Unifying Low Dimensional Observations in Deep Learning Through the Deep Linear Unconstrained Feature Model

9 April 2024

Papers citing "Unifying Low Dimensional Observations in Deep Learning Through the Deep Linear Unconstrained Feature Model"

7 / 7 papers shown

Title
Unraveling the Hessian: A Key to Smooth Convergence in Loss Function Landscapes Nikita Kiselev Andrey Grabovoy 51 1 0 18 Sep 2024
Linguistic Collapse: Neural Collapse in (Large) Language Models Robert Wu V. Papyan 48 12 0 28 May 2024
Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection Yingwen Wu Ruiji Yu Xinwen Cheng Zhengbao He Xiaolin Huang OODD 72 1 0 28 May 2024
Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data Hien Dang Tho Tran T. Nguyen Hung The Tran Nhat Ho Hung Tran 37 28 0 01 Jan 2023
An Unconstrained Layer-Peeled Perspective on Neural Collapse Wenlong Ji Yiping Lu Yiliang Zhang Zhun Deng Weijie J. Su 132 83 0 06 Oct 2021
Exploring Deep Neural Networks via Layer-Peeled Model: Minority Collapse in Imbalanced Training Cong Fang Hangfeng He Qi Long Weijie J. Su FAtt 127 165 0 29 Jan 2021
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 179 1,185 0 30 Nov 2014