14 citations · 42 across the 10 of their papers we have counts for
10 papers
Joint Activity Detection and Channel Estimation in Massive Machine-Type Communications with Low-Resolution ADC
Ye Xue, An Liu, Yang Li +2
In massive machine-type communications, data transmission is usually considered sporadic, and thus inherently has a sparse structure. This paper focuses on the joint activity detec…
Riemannian Low-Rank Model Compression for Federated Learning with Over-the-Air Aggregation
Ye Xue, Vincent Lau
Low-rank model compression is a widely used technique for reducing the computational load when training machine learning models. However, existing methods often rely on relaxing th…
GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systems
Yangchen Li, Ying Cui, Vincent Lau
The optimal implementation of federated learning (FL) in practical edge computing systems has been an outstanding problem. In this paper, we propose an optimization-based quantized…
Structured Bayesian Compression for Deep Neural Networks Based on The Turbo-VBI Approach
Chengyu Xia, Danny H. K. Tsang, Vincent K. N. Lau
With the growth of neural network size, model compression has attracted increasing interest in recent research. As one of the most common techniques, pruning has been studied for a…
Optimization-Based GenQSGD for Federated Edge Learning
Yangchen Li, Ying Cui, Vincent Lau
Optimal algorithm design for federated learning (FL) remains an open problem. This paper explores the full potential of FL in practical edge computing systems where workers may hav…
An Optimization Framework for Federated Edge Learning
Yangchen Li, Ying Cui, Vincent Lau
The optimal design of federated learning (FL) algorithms for solving general machine learning (ML) problems in practical edge computing systems with quantized message passing remai…
Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validation
Huayan Guo, Yifan Zhu, Haoyu Ma +5
In this paper, we develop an orthogonal-frequency-division-multiplexing (OFDM)-based over-the-air (OTA) aggregation solution for wireless federated learning (FL). In particular, th…
Efficient Sparse Coding using Hierarchical Riemannian Pursuit
Ye Xue, Vincent Lau, Songfu Cai
Sparse coding is a class of unsupervised methods for learning a sparse representation of the input data in the form of a linear combination of a dictionary and a sparse code. This…
Online Orthogonal Dictionary Learning Based on Frank-Wolfe Method
Ye Xue, Vincent Lau
Dictionary learning is a widely used unsupervised learning method in signal processing and machine learning. Most existing works of dictionary learning are in an offline manner. Th…
Complete Dictionary Learning via -norm Maximization
Yifei Shen, Ye Xue, Jun Zhang +2
Dictionary learning is a classic representation learning method that has been widely applied in signal processing and data analytics. In this paper, we investigate a family of $\el…