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Compression of Deep Learning Models for Text: A Survey

12 August 2020

Papers citing "Compression of Deep Learning Models for Text: A Survey"

21 / 21 papers shown

Title
Knowledge Distillation Framework for Accelerating High-Accuracy Neural Network-Based Molecular Dynamics Simulations Naoki Matsumura Yuta Yoshimoto Yuto Iwasaki Meguru Yamazaki Yasufumi Sakai 5 0 0 18 Jun 2025
As easy as PIE: understanding when pruning causes language models to disagree Pietro Tropeano Maria Maistro Tuukka Ruotsalo Christina Lioma 91 0 0 27 Mar 2025
Knowledge Augmentation in Federation: Rethinking What Collaborative Learning Can Bring Back to Decentralized Data Wentai Wu Ligang He Saiqin Long Ahmed M. Abdelmoniem Yingliang Wu Rui Mao 126 0 0 05 Mar 2025
MoDeGPT: Modular Decomposition for Large Language Model Compression Chi-Heng Lin Shangqian Gao James Seale Smith Abhishek Patel Shikhar Tuli Yilin Shen Hongxia Jin Yen-Chang Hsu 151 13 0 19 Aug 2024
Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging Deyuan Liu Zhan Qin Han Wang Zhao Yang Zecheng Wang ... Zhao Lv Zhiying Tu Dianhui Chu Bo Li Dianbo Sui 113 2 0 24 Jun 2024
When Quantization Affects Confidence of Large Language Models? Irina Proskurina Luc Brun Guillaume Metzler Julien Velcin MQ 117 2 0 01 May 2024
Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation Yun-Wei Chu Dong-Jun Han Christopher G. Brinton 136 4 0 15 Jan 2024
Can persistent homology whiten Transformer-based black-box models? A case study on BERT compression Luis Balderas Miguel Lastra José M. Benítez 34 1 0 17 Dec 2023
Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey Xinyu She Yue Liu Yanjie Zhao Yiling He Li Li Chakkrit Tantithamthavorn Zhan Qin Haoyu Wang ELM 99 14 0 27 Oct 2023
NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models Jongwoo Ko Seungjoon Park Yujin Kim Sumyeong Ahn Du-Seong Chang Euijai Ahn SeYoung Yun 105 6 0 16 Oct 2023
Efficiently Scaling Transformer Inference Reiner Pope Sholto Douglas Aakanksha Chowdhery Jacob Devlin James Bradbury Anselm Levskaya Jonathan Heek Kefan Xiao Shivani Agrawal J. Dean 116 326 0 09 Nov 2022
COST-EFF: Collaborative Optimization of Spatial and Temporal Efficiency with Slenderized Multi-exit Language Models Bowen Shen Zheng Lin Yuanxin Liu Zhengxiao Liu Lei Wang Weiping Wang VLM 72 5 0 27 Oct 2022
Stochastic gradient descent with gradient estimator for categorical features Paul Peseux Maxime Bérar Thierry Paquet Victor Nicollet 99 0 0 08 Sep 2022
Efficient model compression with Random Operation Access Specific Tile (ROAST) hashing Aditya Desai K. Zhou Anshumali Shrivastava 43 1 0 21 Jul 2022
E-LANG: Energy-Based Joint Inferencing of Super and Swift Language Models Mohammad Akbari Amin Banitalebi-Dehkordi Yong Zhang 69 8 0 01 Mar 2022
Can Model Compression Improve NLP Fairness Guangxuan Xu Qingyuan Hu 59 28 0 21 Jan 2022
Sparse is Enough in Scaling Transformers Sebastian Jaszczur Aakanksha Chowdhery Afroz Mohiuddin Lukasz Kaiser Wojciech Gajewski Henryk Michalewski Jonni Kanerva MoE 71 102 0 24 Nov 2021
A Short Study on Compressing Decoder-Based Language Models Tianda Li Yassir El Mesbahi I. Kobyzev Ahmad Rashid A. Mahmud Nithin Anchuri Habib Hajimolahoseini Yang Liu Mehdi Rezagholizadeh 151 25 0 16 Oct 2021
AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing Katikapalli Subramanyam Kalyan A. Rajasekharan S. Sangeetha VLM LM&MA 103 270 0 12 Aug 2021
Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems Xiaocong Du Bhargav Bhushanam Jiecao Yu Dhruv Choudhary Tianxiang Gao Sherman Wong Louis Feng Jongsoo Park Yu Cao A. Kejariwal 66 7 0 04 May 2021
Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data Mao Ye Dhruv Choudhary Jiecao Yu Ellie Wen Zeliang Chen Jiyan Yang Jongsoo Park Qiang Liu A. Kejariwal 60 9 0 16 Oct 2020