1 citations · 1 across the 6 of their papers we have counts for
7 papers
COBALT: Contextual Bandit Learning with Offline Trajectories for Multi-Turn Code Generation
Ziru Chen, Dongdong Chen, Ruinan Jin +3
Training large language models (LLMs) for multi-turn code generation typically relies on online reinforcement learning (RL), in which models learn from repeatedly generated online…
Communication-Pipelined Split Federated Learning for Foundation Model Fine-Tuning in UAV Networks
Zizhen Zhou, Ying-Chang Liang, Yanyu Cheng +1
Deploying foundation models (FMs) on uncrewed aerial vehicles (UAVs) promises broad ``low-altitude economy'' applications. Split federated learning (SFL)-based fine-tuning leverage…
What Can One Expect When Solving PDEs Using Shallow Neural Networks?
Roy Y. He, Ying Liang, Hongkai Zhao +1
We use elliptic partial differential equations (PDEs) as examples to show various properties and behaviors when shallow neural networks (SNNs) are used to represent the solutions.…
Deep Neural Network-Driven Adaptive Filtering
Qizhen Wang, Gang Wang, Ying-Chang Liang
This paper proposes a deep neural network (DNN)-driven framework to address the longstanding generalization challenge in adaptive filtering (AF). In contrast to traditional AF fram…
Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning
Xianke Qiang, Hongda Liu, Xinran Zhang +2
Large Artificial Intelligence Models (LAMs) powered by massive datasets, extensive parameter scales, and extensive computational resources, leading to significant transformations a…
AIGC-assisted Federated Learning for Edge Intelligence: Architecture Design, Research Challenges and Future Directions
Xianke Qiang, Zheng Chang, Ying-Chang Liang
Federated learning (FL) can fully leverage large-scale terminal data while ensuring privacy and security, and is considered as a distributed alternative for the centralized machine…
A Retention-Centric Framework for Continual Learning with Guaranteed Model Developmental Safety
Gang Li, Wendi Yu, Yao Yao +4
In real-world applications, learning-enabled systems often undergo iterative model development to address challenging or emerging tasks, which involve collecting new data, training…