Towards Understanding the Condensation of Neural Networks at Initial Training

25 May 2021

Papers citing "Towards Understanding the Condensation of Neural Networks at Initial Training"

6 / 6 papers shown

Title
Gradient Descent Robustly Learns the Intrinsic Dimension of Data in Training Convolutional Neural Networks Chenyang Zhang Peifeng Gao Difan Zou Yuan Cao OOD MLT 59 0 0 11 Apr 2025
pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning Jiahao Lai J. Li Jian Xu Yanru Wu Boshi Tang Siqi Chen Yongfeng Huang Wenbo Ding Yang Li FedML 73 0 0 09 Sep 2024
Connectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion Zhiwei Bai Jiajie Zhao Yaoyu Zhang AI4CE 30 0 0 22 May 2024
Understanding the Initial Condensation of Convolutional Neural Networks Zhangchen Zhou Hanxu Zhou Yuqing Li Zhi-Qin John Xu MLT AI4CE 18 5 0 17 May 2023
Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width Hanxu Zhou Qixuan Zhou Zhenyuan Jin Tao Luo Yaoyu Zhang Zhi-Qin John Xu 22 20 0 24 May 2022
Multi-scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains Ziqi Liu Wei Cai Zhi-Qin John Xu AI4CE 197 156 0 22 Jul 2020