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Transformer-based pre-trained language models have significantly improved the performance of various natural language processing (NLP) tasks in the recent years.
The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker. 2019 · 1902
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Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned
Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov. 2019 · 1905
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Reducing transformer depth on demand with structured dropout
Angela Fan, Edouard Grave, and Armand Joulin. 2019 · 1909
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Reweighted proximal pruning for large-scale language representation
Fu-Ming Guo, Sijia Liu, Finlay S Mungall, Xue Lin, and Yanzhi Wang. 2019 · 1909
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
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Structured pruning of large language models
Ziheng Wang, Jeremy Wohlwend, and Tao Lei. 2019 · 1910
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Ofir Zafrir, Guy Boudoukh, Peter Izsak, and Moshe Wasserblat. 2019 · 1910
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Compressing large-scale transformer-based models: A case study on bert
Prakhar Ganesh, Yao Chen, Xin Lou, Mohammad Ali Khan, Yin Yang, Deming Chen, Marianne Winslett, Hassan Sajjad, and Preslav Nakov. 2020 · 2002
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Compressing bert: Studying the effects of weight pruning on transfer learning
Mitchell A Gordon, Kevin Duh, and Nicholas Andrews. 2020 · 2002
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Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers
Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. 2020 · 2002
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Bert-of-theseus: Compressing bert by progressive module replacing
Canwen Xu, Wangchunshu Zhou, Tao Ge, Furu Wei, and Ming Zhou. 2020 · 2002
Cited alongside, same era.
Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally. 2015 · 2015
Cited alongside, same era.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
Cited alongside, same era.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Cited alongside, same era.
To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta. 2017 · 2017
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin. 2019 · 2019
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Are sixteen heads really better than one?
Paul Michel, Omer Levy, and Graham Neubig. 2019 · 2019
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Energy and policy considerations for deep learning in NLP
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 2019
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Patient knowledge distillation for BERT model compression
Siqi Sun, Yu Cheng, Zhe Gan, and Jingjing Liu. 2019 · 2019
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The lottery ticket hypothesis for pre-trained bert networks
Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, and Michael Carbin. 2020 · 2020
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Fast sparse convnets
Erich Elsen, Marat Dukhan, Trevor Gale, and Karen Simonyan. 2020 · 2020
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Cited alongside, same era.
Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
Cited alongside, same era.
GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Tinybert: Distilling bert for natural language understanding
Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu. 2020 · 2020
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Structured pruning of a bert-based question answering model
J. S. McCarley, Rishav Chakravarti, and Avirup Sil. 2020 · 2020
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Comparing rewinding and fine-tuning in neural network pruning
Alex Renda, Jonathan Frankle, and Michael Carbin. 2020 · 2020
Later among the works it cites.