22 citations · 25 across the 6 of their papers we have counts for
7 papers
TRGP: Trust Region Gradient Projection for Continual Learning
Sen Lin, Li Yang, Deliang Fan +1
Catastrophic forgetting is one of the major challenges in continual learning. To address this issue, some existing methods put restrictive constraints on the optimization space of…
Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence
Mehmet Dedeoglu, Sen Lin, Zhaofeng Zhang +1
Learning generative models is challenging for a network edge node with limited data and computing power. Since tasks in similar environments share model similarity, it is plausible…
Distributed Q-Learning with State Tracking for Multi-agent Networked Control
Hang Wang, Sen Lin, Hamid Jafarkhani +1
This paper studies distributed Q-learning for Linear Quadratic Regulator (LQR) in a multi-agent network. The existing results often assume that agents can observe the global system…
Accelerating Distributed Online Meta-Learning via Multi-Agent Collaboration under Limited Communication
Sen Lin, Mehmet Dedeoglu, Junshan Zhang
Online meta-learning is emerging as an enabling technique for achieving edge intelligence in the IoT ecosystem. Nevertheless, to learn a good meta-model for within-task fast adapta…
Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learning
Sheng Yue, Ju Ren, Jiang Xin +2
In order to meet the requirements for performance, safety, and latency in many IoT applications, intelligent decisions must be made right here right now at the network edge. Howeve…
MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning
Sen Lin, Li Yang, Zhezhi He +2
While deep learning has achieved phenomenal successes in many AI applications, its enormous model size and intensive computation requirements pose a formidable challenge to the dep…