6 citations · 6 across the 5 of their papers we have counts for
4 papers · 1 filter
BoHA: Blockwise Hadamard Product Adaptation for Parameter-Efficient Fine-Tuning
Feng Yu, Jia Hu, Geyong Min
Parameter-efficient fine-tuning (PEFT) of large language models trains a small task-specific parameter set while keeping the pretrained model frozen. The dominant Low-Rank Adaptati…
Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning
Feng Yu, Jia Hu, Geyong Min
Federated Parameter-Efficient Fine-Tuning (Fed-PEFT) enables lightweight adaptation of large pre-trained models in federated learning settings by updating only a small subset of pa…
Federated Continual Learning for Edge-AI: A Comprehensive Survey
Zi Wang, Fei Wu, Feng Yu +3
Edge-AI, the convergence of edge computing and artificial intelligence (AI), has become a promising paradigm that enables the deployment of advanced AI models at the network edge,…
Hyperparameter Estimation for Sparse Bayesian Learning Models
Feng Yu, Lixin Shen, Guohui Song
Sparse Bayesian Learning (SBL) models are extensively used in signal processing and machine learning for promoting sparsity through hierarchical priors. The hyperparameters in SBL…