An Information-Theoretic Justification for Model Pruning

16 February 2021

Papers citing "An Information-Theoretic Justification for Model Pruning"

10 / 10 papers shown

Title
LPViT: Low-Power Semi-structured Pruning for Vision Transformers Kaixin Xu Zhe Wang Chunyun Chen Xue Geng Jie Lin Xulei Yang Min-man Wu Min Wu Xiaoli Li Weisi Lin ViT VLM 49 7 0 02 Jul 2024
NOLA: Compressing LoRA using Linear Combination of Random Basis Soroush Abbasi Koohpayegani K. Navaneet Parsa Nooralinejad Soheil Kolouri Hamed Pirsiavash 40 12 0 04 Oct 2023
Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation Berivan Isik Wei-Ning Chen Ayfer Özgür Tsachy Weissman Albert No 53 19 0 08 Jun 2023
Rotation Invariant Quantization for Model Compression Dor-Joseph Kampeas Yury Nahshan Hanoch Kremer Gil Lederman Shira Zaloshinski Zheng Li E. Haleva MQ 23 0 0 03 Mar 2023
M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-Distortion Yangyi Liu Stefano Rini Sadaf Salehkalaibar Jun Chen FedML 11 4 0 23 Jan 2023
Rate-Constrained Remote Contextual Bandits Francesco Pase Deniz Gündüz M. Zorzi 22 8 0 26 Apr 2022
Minimax Optimal Quantization of Linear Models: Information-Theoretic Limits and Efficient Algorithms R. Saha Mert Pilanci Andrea J. Goldsmith MQ 22 3 0 23 Feb 2022
LVAC: Learned Volumetric Attribute Compression for Point Clouds using Coordinate Based Networks Berivan Isik P. Chou S. Hwang Nick Johnston G. Toderici 3DPC 26 28 0 17 Nov 2021
What is the State of Neural Network Pruning? Davis W. Blalock Jose Javier Gonzalez Ortiz Jonathan Frankle John Guttag 191 1,027 0 06 Mar 2020
Universal Deep Neural Network Compression Yoojin Choi Mostafa El-Khamy Jungwon Lee MQ 81 85 0 07 Feb 2018