Applications of Pruning Methods in Natural Language Processing-Reference-Cited by-同舟云学术

Applications of Pruning Methods in Natural Language Processing

Published:2024 Issue: Volume:12 Page:89418-89438
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Touheed Marva¹^ORCID,Zubair Urooj¹^ORCID,Sabir Dilshad¹^ORCID,Hassan Ali²^ORCID,Butt Muhammad Fasih Uddin¹^ORCID,Riaz Farhan³,Abdul Wadood⁴,Ayub Rashid⁵^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan

2. Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad, Pakistan

3. School of Computer Science, College of Health and Science, University of Lincoln, Lincoln, U.K.

4. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

5. Department of Science Technology and Innovation, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

Funder

Researchers Supporting Project number

King Saud University, Riyadh, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10552272.pdf?arnumber=10552272

Reference89 articles.

1. Survey on Comparative Study of Pruning Mechanism on MobileNetV3 Model

2. GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference

3. Deep rewiring: Training very sparse deep networks;Bellec;arXiv:1711.05136,2017

4. Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding;Han;arXiv:1510.00149,2015

5. Stochastically Pruning Large Language Models Using Sparsity Regularization and Compressive Sensing