Search Results for author: Yik-Chung Wu

Found 22 papers, 5 papers with code

Learning a Low-Rank Feature Representation: Achieving Better Trade-Off between Stability and Plasticity in Continual Learning

1 code implementation14 Dec 2023 Zhenrong Liu, Yang Li, Yi Gong, Yik-Chung Wu

This approach optimizes network parameters in the null space of the past tasks' feature representation matrix to guarantee the stability.

Continual Learning

Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving

1 code implementation28 Jun 2023 Wei-Bin Kou, Shuai Wang, Guangxu Zhu, Bin Luo, Yingxian Chen, Derrick Wing Kwan Ng, Yik-Chung Wu

While federated learning (FL) improves the generalization of end-to-end autonomous driving by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence rate due to long-range communications among vehicles and cloud server.

Autonomous Driving Federated Learning

To Fold or Not to Fold: Graph Regularized Tensor Train for Visual Data Completion

1 code implementation19 Jun 2023 Le Xu, Lei Cheng, Ngai Wong, Yik-Chung Wu

Tensor train (TT) representation has achieved tremendous success in visual data completion tasks, especially when it is combined with tensor folding.

Overcoming Beam Squint in Dual-Wideband mmWave MIMO Channel Estimation: A Bayesian Multi-Band Sparsity Approach

no code implementations19 Jun 2023 Le Xu, Lei Cheng, Ngai Wong, Yik-Chung Wu, H. Vincent Poor

A probabilistic model is built to induce the common sparsity in the spatial domain, and the first-order Taylor expansion is adopted to get rid of the grid mismatch in the dictionaries.

ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks

no code implementations14 Dec 2022 Yunqi Wang, Yang Li, Qingjiang Shi, Yik-Chung Wu

In order to achieve high data rate and ubiquitous connectivity in future wireless networks, a key task is to efficiently manage the radio resource by judicious beamforming and power allocation.

Management

Learning Cooperative Beamforming with Edge-Update Empowered Graph Neural Networks

no code implementations23 Nov 2022 Yunqi Wang, Yang Li, Qingjiang Shi, Yik-Chung Wu

However, the current GNNs are only equipped with the node-update mechanism, which restricts it from modeling more complicated problems such as the cooperative beamforming design, where the beamformers are on the graph edges of wireless networks.

Intelligent Reflecting Surface Aided Mobile Edge Computing With Binary Offloading: Energy Minimization for IoT Devices

no code implementations4 May 2022 Yizhen Yang, Yi Gong, Yik-Chung Wu

Mobile edge computing (MEC) is envisioned as a promising technique to support computation-intensive and timecritical applications in future Internet of Things (IoT) era.

Edge-computing Total Energy

Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods

no code implementations28 Apr 2022 Zongze Li, Shuai Wang, Qingfeng Lin, Yang Li, Miaowen Wen, Yik-Chung Wu, H. Vincent Poor

Reconfigurable intelligent surfaces (RISs) have a revolutionary capability to customize the radio propagation environment for wireless networks.

Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference

no code implementations18 Mar 2022 Yangge Chen, Lei Cheng, Yik-Chung Wu

Recently, there is a revival of interest in low-rank matrix completion-based unsupervised learning through the lens of dual-graph regularization, which has significantly improved the performance of multidisciplinary machine learning tasks such as recommendation systems, genotype imputation and image inpainting.

Graph Embedding Image Inpainting +4

Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization

no code implementations31 Aug 2021 Shuai Wang, Dachuan Li, Rui Wang, Qi Hao, Yik-Chung Wu, Derrick Wing Kwan Ng

Wireless federated learning (FL) is an emerging machine learning paradigm that trains a global parametric model from distributed datasets via wireless communications.

Federated Learning

Contrastive ACE: Domain Generalization Through Alignment of Causal Mechanisms

no code implementations2 Jun 2021 Yunqi Wang, Furui Liu, Zhitang Chen, Qing Lian, Shoubo Hu, Jianye Hao, Yik-Chung Wu

Domain generalization aims to learn knowledge invariant across different distributions while semantically meaningful for downstream tasks from multiple source domains, to improve the model's generalization ability on unseen target domains.

Domain Generalization

Edge Federated Learning Via Unit-Modulus Over-The-Air Computation

1 code implementation28 Jan 2021 Shuai Wang, Yuncong Hong, Rui Wang, Qi Hao, Yik-Chung Wu, Derrick Wing Kwan Ng

Simulation results show that the proposed UMAirComp framework with PAM algorithm achieves a smaller mean square error of model parameters' estimation, training loss, and test error compared with other benchmark schemes.

Autonomous Driving Federated Learning

Tensor Train Factorization and Completion under Noisy Data with Prior Analysis and Rank Estimation

no code implementations13 Oct 2020 Le Xu, Lei Cheng, Ngai Wong, Yik-Chung Wu

Tensor train (TT) decomposition, a powerful tool for analyzing multidimensional data, exhibits superior performance in many machine learning tasks.

Image Classification Variational Inference

Edge Learning with Unmanned Ground Vehicle: Joint Path, Energy and Sample Size Planning

no code implementations7 Sep 2020 Dan Liu, Shuai Wang, Zhigang Wen, Lei Cheng, Miaowen Wen, Yik-Chung Wu

However, different devices may transmit different data for different machine learning jobs and a fundamental question is how to jointly plan the UGV path, the devices' energy consumption, and the number of samples for different jobs?

BIG-bench Machine Learning Edge-computing

Learning Centric Power Allocation for Edge Intelligence

no code implementations21 Jul 2020 Shuai Wang, Rui Wang, Qi Hao, Yik-Chung Wu, H. Vincent Poor

While machine-type communication (MTC) devices generate massive data, they often cannot process this data due to limited energy and computation power.

Fairness

Machine Intelligence at the Edge with Learning Centric Power Allocation

no code implementations12 Nov 2019 Shuai Wang, Yik-Chung Wu, Minghua Xia, Rui Wang, H. Vincent Poor

However, power allocation in this paradigm requires maximizing the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient.

Fairness Learning Theory

Convergence analysis of belief propagation for pairwise linear Gaussian models

no code implementations12 Jun 2017 Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, José M. F. Moura

Gaussian belief propagation (BP) has been widely used for distributed inference in large-scale networks such as the smart grid, sensor networks, and social networks, where local measurements/observations are scattered over a wide geographical area.

Convergence analysis of the information matrix in Gaussian belief propagation

no code implementations13 Apr 2017 Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, José M. F. Moura

Gaussian belief propagation (BP) has been widely used for distributed estimation in large-scale networks such as the smart grid, communication networks, and social networks, where local measurements/observations are scattered over a wide geographical area.

Convergence Analysis of Distributed Inference with Vector-Valued Gaussian Belief Propagation

no code implementations7 Nov 2016 Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, José M. F. Moura

A necessary and sufficient convergence condition for the belief mean vector to converge to the optimal centralized estimator is provided under the assumption that the message information matrix is initialized as a positive semidefinite matrix.

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