Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model

22 May 2023

Papers citing "Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model"

10 / 10 papers shown

Title
Position: Solve Layerwise Linear Models First to Understand Neural Dynamical Phenomena (Neural Collapse, Emergence, Lazy/Rich Regime, and Grokking) Yoonsoo Nam Seok Hyeong Lee Clementine Domine Yea Chan Park Charles London Wonyl Choi Niclas Goring Seungjai Lee AI4CE 146 1 0 28 Feb 2025
No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier Zexi Li Xinyi Shang Rui He Tao R. Lin Chao Wu FedML 81 55 0 17 Mar 2023
Are All Losses Created Equal: A Neural Collapse Perspective Jinxin Zhou Chong You Xiao Li Kangning Liu Sheng Liu Qing Qu Zhihui Zhu 64 64 0 04 Oct 2022
Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold Can Yaras Peng Wang Zhihui Zhu Laura Balzano Qing Qu 43 44 0 19 Sep 2022
Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks J. Haas William Yolland B. Rabus OODD 56 18 0 17 Sep 2022
Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations Jiayao Zhang Hua Wang Weijie J. Su 65 8 0 11 Oct 2021
An Unconstrained Layer-Peeled Perspective on Neural Collapse Wenlong Ji Yiping Lu Yiliang Zhang Zhun Deng Weijie J. Su 161 86 0 06 Oct 2021
Neural collapse with unconstrained features D. Mixon Hans Parshall Jianzong Pi 59 120 0 23 Nov 2020
Prevalence of Neural Collapse during the terminal phase of deep learning training Vardan Papyan Xuemei Han D. Donoho 178 572 0 18 Aug 2020
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification Kaiming He Xinming Zhang Shaoqing Ren Jian Sun VLM 288 18,587 0 06 Feb 2015