22 citations · 52 across the 9 of their papers we have counts for
10 papers
Harnessing the Potential of Optimizing Data Mixtures via Bayesian Domain Reweighting
Xiang Yuan, Kaiqing Lei, Zhenyu Jin +3
The performance of Large Language Models (LLMs) is fundamentally influenced by the distributional composition of multi-domain pre-training data. While manual heuristics were preval…
DuMeta++: Spatiotemporal Dual Meta-Learning for Generalizable Few-Shot Brain Tissue Segmentation Across Diverse Ages
Yongheng Sun, Jun Shu, Jianhua Ma +1
Accurate segmentation of brain tissues from MRI scans is critical for neuroscience and clinical applications, but achieving consistent performance across the human lifespan remains…
Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance
Xiang Yuan, Jun Shu, Deyu meng +1
Robust fine-tuning aims to achieve competitive in-distribution (ID) performance while maintaining the out-of-distribution (OOD) robustness of a pre-trained model when transferring…
Improving Memory Efficiency for Training KANs via Meta Learning
Zhangchi Zhao, Jun Shu, Deyu Meng +1
Inspired by the Kolmogorov-Arnold representation theorem, KANs offer a novel framework for function approximation by replacing traditional neural network weights with learnable uni…
Dual Meta-Learning with Longitudinally Generalized Regularization for One-Shot Brain Tissue Segmentation Across the Human Lifespan
Yongheng Sun, Fan Wang, Jun Shu +3
Brain tissue segmentation is essential for neuroscience and clinical studies. However, segmentation on longitudinal data is challenging due to dynamic brain changes across the life…
Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction
Ziyi Yang, Jun Shu, Yong Liang +2
Current machine learning has made great progress on computer vision and many other fields attributed to the large amount of high-quality training samples, while it does not work ve…