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Found 6 papers, 3 papers with code
Stackelberg Game Preference Optimization for Data-Efficient Alignment of Language Models
Aligning language models with human preferences is critical for real-world deployment, but existing methods often require large amounts of high-quality human annotations.
IntelliCare: Improving Healthcare Analysis with Variance-Controlled Patient-Level Knowledge from Large Language Models
While pioneering deep learning methods have made great strides in analyzing electronic health record (EHR) data, they often struggle to fully capture the semantics of diverse medical codes from limited data.
SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction
To tackle this problem, we propose SMART, a Self-Supervised Missing-Aware RepresenTation Learning approach for patient health status prediction, which encodes missing information via elaborated attentions and learns to impute missing values through a novel self-supervised pre-training approach that reconstructs missing data representations in the latent space.
LoRA Dropout as a Sparsity Regularizer for Overfitting Control
We then demonstrate the theoretical mechanism of our LoRA Dropout mechanism from the perspective of sparsity regularization by providing a generalization error bound under this framework.
Domain Generalization through the Lens of Angular Invariance
Based on the proposed term of invariance, we propose a novel deep DG method called Angular Invariance Domain Generalization Network (AIDGN).
Sense-Store-Send: Trajectory Optimization for a Buffer-aided Internet of UAVs
To minimize the overall completion time for all the sensing tasks, we formulate a joint trajectory, sensing location, and sensing time optimization problem.
