On the Power-Law Hessian Spectrums in Deep Learning

On the Power-Law Hessian Spectrums in Deep Learning

31 January 2022

Zeke Xie

Papers citing "On the Power-Law Hessian Spectrums in Deep Learning"

9 / 9 papers shown

Title
Commute Your Domains: Trajectory Optimality Criterion for Multi-Domain Learning Alexey Rukhovich Alexander Podolskiy Irina Piontkovskaya 43 0 0 28 Jan 2025
Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis Zhijie Chen Qiaobo Li A. Banerjee FedML 28 0 0 11 Nov 2024
Unraveling the Hessian: A Key to Smooth Convergence in Loss Function Landscapes Nikita Kiselev Andrey Grabovoy 36 1 0 18 Sep 2024
Loss Gradient Gaussian Width based Generalization and Optimization Guarantees A. Banerjee Qiaobo Li Yingxue Zhou 39 0 0 11 Jun 2024
Stochastic Gradient Flow Dynamics of Test Risk and its Exact Solution for Weak Features Rodrigo Veiga Anastasia Remizova Nicolas Macris 27 0 0 12 Feb 2024
A Precise Characterization of SGD Stability Using Loss Surface Geometry Gregory Dexter Borja Ocejo S. Keerthi Aman Gupta Ayan Acharya Rajiv Khanna MLT 15 0 0 22 Jan 2024
Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training Yefan Zhou Tianyu Pang Keqin Liu Charles H. Martin Michael W. Mahoney Yaoqing Yang 34 7 0 01 Dec 2023
diffGrad: An Optimization Method for Convolutional Neural Networks S. Dubey Soumendu Chakraborty S. K. Roy Snehasis Mukherjee S. Singh B. B. Chaudhuri ODL 62 181 0 12 Sep 2019
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 273 2,878 0 15 Sep 2016