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
32 papers · 1 filter
An Overview of Signal Processing Techniques for RIS/IRS-aided Wireless Systems
Cunhua Pan, Gui Zhou, Kangda Zhi +8
In the past as well as present wireless communication systems, the wireless propagation environment is regarded as an uncontrollable black box that impairs the received signal qual…
Configuring Intelligent Reflecting Surface with Performance Guarantees: Optimal Beamforming
Yaowen Zhang, Kaiming Shen, Shuyi Ren +3
This work proposes linear time strategies to optimally configure the phase shifts for the reflective elements of an intelligent reflecting surface (IRS). Specifically, we show that…
On the existence of global minima and convergence analyses for gradient descent methods in the training of deep neural networks
Arnulf Jentzen, Adrian Riekert
In this article we study fully-connected feedforward deep ReLU ANNs with an arbitrarily large number of hidden layers and we prove convergence of the risk of the GD optimization me…
Configuring Intelligent Reflecting Surface with Performance Guarantees: Blind Beamforming
Shuyi Ren, Kaiming Shen, Yaowen Zhang +3
This work gives a blind beamforming strategy for intelligent reflecting surface (IRS), aiming to boost the received signal-to-noise ratio (SNR) by coordinating phase shifts across…
Optimal Discrete Constellation Inputs for Aggregated LiFi-WiFi Networks
Shuai Ma, Fan Zhang, Songtao Lu +5
In this paper, we investigate the performance of a practical aggregated LiFi-WiFi system with the discrete constellation inputs from a practical view. We derive the achievable rate…
Boosting Fast Adversarial Training with Learnable Adversarial Initialization
Xiaojun Jia, Yong Zhang, Baoyuan Wu +2
Adversarial training (AT) has been demonstrated to be effective in improving model robustness by leveraging adversarial examples for training. However, most AT methods are in face…
Joint LED Selection and Precoding Optimization for Multiple-User Multiple-Cell VLC Systems
Yang Yang, Yujie Yang, Mingzhe Chen +4
This paper proposes a hybrid dimming scheme based on joint LED selection and precoding design (TASP-HD) for multiple-user (MU) multiple-cell (MC) visible light communications (VLC)…
Efficient Estimation of Sensor Biases for the 3-Dimensional Asynchronous Multi-Sensor System
Wenqiang Pu, Ya-Feng Liu, Zhi-Quan Luo
An important preliminary procedure in multi-sensor data fusion is \textit{sensor registration}, and the key step in this procedure is to estimate sensor biases from their noisy mea…
Existence, uniqueness, and convergence rates for gradient flows in the training of artificial neural networks with ReLU activation
Simon Eberle, Arnulf Jentzen, Adrian Riekert +1
The training of artificial neural networks (ANNs) with rectified linear unit (ReLU) activation via gradient descent (GD) type optimization schemes is nowadays a common industrially…
Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning
Guangyi Liu, Yinghong Liao, Fuyu Wang +8
Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing…
Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation
Arnulf Jentzen, Adrian Riekert
Gradient descent (GD) type optimization schemes are the standard methods to train artificial neural networks (ANNs) with rectified linear unit (ReLU) activation. Such schemes can b…
Transformer with Peak Suppression and Knowledge Guidance for Fine-grained Image Recognition
Xinda Liu, Lili Wang, Xiaoguang Han
Fine-grained image recognition is challenging because discriminative clues are usually fragmented, whether from a single image or multiple images. Despite their significant improve…
Hepatocellular Carcinoma Segmentation from Digital Subtraction Angiography Videos using Learnable Temporal Difference
Wenting Jiang, Yicheng Jiang, Lu Zhang +5
Automatic segmentation of hepatocellular carcinoma (HCC) in Digital Subtraction Angiography (DSA) videos can assist radiologists in efficient diagnosis of HCC and accurate evaluati…
Task-Aware Sampling Layer for Point-Wise Analysis
Yiqun Lin, Lichang Chen, Haibin Huang +3
Sampling, grouping, and aggregation are three important components in the multi-scale analysis of point clouds. In this paper, we present a novel data-driven sampler learning strat…
Beamforming Technologies for Ultra-Massive MIMO in Terahertz Communications
Boyu Ning, Zhongbao Tian, Weidong Mei +5
Terahertz (THz) communications with a frequency band THz are envisioned as a promising solution to future high-speed wireless communication. Although with tens of gigahert…
Data-Driven Adaptive Network Slicing for Multi-Tenant Networks
Navid Reyhanian, Zhi-Quan Luo
Network slicing to support multi-tenancy plays a key role in improving the performance of 5G networks. In this paper, we propose a two time-scale framework for the reservation-base…
SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation
Hong-Yu Zhou, Chengdi Wang, Haofeng Li +4
Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis. Both approaches can improve the performance of fully-supervised met…
RevCore: Review-augmented Conversational Recommendation
Yu Lu, Junwei Bao, Yan Song +4
Existing conversational recommendation (CR) systems usually suffer from insufficient item information when conducted on short dialogue history and unfamiliar items. Incorporating e…
Quantized Federated Learning under Transmission Delay and Outage Constraints
Yanmeng Wang, Yanqing Xu, Qingjiang Shi +1
Federated learning (FL) has been recognized as a viable distributed learning paradigm which trains a machine learning model collaboratively with massive mobile devices in the wirel…
Compressed Gradient Tracking for Decentralized Optimization Over General Directed Networks
Zhuoqing Song, Lei Shi, Shi Pu +1
In this paper, we propose two communication efficient decentralized optimization algorithms over a general directed multi-agent network. The first algorithm, termed Compressed Push…
Ten Quick Tips for Deep Learning in Biology
Benjamin D. Lee, Anthony Gitter, Casey S. Greene +17
Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that…
An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization
Lunchen Xie, Jiaqi Liu, Songtao Lu +2
XGBoost is one of the most widely used machine learning models in the industry due to its superior learning accuracy and efficiency. Targeting at data isolation issues in the big d…
Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective
Haoran Sun, Wenqiang Pu, Xiao Fu +2
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…
Stochastic Mirror Descent for Low-Rank Tensor Decomposition Under Non-Euclidean Losses
Wenqiang Pu, Shahana Ibrahim, Xiao Fu +1
This work considers low-rank canonical polyadic decomposition (CPD) under a class of non-Euclidean loss functions that frequently arise in statistical machine learning and signal p…