v1v2 (latest)

Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization

12 June 2024

Papers citing "Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization"

22 / 22 papers shown

Title
Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes Ruiqi Zhang Jingfeng Wu Licong Lin Peter L. Bartlett 70 2 0 05 Apr 2025
Understanding the Generalization Benefits of Late Learning Rate Decay Yinuo Ren Chao Ma Lexing Ying AI4CE 70 6 0 21 Jan 2024
Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult Yuqing Wang Zhenghao Xu Tuo Zhao Molei Tao 72 11 0 26 Oct 2023
Learning threshold neurons via the "edge of stability" Kwangjun Ahn Sébastien Bubeck Sinho Chewi Y. Lee Felipe Suarez Yi Zhang MLT 79 41 0 14 Dec 2022
Understanding Edge-of-Stability Training Dynamics with a Minimalist Example Xingyu Zhu Zixuan Wang Xiang Wang Mo Zhou Rong Ge 107 39 0 07 Oct 2022
Analyzing Sharpness along GD Trajectory: Progressive Sharpening and Edge of Stability Z. Li Zixuan Wang Jian Li 53 47 0 26 Jul 2022
Beyond the Edge of Stability via Two-step Gradient Updates Lei Chen Joan Bruna MLT 35 10 0 08 Jun 2022
Understanding the unstable convergence of gradient descent Kwangjun Ahn J.N. Zhang S. Sra 74 62 0 03 Apr 2022
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect Yuqing Wang Minshuo Chen T. Zhao Molei Tao AI4CE 98 42 0 07 Oct 2021
Fast Margin Maximization via Dual Acceleration Ziwei Ji Nathan Srebro Matus Telgarsky 53 39 0 01 Jul 2021
When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations? Niladri S. Chatterji Philip M. Long Peter L. Bartlett 23 12 0 09 Feb 2021
Implicit Gradient Regularization David Barrett Benoit Dherin 73 151 0 23 Sep 2020
Directional convergence and alignment in deep learning Ziwei Ji Matus Telgarsky 59 171 0 11 Jun 2020
Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss Lénaïc Chizat Francis R. Bach MLT 124 339 0 11 Feb 2020
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks Kaifeng Lyu Jian Li 89 336 0 13 Jun 2019
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 227 1,275 0 04 Oct 2018
Implicit Bias of Gradient Descent on Linear Convolutional Networks Suriya Gunasekar Jason D. Lee Daniel Soudry Nathan Srebro MDE 124 413 0 01 Jun 2018
A Mean Field View of the Landscape of Two-Layers Neural Networks Song Mei Andrea Montanari Phan-Minh Nguyen MLT 98 861 0 18 Apr 2018
Risk and parameter convergence of logistic regression Ziwei Ji Matus Telgarsky 73 130 0 20 Mar 2018
Convergence of Gradient Descent on Separable Data Mor Shpigel Nacson Jason D. Lee Suriya Gunasekar Pedro H. P. Savarese Nathan Srebro Daniel Soudry 73 169 0 05 Mar 2018
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour Priya Goyal Piotr Dollár Ross B. Girshick P. Noordhuis Lukasz Wesolowski Aapo Kyrola Andrew Tulloch Yangqing Jia Kaiming He 3DH 128 3,685 0 08 Jun 2017
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 427 2,945 0 15 Sep 2016