Embedding Principle: a hierarchical structure of loss landscape of deep neural networks

30 November 2021

Papers citing "Embedding Principle: a hierarchical structure of loss landscape of deep neural networks"

9 / 9 papers shown

Title
Connectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion Zhiwei Bai Jiajie Zhao Yaoyu Zhang AI4CE 32 0 0 22 May 2024
Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks Arnulf Jentzen Adrian Riekert 38 4 0 07 Feb 2024
Understanding the Initial Condensation of Convolutional Neural Networks Zhangchen Zhou Hanxu Zhou Yuqing Li Zhi-Qin John Xu MLT AI4CE 23 5 0 17 May 2023
Phase Diagram of Initial Condensation for Two-layer Neural Networks Zheng Chen Yuqing Li Tao Luo Zhaoguang Zhou Z. Xu MLT AI4CE 41 8 0 12 Mar 2023
Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks Zhiwei Bai Tao Luo Z. Xu Yaoyu Zhang 23 4 0 26 May 2022
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
Overview frequency principle/spectral bias in deep learning Z. Xu Yaoyu Zhang Tao Luo FaML 25 65 0 19 Jan 2022
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 278 2,888 0 15 Sep 2016
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