2 citations · 2 across the 3 of their papers we have counts for
3 papers
stat.ML2025
Gradient Descent Robustly Learns the Intrinsic Dimension of Data in Training Convolutional Neural Networks
Chenyang Zhang, Peifeng Gao, Difan Zou +1
Modern neural networks are usually highly over-parameterized. Behind the wide usage of over-parameterized networks is the belief that, if the data are simple, then the trained netw…
cs.LG2023
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…
cs.LG2023★ 2 cited
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…