An Investigation into Neural Net Optimization via Hessian Eigenvalue Density

29 January 2019

Papers citing "An Investigation into Neural Net Optimization via Hessian Eigenvalue Density"

40 / 40 papers shown

Title
KerZOO: Kernel Function Informed Zeroth-Order Optimization for Accurate and Accelerated LLM Fine-Tuning Zhendong Mi Qitao Tan Xiaodong Yu Zining Zhu Geng Yuan Shaoyi Huang 200 0 0 24 May 2025
Accelerating Neural Network Training Along Sharp and Flat Directions Daniyar Zakarin Sidak Pal Singh ODL 65 0 0 17 May 2025
DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization Youngjun Song Youngsik Hwang Jonghun Lee Heechang Lee Dong-Young Lim AAML 106 0 0 30 Mar 2025
A Hessian-informed hyperparameter optimization for differential learning rate Shiyun Xu Zhiqi Bu Yiliang Zhang Ian Barnett 96 1 0 12 Jan 2025
Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis Zhijie Chen Qiaobo Li A. Banerjee FedML 90 0 0 11 Nov 2024
Theoretical characterisation of the Gauss-Newton conditioning in Neural Networks Jim Zhao Sidak Pal Singh Aurelien Lucchi AI4CE 113 0 0 04 Nov 2024
Debiasing Mini-Batch Quadratics for Applications in Deep Learning Lukas Tatzel Bálint Mucsányi Osane Hackel Philipp Hennig 86 0 0 18 Oct 2024
What Does It Mean to Be a Transformer? Insights from a Theoretical Hessian Analysis Weronika Ormaniec Felix Dangel Sidak Pal Singh 113 7 0 14 Oct 2024
Fast Training of Sinusoidal Neural Fields via Scaling Initialization Taesun Yeom Sangyoon Lee Jaeho Lee 96 3 0 07 Oct 2024
Loss Gradient Gaussian Width based Generalization and Optimization Guarantees A. Banerjee Qiaobo Li Yingxue Zhou 125 0 0 11 Jun 2024
Does SGD really happen in tiny subspaces? Minhak Song Kwangjun Ahn Chulhee Yun 100 6 1 25 May 2024
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization Zhe Li Bicheng Ying Zidong Liu Chaosheng Dong Haibo Yang FedML 118 3 0 24 May 2024
High dimensional analysis reveals conservative sharpening and a stochastic edge of stability Atish Agarwala Jeffrey Pennington 91 4 0 30 Apr 2024
Second-Order Fine-Tuning without Pain for LLMs:A Hessian Informed Zeroth-Order Optimizer Yanjun Zhao Sizhe Dang Haishan Ye Guang Dai Yi Qian Ivor W.Tsang 118 13 0 23 Feb 2024
Generalisation under gradient descent via deterministic PAC-Bayes Eugenio Clerico Tyler Farghly George Deligiannidis Benjamin Guedj Arnaud Doucet 92 4 0 06 Sep 2022
MIO : Mutual Information Optimization using Self-Supervised Binary Contrastive Learning Siladittya Manna Umapada Pal Saumik Bhattacharya SSL 102 1 0 24 Nov 2021
Beyond Random Matrix Theory for Deep Networks Diego Granziol 105 16 0 13 Jun 2020
Concentration of quadratic forms under a Bernstein moment assumption Pierre C. Bellec 88 21 0 25 Jan 2019
Gradient Descent Happens in a Tiny Subspace Guy Gur-Ari Daniel A. Roberts Ethan Dyer 100 233 0 12 Dec 2018
The Full Spectrum of Deepnet Hessians at Scale: Dynamics with SGD Training and Sample Size Vardan Papyan 59 31 0 16 Nov 2018
The Goldilocks zone: Towards better understanding of neural network loss landscapes Stanislav Fort Adam Scherlis 66 50 0 06 Jul 2018
How Does Batch Normalization Help Optimization? Shibani Santurkar Dimitris Tsipras Andrew Ilyas Aleksander Madry ODL 100 1,544 0 29 May 2018
Measuring the Intrinsic Dimension of Objective Landscapes Chunyuan Li Heerad Farkhoor Rosanne Liu J. Yosinski 86 414 0 24 Apr 2018
Essentially No Barriers in Neural Network Energy Landscape Felix Dräxler K. Veschgini M. Salmhofer Fred Hamprecht MoMe 116 434 0 02 Mar 2018
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries Z. Yao A. Gholami Qi Lei Kurt Keutzer Michael W. Mahoney 69 167 0 22 Feb 2018
An Alternative View: When Does SGD Escape Local Minima? Robert D. Kleinberg Yuanzhi Li Yang Yuan MLT 77 317 0 17 Feb 2018
Estimating the Spectral Density of Large Implicit Matrices Ryan P. Adams Jeffrey Pennington Matthew J. Johnson Jamie Smith Yaniv Ovadia Brian Patton J. Saunderson 72 34 0 09 Feb 2018
Visualizing the Loss Landscape of Neural Nets Hao Li Zheng Xu Gavin Taylor Christoph Studer Tom Goldstein 254 1,896 0 28 Dec 2017
Three Factors Influencing Minima in SGD Stanislaw Jastrzebski Zachary Kenton Devansh Arpit Nicolas Ballas Asja Fischer Yoshua Bengio Amos Storkey 76 463 0 13 Nov 2017
Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes Lei Wu Zhanxing Zhu E. Weinan ODL 64 221 0 30 Jun 2017
Empirical Analysis of the Hessian of Over-Parametrized Neural Networks Levent Sagun Utku Evci V. U. Güney Yann N. Dauphin Léon Bottou 54 418 0 14 Jun 2017
Sharp Minima Can Generalize For Deep Nets Laurent Dinh Razvan Pascanu Samy Bengio Yoshua Bengio ODL 122 774 0 15 Mar 2017
Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond Levent Sagun Léon Bottou Yann LeCun UQCV 91 236 0 22 Nov 2016
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
TensorFlow: A system for large-scale machine learning Martín Abadi P. Barham Jianmin Chen Zhiwen Chen Andy Davis ... Vijay Vasudevan Pete Warden Martin Wicke Yuan Yu Xiaoqiang Zhang GNN AI4CE 433 18,361 0 27 May 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,322 0 10 Dec 2015
Rethinking the Inception Architecture for Computer Vision Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens Z. Wojna 3DV BDL 886 27,412 0 02 Dec 2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 463 43,328 0 11 Feb 2015
Qualitatively characterizing neural network optimization problems Ian Goodfellow Oriol Vinyals Andrew M. Saxe ODL 110 523 0 19 Dec 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.7K 100,479 0 04 Sep 2014