4 papers
TabLoRA: Parameter-Efficient Low-Rank Ensemble Learning for Large-Scale Tabular Data
Jiaqi Luo, Shixin Xu
Tabular learning is still dominated by gradient-boosted decision trees (GBDTs), while recent deep learning approaches have become increasingly competitive. However, applying deep t…
Efficient Global-Local Fusion Sampling for Physics-Informed Neural Networks
Jiaqi Luo, Shixin Xu, Zhouwang Yang
The accuracy of Physics-Informed Neural Networks (PINNs) critically depends on the placement of collocation points, as the PDE loss is approximated through sampling over the soluti…
TIME: TabPFN-Integrated Multimodal Engine for Robust Tabular-Image Learning
Jiaqi Luo, Yuan Yuan, Shixin Xu
Tabular-image multimodal learning, which integrates structured tabular data with imaging data, holds great promise for a variety of tasks, especially in medical applications. Yet,…
An Imbalanced Learning-based Sampling Method for Physics-informed Neural Networks
Jiaqi Luo, Yahong Yang, Yuan Yuan +2
This paper introduces Residual-based Smote (RSmote), an innovative local adaptive sampling technique tailored to improve the performance of Physics-Informed Neural Networks (PINNs)…