Understanding the effect of sparsity on neural networks robustness

Understanding the effect of sparsity on neural networks robustness

22 June 2022

Behnam Neyshabur

Papers citing "Understanding the effect of sparsity on neural networks robustness"

11 / 11 papers shown

Title
Holistic Adversarially Robust Pruning Qi Zhao Christian Wressnegger 77 8 0 19 Dec 2024
Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness Boqian Wu Q. Xiao Shunxin Wang N. Strisciuglio Mykola Pechenizkiy M. V. Keulen D. Mocanu Elena Mocanu OOD 3DH 52 0 0 03 Oct 2024
REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints Francesco Corti Balz Maag Joachim Schauer U. Pferschy O. Saukh 24 2 0 22 Nov 2023
Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review M. Lê Pierre Wolinski Julyan Arbel 32 8 0 20 Nov 2023
Sparse Mixture Once-for-all Adversarial Training for Efficient In-Situ Trade-Off Between Accuracy and Robustness of DNNs Souvik Kundu Sairam Sundaresan S. N. Sridhar Shunlin Lu Han Tang P. Beerel AAML MoE 36 4 0 27 Dec 2022
Studying the impact of magnitude pruning on contrastive learning methods Francesco Corti R. Entezari Sara Hooker D. Bacciu O. Saukh 11 5 0 01 Jul 2022
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden Alexandra Peste MQ 139 684 0 31 Jan 2021
Towards Learning Convolutions from Scratch Behnam Neyshabur SSL 216 71 0 27 Jul 2020
What is the State of Neural Network Pruning? Davis W. Blalock Jose Javier Gonzalez Ortiz Jonathan Frankle John Guttag 183 1,027 0 06 Mar 2020
Comparing Rewinding and Fine-tuning in Neural Network Pruning Alex Renda Jonathan Frankle Michael Carbin 222 382 0 05 Mar 2020
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 254 5,833 0 08 Jul 2016