5 papers
GAST: Gradient-aligned Sparse Tuning of Large Language Models with Data-layer Selection
Kai Yao, Zhenghan Song, Kaixin Wu +5
Parameter-Efficient Fine-Tuning (PEFT) has become a key strategy for adapting large language models, with recent advances in sparse tuning reducing overhead by selectively updating…
Deep Survival Analysis for Competing Risk Modeling with Functional Covariates and Missing Data Imputation
Penglei Gao, Yan Zou, Abhijit Duggal +3
We introduce the Functional Competing Risk Net (FCRN), a unified deep-learning framework for discrete-time survival analysis under competing risks, which seamlessly integrates func…
GradOT: Training-free Gradient-preserving Offsite-tuning for Large Language Models
Kai Yao, Zhaorui Tan, Penglei Gao +7
The rapid growth of large language models (LLMs) with traditional centralized fine-tuning emerges as a key technique for adapting these models to domain-specific challenges, yieldi…
Layer-wise Importance Matters: Less Memory for Better Performance in Parameter-efficient Fine-tuning of Large Language Models
Kai Yao, Penglei Gao, Lichun Li +4
Parameter-Efficient Fine-Tuning (PEFT) methods have gained significant popularity for adapting pre-trained Large Language Models (LLMs) to downstream tasks, primarily due to their…
OPa-Ma: Text Guided Mamba for 360-degree Image Out-painting
Penglei Gao, Kai Yao, Tiandi Ye +3
In this paper, we tackle the recently popular topic of generating 360-degree images given the conventional narrow field of view (NFoV) images that could be taken from a single came…