2 citations · 4 across the 4 of their papers we have counts for
4 papers · 1 filter
LightSAM: Parameter-Agnostic Sharpness-Aware Minimization
Yifei Cheng, Li Shen, Hao Sun +3
Sharpness-Aware Minimization (SAM) optimizer enhances the generalization ability of the machine learning model by exploring the flat minima landscape through weight perturbations.…
Merging Multi-Task Models via Weight-Ensembling Mixture of Experts
Anke Tang, Li Shen, Yong Luo +3
Merging various task-specific Transformer-based models trained on different tasks into a single unified model can execute all the tasks concurrently. Previous methods, exemplified…
Asymmetrically Decentralized Federated Learning
Qinglun Li, Miao Zhang, Nan Yin +2
To address the communication burden and privacy concerns associated with the centralized server in Federated Learning (FL), Decentralized Federated Learning (DFL) has emerged, whic…
Continual Learning From a Stream of APIs
Enneng Yang, Zhenyi Wang, Li Shen +5
Continual learning (CL) aims to learn new tasks without forgetting previous tasks. However, existing CL methods require a large amount of raw data, which is often unavailable due t…