Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions

19 January 2023

Papers citing "Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions"

7 / 7 papers shown

Title
Tightening convex relaxations of trained neural networks: a unified approach for convex and S-shaped activations Pablo Carrasco Gonzalo Muñoz 59 2 0 30 Oct 2024
Optimization Over Trained Neural Networks: Taking a Relaxing Walk Jiatai Tong Junyang Cai Thiago Serra 60 6 0 07 Jan 2024
Computational Tradeoffs of Optimization-Based Bound Tightening in ReLU Networks Fabian Badilla Marcos Goycoolea Gonzalo Muñoz Thiago Serra 57 7 0 27 Dec 2023
When Deep Learning Meets Polyhedral Theory: A Survey Joey Huchette Gonzalo Muñoz Thiago Serra Calvin Tsay AI4CE 91 32 0 29 Apr 2023
Pruning has a disparate impact on model accuracy Cuong Tran Ferdinando Fioretto Jung-Eun Kim Rakshit Naidu 32 38 0 26 May 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
What is the State of Neural Network Pruning? Davis W. Blalock Jose Javier Gonzalez Ortiz Jonathan Frankle John Guttag 178 1,027 0 06 Mar 2020