2 citations · 3 across the 16 of their papers we have counts for
17 papers
Heat Transport of the -Fermi--Pasta--Ulam--Tsingou chain in the long-wave limit
Henrique Santos Lima, Matheus M. R. Poltronieri Martins, Lucas Reis e Silva
Resolving the anomalous conductivity exponent of the symmetric -Fermi--Pasta--Ulam--Tsingou chain by molecular dynamics can require very large systems because of long finite-siz…
VisCache: Visual KV Cache Pruning for Efficient Vision Large Language Model Inference
Lyuke Wang, Zhuo Li, Guangxu Zhu
While Vision Large Language Models (VLLMs) have achieved remarkable success in multimodal reasoning, their long-context inference remains prohibitively expensive due to the massive…
AirMoE: Statistic-Augmented Over-the-Air MoE for Collaborative Intelligence
Wei-Bin Kou, Jingreng Lei, Guangxu Zhu +1
Mixture of Experts (MoE) are increasingly deployed over wireless cloud-edge networks, as a single edge device lacks sufficient resources to host large-scale models locally. In this…
The Universal Language of CSI:Unifying Wireless Sensing Across Devices and Environments
Jiayi Chen, Weiting Ou, Guangxu Zhu
WiFi sensing based on Channel State Information (CSI) promises ubiquitous, device-free perception, yet current research remains trapped in a Tower of Babel - fragmented into isolat…
Bridging Large-Model Reasoning and Real-Time Control via Agentic Fast-Slow Planning
Jiayi Chen, Shuai Wang, Guangxu Zhu +1
Large foundation models enable powerful reasoning for autonomous systems, but mapping semantic intent to reliable real-time control remains challenging. Existing approaches either…
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…
Statistic-Augmented, Decoupled MoE Routing and Aggregating in Autonomous Driving
Wei-Bin Kou, Guangxu Zhu, Jingreng Lei +3
Autonomous driving (AD) scenarios are inherently complex and diverse, posing significant challenges for a single deep learning model to effectively cover all possible conditions, s…
Adverse Weather-Independent Framework Towards Autonomous Driving Perception through Temporal Correlation and Unfolded Regularization
Wei-Bin Kou, Guangxu Zhu, Rongguang Ye +5
Various adverse weather conditions such as fog and rain pose a significant challenge to autonomous driving (AD) perception tasks like semantic segmentation, object detection, etc.…
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…
Opportunistic Collaborative Planning with Large Vision Model Guided Control and Joint Query-Service Optimization
Jiayi Chen, Shuai Wang, Guoliang Li +4
Navigating autonomous vehicles in open scenarios is a challenge due to the difficulties in handling unseen objects. Existing solutions either rely on small models that struggle wit…
Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator
Wei-Bin Kou, Guangxu Zhu, Rongguang Ye +3
Learning-based street scene semantic understanding in autonomous driving (AD) has advanced significantly recently, but the performance of the AD model is heavily dependent on the q…
Enhancing Large Vision Model in Street Scene Semantic Understanding through Leveraging Posterior Optimization Trajectory
Wei-Bin Kou, Qingfeng Lin, Ming Tang +5
To improve the generalization of the autonomous driving (AD) perception model, vehicles need to update the model over time based on the continuously collected data. As time progres…
Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving
Wei-Bin Kou, Qingfeng Lin, Ming Tang +4
Street Scene Semantic Understanding (denoted as TriSU) is a complex task for autonomous driving (AD). However, inference model trained from data in a particular geographical region…
Generalizable Autonomous Driving System across Diverse Adverse Weather Conditions
Wei-Bin Kou, Guangxu Zhu, Rongguang Ye +4
Various adverse weather conditions pose a significant challenge to autonomous driving (AD) street scene semantic understanding (segmentation). A common strategy is to minimize the…
FedRC: A Rapid-Converged Hierarchical Federated Learning Framework in Street Scene Semantic Understanding
Wei-Bin Kou, Qingfeng Lin, Ming Tang +3
Street Scene Semantic Understanding (denoted as TriSU) is a crucial but complex task for world-wide distributed autonomous driving (AD) vehicles (e.g., Tesla). Its inference model…
pFedLVM: A Large Vision Model (LVM)-Driven and Latent Feature-Based Personalized Federated Learning Framework in Autonomous Driving
Wei-Bin Kou, Qingfeng Lin, Ming Tang +8
Deep learning-based Autonomous Driving (AD) models often exhibit poor generalization due to data heterogeneity in an ever domain-shifting environment. While Federated Learning (FL)…