Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians

24 January 2019

Papers citing "Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians"

22 / 22 papers shown

Title
Non-identifiability distinguishes Neural Networks among Parametric Models Sourav Chatterjee Timothy Sudijono 30 0 0 25 Apr 2025
Position: Curvature Matrices Should Be Democratized via Linear Operators Felix Dangel Runa Eschenhagen Weronika Ormaniec Andres Fernandez Lukas Tatzel Agustinus Kristiadi 58 3 0 31 Jan 2025
Unraveling the Hessian: A Key to Smooth Convergence in Loss Function Landscapes Nikita Kiselev Andrey Grabovoy 51 1 0 18 Sep 2024
Loss Gradient Gaussian Width based Generalization and Optimization Guarantees A. Banerjee Qiaobo Li Yingxue Zhou 49 0 0 11 Jun 2024
Does SGD really happen in tiny subspaces? Minhak Song Kwangjun Ahn Chulhee Yun 71 4 1 25 May 2024
Architectural Strategies for the optimization of Physics-Informed Neural Networks Hemanth Saratchandran Shin-Fang Chng Simon Lucey AI4CE 36 0 0 05 Feb 2024
Spectral alignment of stochastic gradient descent for high-dimensional classification tasks Gerard Ben Arous Reza Gheissari Jiaoyang Huang Aukosh Jagannath 27 14 0 04 Oct 2023
On the Overlooked Structure of Stochastic Gradients Zeke Xie Qian-Yuan Tang Mingming Sun P. Li 28 6 0 05 Dec 2022
Analyzing Sharpness along GD Trajectory: Progressive Sharpening and Edge of Stability Z. Li Zixuan Wang Jian Li 19 42 0 26 Jul 2022
High-dimensional limit theorems for SGD: Effective dynamics and critical scaling Gerard Ben Arous Reza Gheissari Aukosh Jagannath 54 58 0 08 Jun 2022
On the Power-Law Hessian Spectrums in Deep Learning Zeke Xie Qian-Yuan Tang Yunfeng Cai Mingming Sun P. Li ODL 42 8 0 31 Jan 2022
Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping Xuran Meng Jianfeng Yao 19 7 0 26 Nov 2021
Does the Data Induce Capacity Control in Deep Learning? Rubing Yang J. Mao Pratik Chaudhari 27 15 0 27 Oct 2021
Appearance of Random Matrix Theory in Deep Learning Nicholas P. Baskerville Diego Granziol J. Keating 15 11 0 12 Feb 2021
A Deeper Look at the Hessian Eigenspectrum of Deep Neural Networks and its Applications to Regularization Adepu Ravi Sankar Yash Khasbage Rahul Vigneswaran V. Balasubramanian 25 41 0 07 Dec 2020
A Framework for Private Matrix Analysis Jalaj Upadhyay Sarvagya Upadhyay 15 4 0 06 Sep 2020
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification Yingxue Zhou Zhiwei Steven Wu A. Banerjee 16 106 0 07 Jul 2020
Directional Pruning of Deep Neural Networks Shih-Kang Chao Zhanyu Wang Yue Xing Guang Cheng ODL 13 33 0 16 Jun 2020
The Break-Even Point on Optimization Trajectories of Deep Neural Networks Stanislaw Jastrzebski Maciej Szymczak Stanislav Fort Devansh Arpit Jacek Tabor Kyunghyun Cho Krzysztof J. Geras 47 154 0 21 Feb 2020
Asymptotics of Wide Networks from Feynman Diagrams Ethan Dyer Guy Gur-Ari 24 113 0 25 Sep 2019
Stiffness: A New Perspective on Generalization in Neural Networks Stanislav Fort Pawel Krzysztof Nowak Stanislaw Jastrzebski S. Narayanan 19 94 0 28 Jan 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 281 2,889 0 15 Sep 2016