang, Hang Zhu +3
Generating plausible hair image given limited guidance, such as sparse sketches or low-resolution image, has been made possible with the rise of Generative Adversarial Networks (GA…
Point cloud denoising based on tensor Tucker decomposition
Jianze Li, Xiao-Ping Zhang, Tuan Tran
In this paper, we propose a new algorithm for point cloud denoising based on the tensor Tucker decomposition. We first represent the local surface patches of a noisy point cloud to…
On the Coordinate Change to the First-Order Spline Kernel for Regularized Impulse Response Estimation
Yusuke Fujimoto, Tianchi Chen
The so-called tuned-correlated kernel (sometimes also called the first-order stable spline kernel) is one of the most widely used kernels for the regularized impulse response estim…
Model and Reinforcement Learning for Markov Games with Risk Preferences
Wenjie Huang, Pham Viet Hai, William B. Haskell
We motivate and propose a new model for non-cooperative Markov game which considers the interactions of risk-aware players. This model characterizes the time-consistent dynamic "ri…