A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation

11 June 2022

Papers citing "A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation"

4 / 4 papers shown

Title
Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing Qi Le Enmao Diao Ziyan Wang Xinran Wang Jie Ding Li Yang Ali Anwar 77 2 0 24 Feb 2025
Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization Geng Li G. Wang Jie Ding 34 3 0 07 May 2023
Pruning Deep Neural Networks from a Sparsity Perspective Enmao Diao G. Wang Jiawei Zhan Yuhong Yang Jie Ding Vahid Tarokh 27 30 0 11 Feb 2023
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 141 684 0 31 Jan 2021