2 citations · 5 across the 4 of their papers we have counts for
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cs.LG2024
Enhancing Parameter Efficiency and Generalization in Large-Scale Models: A Regularized and Masked Low-Rank Adaptation Approach
Yuzhu Mao, Siqi Ping, Zihao Zhao +2
Large pre-trained models, such as large language models (LLMs), present significant resource challenges for fine-tuning due to their extensive parameter sizes, especially for appli…
cs.LG2024★ 2 cited
FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clustering
Siqi Ping, Yuzhu Mao, Yang Liu +2
Although large-scale pre-trained models hold great potential for adapting to downstream tasks through fine-tuning, the performance of such fine-tuned models is often limited by the…
cs.LG2023★ 2 cited
Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Prompt
Zihao Zhao, Zhenpeng Shi, Yang Liu +1
Federated Learning (FL) is often impeded by communication overhead issues. Prompt tuning, as a potential solution, has been introduced to only adjust a few trainable parameters rat…