Learning low-precision neural networks without Straight-Through Estimator(STE)

4 March 2019

Papers citing "Learning low-precision neural networks without Straight-Through Estimator(STE)"

9 / 9 papers shown

Title
Patch-wise Mixed-Precision Quantization of Vision Transformer Junrui Xiao Zhikai Li Lianwei Yang Qingyi Gu MQ 27 12 0 11 May 2023
QFT: Post-training quantization via fast joint finetuning of all degrees of freedom Alexander Finkelstein Ella Fuchs Idan Tal Mark Grobman Niv Vosco Eldad Meller MQ 21 6 0 05 Dec 2022
Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution Yushu Wu Yifan Gong Pu Zhao Yanyu Li Zheng Zhan Wei Niu Hao Tang Minghai Qin Bin Ren Yanzhi Wang SupR MQ 27 23 0 25 Jul 2022
Differentiable Model Compression via Pseudo Quantization Noise Alexandre Défossez Yossi Adi Gabriel Synnaeve DiffM MQ 10 46 0 20 Apr 2021
Learnable Companding Quantization for Accurate Low-bit Neural Networks Kohei Yamamoto MQ 22 63 0 12 Mar 2021
Pruning and Quantization for Deep Neural Network Acceleration: A Survey Tailin Liang C. Glossner Lei Wang Shaobo Shi Xiaotong Zhang MQ 124 673 0 24 Jan 2021
Efficient Residue Number System Based Winograd Convolution Zhi-Gang Liu Matthew Mattina 20 12 0 23 Jul 2020
Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On Microcontrollers Manuele Rusci Alessandro Capotondi Luca Benini MQ 11 74 0 30 May 2019
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights Aojun Zhou Anbang Yao Yiwen Guo Lin Xu Yurong Chen MQ 313 1,047 0 10 Feb 2017