7 citations · 8 across the 7 of their papers we have counts for
9 papers
Physical AI: A Survey of Models, Learning, Systems, and Open Challenges
Bin Gao, Anuj Gautam, Aritra Bagchi +17
Physical AI aims to enable intelligent systems to perceive, reason, act, and learn through interaction with the physical world. Unlike purely digital intelligence, physical agents…
GALA: Generative Aligned Learning for Adaptive Multimodal Representation in the Taobao Shangou Recommender System
Jiping Liu, Zhongmin Zhang, Zisen Sang +7
Modern recommender systems in food delivery increasingly leverage multimodal signals, including images, text, and user interaction histories, to enhance user experience, yet effect…
CoDrone: Autonomous Drone Navigation Assisted by Edge and Cloud Foundation Models
Pengyu Chen, Tao Ouyang, Ke Luo +2
Autonomous navigation for Unmanned Aerial Vehicles faces key challenges from limited onboard computational resources, which restrict deployed deep neural networks to shallow archit…
CoEdge-RAG: Optimizing Hierarchical Scheduling for Retrieval-Augmented LLMs in Collaborative Edge Computing
Guihang Hong, Tao Ouyang, Kongyange Zhao +2
Motivated by the imperative for real-time responsiveness and data privacy preservation, large language models (LLMs) are increasingly deployed on resource-constrained edge devices…
Online Optimization of DNN Inference Network Utility in Collaborative Edge Computing
Rui Li, Tao Ouyang, Liekang Zeng +3
Collaborative Edge Computing (CEC) is an emerging paradigm that collaborates heterogeneous edge devices as a resource pool to compute DNN inference tasks in proximity such as edge…
BeeFlow: Behavior Tree-based Serverless Workflow Modeling and Scheduling for Resource-Constrained Edge Clusters
Ke Luo, Tao Ouyang, Zhi Zhou +1
Serverless computing has gained popularity in edge computing due to its flexible features, including the pay-per-use pricing model, auto-scaling capabilities, and multi-tenancy sup…
HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association
Qiong Wu, Xu Chen, Tao Ouyang +4
Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients while keeping the training data locally. However, for ma…
Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspective
Guangjing Huang, Xu Chen, Tao Ouyang +3
Federated learning (FL) is a promising distributed framework for collaborative artificial intelligence model training while protecting user privacy. A bootstrapping component that…
Separating Data via Block Invalidation Time Inference for Write Amplification Reduction in Log-Structured Storage
Qiuping Wang, Jinhong Li, Patrick P. C. Lee +3
Log-structured storage has been widely deployed in various domains of storage systems, yet its garbage collection incurs write amplification (WA) due to the rewrites of live data.…