2 citations · 2 across the 7 of their papers we have counts for
4 papers · 1 filter
Fine-Tuning Low-Bit Models with Gradient in Quantized Code Space
Shiguang Wu, Zhouchen Lin, Quanming Yao
Fine-tuning Low-bit models aims to adapt a quantized model while keeping the final deployed checkpoint in the same low-bit form. This setting is practically important as it reduces…
Self-Generative Adversarial Fine-Tuning for Large Language Models
Shiguang Wu, Yaqing Wang, Quanming Yao
Fine-tuning large language models (LLMs) for alignment typically relies on supervised fine-tuning or reinforcement learning from human feedback, both limited by the cost and scarci…
Learning to Learn with Contrastive Meta-Objective
Shiguang Wu, Yaqing Wang, Yatao Bian +1
Meta-learning enables learning systems to adapt quickly to new tasks, similar to humans. Different meta-learning approaches all work under/with the mini-batch episodic training fra…
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction
Shiguang Wu, Yaqing Wang, Quanming Yao
Molecular property prediction (MPP) plays a crucial role in biomedical applications, but it often encounters challenges due to a scarcity of labeled data. Existing works commonly a…