4 citations · 10 across the 10 of their papers we have counts for
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SFT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity
Xinyu Yang, Jixuan Leng, Geyang Guo +5
Current PEFT methods for LLMs can achieve either high quality, efficient training, or scalable serving, but not all three simultaneously. To address this limitation, we investigate…
PEANuT: Parameter-Efficient Adaptation with Weight-aware Neural Tweakers
Yibo Zhong, Haoxiang Jiang, Lincan Li +5
Fine-tuning large pre-trained foundation models often yields excellent downstream performance but is prohibitively expensive when updating all parameters. Parameter-efficient fine-…
Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance
Ryumei Nakada, Yichen Xu, Lexin Li +1
Imbalanced classification and spurious correlation are common challenges in data science and machine learning. Both issues are linked to data imbalance, with certain groups of data…
Contrastive Learning on Multimodal Analysis of Electronic Health Records
Tianxi Cai, Feiqing Huang, Ryumei Nakada +2
Electronic health record (EHR) systems capture a wealth of multimodal clinical data, encompassing both structured clinical codes and unstructured clinical notes. Yet, many EHR-focu…