SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural
Networks

SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks

11 October 2019

Lucas Liebenwein

Igor Gilitschenski

Daniela Rus

Papers citing "SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks"

7 / 7 papers shown

Title
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments Angie Boggust Venkatesh Sivaraman Yannick Assogba Donghao Ren Dominik Moritz Fred Hohman VLM 63 3 0 06 Aug 2024
Pruning has a disparate impact on model accuracy Cuong Tran Ferdinando Fioretto Jung-Eun Kim Rakshit Naidu 48 38 0 26 May 2022
Revisiting Random Channel Pruning for Neural Network Compression Yawei Li Kamil Adamczewski Wen Li Shuhang Gu Radu Timofte Luc Van Gool 37 84 0 11 May 2022
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks Shuai Zhang Meng Wang Sijia Liu Pin-Yu Chen Jinjun Xiong UQCV MLT 31 13 0 12 Oct 2021
Sparse Flows: Pruning Continuous-depth Models Lucas Liebenwein Ramin Hasani Alexander Amini Daniela Rus 26 16 0 24 Jun 2021
A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness James Diffenderfer Brian Bartoldson Shreya Chaganti Jize Zhang B. Kailkhura OOD 31 69 0 16 Jun 2021
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy Lucas Liebenwein Cenk Baykal Brandon Carter David K Gifford Daniela Rus AAML 42 71 0 04 Mar 2021