1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit

1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit

26 August 2024

Liping Jing

Papers citing "1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit"

5 / 5 papers shown

Title
Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics Cong Xu Wenbin Liang Mo Yu Anan Liu K. Zhang Lizhuang Ma J. Wang J. Wang W. Zhang MQ 51 0 0 01 May 2025
MLP-Mixer: An all-MLP Architecture for Vision Ilya O. Tolstikhin N. Houlsby Alexander Kolesnikov Lucas Beyer Xiaohua Zhai ... Andreas Steiner Daniel Keysers Jakob Uszkoreit Mario Lucic Alexey Dosovitskiy 239 2,554 0 04 May 2021
BiDet: An Efficient Binarized Object Detector Ziwei Wang Ziyi Wu Jiwen Lu Jie Zhou MQ 44 64 0 09 Mar 2020
Training High-Performance and Large-Scale Deep Neural Networks with Full 8-bit Integers Yukuan Yang Shuang Wu Lei Deng Tianyi Yan Yuan Xie Guoqi Li MQ 99 108 0 05 Sep 2019
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights Aojun Zhou Anbang Yao Yiwen Guo Lin Xu Yurong Chen MQ 300 1,046 0 10 Feb 2017