5 citations · 18 across the 18 of their papers we have counts for
7 papers · 1 filter
The Devil is in the Condition Numbers: Why is GLU Better than non-GLU Structure?
Xingyu Lyu, Qianqian Xu, Zhiyong Yang +2
Gated Linear Units (GLU) and their variants are widely adopted in modern open-source large language model architectures and consistently outperform their non-gated counterparts, ye…
TuckA: Hierarchical Compact Tensor Experts for Efficient Fine-Tuning
Qifeng Lei, Zhiyong Yang, Qianqian Xu +3
Efficiently fine-tuning pre-trained models for downstream tasks is a key challenge in the era of foundation models. Parameter-efficient fine-tuning (PEFT) presents a promising solu…
Top-K Pairwise Ranking: Bridging the Gap Among Ranking-Based Measures for Multi-Label Classification
Zitai Wang, Qianqian Xu, Zhiyong Yang +4
Multi-label ranking, which returns multiple top-ranked labels for each instance, has a wide range of applications for visual tasks. Due to its complicated setting, prior arts have…
Towards Demystifying the Generalization Behaviors When Neural Collapse Emerges
Peifeng Gao, Qianqian Xu, Yibo Yang +5
Neural Collapse (NC) is a well-known phenomenon of deep neural networks in the terminal phase of training (TPT). It is characterized by the collapse of features and classifier into…
A Study of Neural Collapse Phenomenon: Grassmannian Frame, Symmetry and Generalization
Peifeng Gao, Qianqian Xu, Peisong Wen +3
In this paper, we extend original Neural Collapse Phenomenon by proving Generalized Neural Collapse hypothesis. We obtain Grassmannian Frame structure from the optimization and gen…
Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective
Yunrui Zhao, Qianqian Xu, Yangbangyan Jiang +2
Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive examples with many unlabeled ones. Compared with ordinary semi-supervised learning, t…
Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stability
Peisong Wen, Qianqian Xu, Zhiyong Yang +2
Stochastic optimization of the Area Under the Precision-Recall Curve (AUPRC) is a crucial problem for machine learning. Although various algorithms have been extensively studied fo…