4 papers
GACER: Granularity-Aware ConcurrEncy Regulation for Multi-Tenant Deep Learning
Yongbo Yu, Fuxun Yu, Mingjia Zhang +4
As deep learning continues to advance and is applied to increasingly complex scenarios, the demand for concurrent deployment of multiple neural network models has arisen. This dema…
QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM
Ping Xu, Yue Wang, Xiang Chen +1
This paper focuses on online kernel learning over a decentralized network. Each agent in the network receives continuous streaming data locally and works collaboratively to learn a…
Improving Redundancy Availability: Dynamic Subtasks Modulation for Robots with Redundancy Insufficiency
Lu Chen, Lipeng Chen, Xiangchi Chen +4
This work presents an approach for robots to suitably carry out complex applications characterized by the presence of multiple additional constraints or subtasks (e.g. obstacle and…
COKE: Communication-Censored Decentralized Kernel Learning
Ping Xu, Yue Wang, Xiang Chen +1
This paper studies the decentralized optimization and learning problem where multiple interconnected agents aim to learn an optimal decision function defined over a reproducing ker…