Can pruning improve certified robustness of neural networks?

Can pruning improve certified robustness of neural networks?

15 June 2022

Papers citing "Can pruning improve certified robustness of neural networks?"

10 / 10 papers shown

Title
Layer Pruning with Consensus: A Triple-Win Solution Leandro Giusti Mugnaini Carolina Tavares Duarte Anna Helena Reali Costa Artur Jordao 78 0 0 21 Nov 2024
Verified Relative Safety Margins for Neural Network Twins Anahita Baninajjar Kamran Hosseini Ahmed Rezine A. Aminifar AAML 16 1 0 25 Sep 2024
Efficient DNN-Powered Software with Fair Sparse Models Xuanqi Gao Weipeng Jiang Juan Zhai Shiqing Ma Xiaoyu Zhang Chao Shen 50 0 0 03 Jul 2024
Harnessing Neuron Stability to Improve DNN Verification Hai V. Duong Dong Xu ThanhVu Nguyen Matthew B. Dwyer 24 4 0 19 Jan 2024
Benchmarking Adversarial Robustness of Compressed Deep Learning Models Brijesh Vora Kartik Patwari Syed Mahbub Hafiz Zubair Shafiq Chen-Nee Chuah AAML 27 2 0 16 Aug 2023
A DPLL(T) Framework for Verifying Deep Neural Networks Hai V. Duong Thanh-Dat Nguyen Matthew B. Dwyer 25 8 0 17 Jul 2023
Fully Automatic Neural Network Reduction for Formal Verification Tobias Ladner Matthias Althoff AAML 34 3 0 03 May 2023
A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities Andrea Tocchetti Lorenzo Corti Agathe Balayn Mireia Yurrita Philip Lippmann Marco Brambilla Jie-jin Yang 32 10 0 17 Oct 2022
Pruning Filters while Training for Efficiently Optimizing Deep Learning Networks Sourjya Roy Priyadarshini Panda G. Srinivasan A. Raghunathan 3DPC VLM 29 19 0 05 Mar 2020
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 249 1,842 0 03 Feb 2017