354 citations · 634 across the 70 of their papers we have counts for
9 papers · 1 filter
Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs
Zixuan Hu, Yongxian Wei, Li Shen +2
Large Language Models (LLMs) such as ChatGPT demonstrate strong few-shot adaptability without requiring fine-tuning, positioning them ideal for data-limited and real-time applicati…
Communication Learning in Multi-Agent Systems from Graph Modeling Perspective
Shengchao Hu, Li Shen, Ya Zhang +1
In numerous artificial intelligence applications, the collaborative efforts of multiple intelligent agents are imperative for the successful attainment of target objectives. To enh…
A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs
Yan Sun, Li Shen, Dacheng Tao
As a popular paradigm for juggling data privacy and collaborative training, federated learning (FL) is flourishing to distributively process the large scale of heterogeneous datase…
Divide, Conquer and Combine: A Training-Free Framework for High-Resolution Image Perception in Multimodal Large Language Models
Wenbin Wang, Liang Ding, Minyan Zeng +4
Multimodal large language models (MLLMs) have experienced significant advancements recently, but still struggle to recognize and interpret intricate details in high-resolution (HR)…
Convergent Differential Privacy Analysis for General Federated Learning
Yan Sun, Qixin Zhang, Li Shen +1
The powerful cooperation of federated learning (FL) and differential privacy~(DP) provides a promising paradigm for the large-scale private clients. However, existing analyses in F…
Learning Multi-Agent Communication from Graph Modeling Perspective
Shengchao Hu, Li Shen, Ya Zhang +1
In numerous artificial intelligence applications, the collaborative efforts of multiple intelligent agents are imperative for the successful attainment of target objectives. To enh…
Communication-Efficient Distributed Learning with Local Immediate Error Compensation
Yifei Cheng, Li Shen, Linli Xu +6
Gradient compression with error compensation has attracted significant attention with the target of reducing the heavy communication overhead in distributed learning. However, exis…
Representation Surgery for Multi-Task Model Merging
Enneng Yang, Li Shen, Zhenyi Wang +4
Multi-task learning (MTL) compresses the information from multiple tasks into a unified backbone to improve computational efficiency and generalization. Recent work directly merges…
Solving Continual Offline Reinforcement Learning with Decision Transformer
Kaixin Huang, Li Shen, Chen Zhao +2
Continuous offline reinforcement learning (CORL) combines continuous and offline reinforcement learning, enabling agents to learn multiple tasks from static datasets without forget…