Stochastic Training is Not Necessary for Generalization

29 September 2021

Papers citing "Stochastic Training is Not Necessary for Generalization"

25 / 25 papers shown

Title
Gradient Descent as a Shrinkage Operator for Spectral Bias Simon Lucey 38 0 0 25 Apr 2025
Can Optimization Trajectories Explain Multi-Task Transfer? David Mueller Mark Dredze Nicholas Andrews 55 1 0 26 Aug 2024
Just How Flexible are Neural Networks in Practice? Ravid Shwartz-Ziv Micah Goldblum Arpit Bansal C. B. Bruss Yann LeCun Andrew Gordon Wilson 40 4 0 17 Jun 2024
Enhancing Domain Adaptation through Prompt Gradient Alignment Hoang Phan Lam C. Tran Quyen Tran Trung Le 52 0 0 13 Jun 2024
Neural Redshift: Random Networks are not Random Functions Damien Teney A. Nicolicioiu Valentin Hartmann Ehsan Abbasnejad 94 18 0 04 Mar 2024
Why Does Little Robustness Help? Understanding and Improving Adversarial Transferability from Surrogate Training Yechao Zhang Shengshan Hu Leo Yu Zhang Junyu Shi Minghui Li Xiaogeng Liu Wei Wan Hai Jin AAML 22 21 0 15 Jul 2023
DropCompute: simple and more robust distributed synchronous training via compute variance reduction Niv Giladi Shahar Gottlieb Moran Shkolnik A. Karnieli Ron Banner Elad Hoffer Kfir Y. Levy Daniel Soudry 25 2 0 18 Jun 2023
Correlated Noise in Epoch-Based Stochastic Gradient Descent: Implications for Weight Variances Marcel Kühn B. Rosenow 11 3 0 08 Jun 2023
Do deep neural networks have an inbuilt Occam's razor? Chris Mingard Henry Rees Guillermo Valle Pérez A. Louis UQCV BDL 19 15 0 13 Apr 2023
mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization Kayhan Behdin Qingquan Song Aman Gupta S. Keerthi Ayan Acharya Borja Ocejo Gregory Dexter Rajiv Khanna D. Durfee Rahul Mazumder AAML 15 7 0 19 Feb 2023
U-Clip: On-Average Unbiased Stochastic Gradient Clipping Bryn Elesedy Marcus Hutter 11 1 0 06 Feb 2023
On a continuous time model of gradient descent dynamics and instability in deep learning Mihaela Rosca Yan Wu Chongli Qin Benoit Dherin 16 6 0 03 Feb 2023
Disentangling the Mechanisms Behind Implicit Regularization in SGD Zachary Novack Simran Kaur Tanya Marwah Saurabh Garg Zachary Chase Lipton FedML 27 2 0 29 Nov 2022
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization Sanae Lotfi Marc Finzi Sanyam Kapoor Andres Potapczynski Micah Goldblum A. Wilson BDL MLT AI4CE 24 51 0 24 Nov 2022
Two Facets of SDE Under an Information-Theoretic Lens: Generalization of SGD via Training Trajectories and via Terminal States Ziqiao Wang Yongyi Mao 21 10 0 19 Nov 2022
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization Jonas Geiping Micah Goldblum Gowthami Somepalli Ravid Shwartz-Ziv Tom Goldstein A. Wilson 26 35 0 12 Oct 2022
Deep Double Descent via Smooth Interpolation Matteo Gamba Erik Englesson Marten Bjorkman Hossein Azizpour 59 10 0 21 Sep 2022
Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD Konstantinos E. Nikolakakis Farzin Haddadpour Amin Karbasi Dionysios S. Kalogerias 34 17 0 26 Apr 2022
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence) Jiayuan Ye Reza Shokri FedML 22 44 0 10 Mar 2022
When Do Flat Minima Optimizers Work? Jean Kaddour Linqing Liu Ricardo M. A. Silva Matt J. Kusner ODL 11 58 0 01 Feb 2022
Subspace Adversarial Training Tao Li Yingwen Wu Sizhe Chen Kun Fang Xiaolin Huang AAML OOD 36 56 0 24 Nov 2021
Logit Attenuating Weight Normalization Aman Gupta R. Ramanath Jun Shi Anika Ramachandran Sirou Zhou Mingzhou Zhou S. Keerthi 34 1 0 12 Aug 2021
The large learning rate phase of deep learning: the catapult mechanism Aitor Lewkowycz Yasaman Bahri Ethan Dyer Jascha Narain Sohl-Dickstein Guy Gur-Ari ODL 159 234 0 04 Mar 2020
Bag of Tricks for Image Classification with Convolutional Neural Networks Tong He Zhi-Li Zhang Hang Zhang Zhongyue Zhang Junyuan Xie Mu Li 221 1,399 0 04 Dec 2018
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 2,888 0 15 Sep 2016