1 citations · 1 across the 4 of their papers we have counts for
5 papers
Structure-Guided Diffusion Model for EEG-Based Visual Cognition Reconstruction
Yongxiang Lian, Yueyang Cang, Pingge Hu +2
Objective: Decoding visual information from electroencephalography (EEG) is an important problem in neuroscience and brain-computer interface (BCI) research. Existing methods are l…
Graph-GRPO: Stabilizing Multi-Agent Topology Learning via Group Relative Policy Optimization
Yueyang Cang, Xiaoteng Zhang, Erlu Zhao +7
Optimizing communication topology is fundamental to the efficiency and effectiveness of Large Language Model (LLM)-based Multi-Agent Systems (MAS). While recent approaches utilize…
PromptCD: Test-Time Behavior Enhancement via Polarity-Prompt Contrastive Decoding
Baolong Bi, Yuyao Ge, Shenghua Liu +9
Reliable AI systems require large language models (LLMs) to exhibit behaviors aligned with human preferences and values. However, most existing alignment approaches operate at trai…
FPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework
Yuchen He, Chuyun Shen, Xiangfeng Wang +1
Federated continual learning (FCL) aims to learn from sequential data stream in the decentralized federated learning setting, while simultaneously mitigating the catastrophic forge…
Masked Autoencoders are Parameter-Efficient Federated Continual Learners
Yuchen He, Xiangfeng Wang
Federated learning is a specific distributed learning paradigm in which a central server aggregates updates from multiple clients' local models, thereby enabling the server to lear…