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
19 papers · 1 filter
Explainable Graph Representation Learning via Graph Pattern Analysis
Xudong Wang, Ziheng Sun, Chris Ding +1
Explainable artificial intelligence (XAI) is an important area in the AI community, and interpretability is crucial for building robust and trustworthy AI models. While previous wo…
CSI-BERT2: A BERT-inspired Framework for Efficient CSI Prediction and Classification in Wireless Communication and Sensing
Zijian Zhao, Fanyi Meng, Zhonghao Lyu +3
Channel state information (CSI) is a fundamental component in both wireless communication and sensing systems, enabling critical functions such as radio resource optimization and e…
Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing
Zijian Zhao, Tingwei Chen, Fanyi Meng +3
Despite the development of various deep learning methods for Wi-Fi sensing, package loss often results in noncontinuous estimation of the Channel State Information (CSI), which neg…
Activity Detection for Massive Connectivity in Cell-free Networks with Unknown Large-scale Fading, Channel Statistics, Noise Variance, and Activity Probability: A Bayesian Approach
Hao Zhang, Qingfeng Lin, Yang Li +2
Activity detection is an important task in the next generation grant-free multiple access. While there are a number of existing algorithms designed for this purpose, they mostly re…
Gradient descent provably escapes saddle points in the training of shallow ReLU networks
Patrick Cheridito, Arnulf Jentzen, Florian Rossmannek
Dynamical systems theory has recently been applied in optimization to prove that gradient descent algorithms bypass so-called strict saddle points of the loss function. However, in…
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…
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…
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…
Landscape analysis for shallow neural networks: complete classification of critical points for affine target functions
Patrick Cheridito, Arnulf Jentzen, Florian Rossmannek
In this paper, we analyze the landscape of the true loss of neural networks with one hidden layer and ReLU, leaky ReLU, or quadratic activation. In all three cases, we provide a co…
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…
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,…
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…
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…
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…
Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering
Jicong Fan, Chengrun Yang, Madeleine Udell
Low dimensional nonlinear structure abounds in datasets across computer vision and machine learning. Kernelized matrix factorization techniques have recently been proposed to learn…
SR2CNN: Zero-Shot Learning for Signal Recognition
Yihong Dong, Xiaohan Jiang, Huaji Zhou +2
Signal recognition is one of significant and challenging tasks in the signal processing and communications field. It is often a common situation that there's no training data acces…
Scalable Learning Paradigms for Data-Driven Wireless Communication
Yue Xu, Feng Yin, Wenjun Xu +3
The marriage of wireless big data and machine learning techniques revolutionizes the wireless system by the data-driven philosophy. However, the ever exploding data volume and mode…
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…
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…