Quantisation and Pruning for Neural Network Compression and Regularisation

14 January 2020

Papers citing "Quantisation and Pruning for Neural Network Compression and Regularisation"

6 / 6 papers shown

Title
Automatic Attention Pruning: Improving and Automating Model Pruning using Attentions Kaiqi Zhao Animesh Jain Ming Zhao 28 10 0 14 Mar 2023
Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks Rakshith Sathish S. Khare Debdoot Sheet 42 4 0 30 Sep 2022
FCN-Pose: A Pruned and Quantized CNN for Robot Pose Estimation for Constrained Devices M. Dantas I. R. R. Silva A. T. O. Filho Gibson B. N. Barbosa Daniel Bezerra D. Sadok J. Kelner M. Marquezini Ricardo F. D. Silva 17 1 0 26 May 2022
Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations Xinyu Zhang Ian Colbert Ken Kreutz-Delgado Srinjoy Das MQ 37 11 0 15 Oct 2021
Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression Canwen Xu Wangchunshu Zhou Tao Ge Kelvin J. Xu Julian McAuley Furu Wei 21 41 0 07 Sep 2021
Benchmarking Quantized Neural Networks on FPGAs with FINN Quentin Ducasse Pascal Cotret Loïc Lagadec Rob Stewart MQ 27 13 0 02 Feb 2021