Minimax Optimal Quantization of Linear Models: Information-Theoretic Limits and Efficient Algorithms

23 February 2022

Papers citing "Minimax Optimal Quantization of Linear Models: Information-Theoretic Limits and Efficient Algorithms"

5 / 5 papers shown

Title
An Information-Theoretic Justification for Model Pruning Berivan Isik Tsachy Weissman Albert No 100 36 0 16 Feb 2021
Transform Quantization for CNN (Convolutional Neural Network) Compression Sean I. Young Wang Zhe David S. Taubman B. Girod MQ 38 71 0 02 Sep 2020
Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor M. Safaryan Egor Shulgin Peter Richtárik 37 61 0 20 Feb 2020
Rate Distortion For Model Compression: From Theory To Practice Weihao Gao Yu-Han Liu Chong-Jun Wang Sewoong Oh 35 31 0 09 Oct 2018
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding Song Han Huizi Mao W. Dally 3DGS 135 8,793 0 01 Oct 2015