1 citations · 1 across the 9 of their papers we have counts for
10 papers
Efficient Transceiver Design for Aerial Image Transmission and Large-scale Scene Reconstruction
Zeyi Ren, Jialin Dong, Wei Zuo +4
Large-scale three-dimensional (3D) scene reconstruction in low-altitude intelligent networks (LAIN) demands highly efficient wireless image transmission. However, existing schemes…
Nonparametric Teaching of Attention Learners
Chen Zhang, Jianghui Wang, Bingyang Cheng +7
Attention learners, neural networks built on the attention mechanism, e.g., transformers, excel at learning the implicit relationships that relate sequences to their corresponding…
HPTune: Hierarchical Proactive Tuning for Collision-Free Model Predictive Control
Wei Zuo, Chengyang Li, Yikun Wang +5
Parameter tuning is a powerful approach to enhance adaptability in model predictive control (MPC) motion planners. However, existing methods typically operate in a myopic fashion t…
FedDSR: Federated Deep Supervision and Regularization Towards Autonomous Driving
Wei-Bin Kou, Guangxu Zhu, Bingyang Cheng +3
Federated Learning (FL) enables collaborative training of autonomous driving (AD) models across distributed vehicles while preserving data privacy. However, FL encounters critical…
NTK-Guided Implicit Neural Teaching
Chen Zhang, Wei Zuo, Bingyang Cheng +4
Implicit Neural Representations (INRs) parameterize continuous signals via multilayer perceptrons (MLPs), enabling compact, resolution-independent modeling for tasks like image, au…
Large-Scale Bayesian Tensor Reconstruction via Approximate Message Passing
Bingyang Cheng, Zhongtao Chen, Yichen Jin +4
While CANDECOMP/PARAFAC (CP) decomposition (CPD) is fundamental for tensor reconstruction, Bayesian CPD often scales poorly because variational updates require repeated matrix inve…
iMacHSR: Intermediate Multi-Access Heterogeneous Supervision and Regularization Scheme Toward Architecture-Agnostic Training
Wei-Bin Kou, Guangxu Zhu, Yichen Jin +4
While deep supervision is a powerful training strategy by supervising intermediate layers with auxiliary losses, it faces three underexplored problems: (I) Existing deep supervisio…
FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving
Wei-Bin Kou, Guangxu Zhu, Bingyang Cheng +3
Street Scene Semantic Understanding (denoted as S3U) is a crucial but complex task for autonomous driving (AD) vehicles. Their inference models typically face poor generalization d…
A General Optimization Framework for Tackling Distance Constraints in Movable Antenna-Aided Systems
Yichen Jin, Qingfeng Lin, Yang Li +4
The recently emerged movable antenna (MA) shows great promise in leveraging spatial degrees of freedom to enhance the performance of wireless systems. However, resource allocation…
Channel Estimation for Rydberg Atomic Receivers
Bokai Xu, Jiayi Zhang, Zhongtao Chen +4
The rapid development of the quantum technology presents huge opportunities for 6G communications. Leveraging the quantum properties of highly excited Rydberg atoms, Rydberg atom-b…