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The breakthrough performance of large language models (LLMs) comes with major computational footprints and high deployment costs.
Second order derivatives for network pruning: Optimal brain surgeon
Babak Hassibi and David Stork · 1992
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts · 2013
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
Earlier work this paper cites.
Pointer sentinel mixture models, 2016
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Learning efficient convolutional networks through network slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 2017
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Identifying quora question pairs having the same intent
S. Shankar · 2017
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Amc: Automl for model compression and acceleration on mobile devices
Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, and Song Han · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman · 2018
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Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin · 2018
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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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
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Patient knowledge distillation for bert model compression
Siqi Sun, Yu Cheng, Zhe Gan, and Jingjing Liu · 2019
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Well-read students learn better: The impact of student initialization on knowledge distillation
Iulia Turc, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Earlier work this paper cites.
Reducing transformer depth on demand with structured dropout
Angela Fan, Edouard Grave, and Armand Joulin · 2019
Earlier work this paper cites.
Provable filter pruning for efficient neural networks
Lucas Liebenwein, Cenk Baykal, Harry Lang, Dan Feldman, and Daniela Rus · 2019
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Filter pruning via geometric median for deep convolutional neural networks acceleration
Yang He, Ping Liu, Ziwei Wang, Zhilan Hu, and Yi Yang · 2019
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Once-for-all: Train one network and specialize it for efficient deployment
Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han · 2019
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Openwebtext corpus, 2019
Aaron Gokaslan and Vanya Cohen · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
Aran Komatsuzaki · 2019
Cited alongside, same era.
Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, and Alexandra Peste · 2021
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A survey of quantization methods for efficient neural network inference
Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W Mahoney, and Kurt Keutzer · 2021
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Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
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Block pruning for faster transformers
François Lagunas, Ella Charlaix, Victor Sanh, and Alexander Rush · 2021
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Group fisher pruning for practical network compression
Liyang Liu, Shilong Zhang, Zhanghui Kuang, Aojun Zhou, Jing-Hao Xue, Xinjiang Wang, Yimin Chen, Wenming Yang, Qingmin Liao, and Wayne Zhang · 2021
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Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Mobilebert: a compact task-agnostic bert for resource-limited devices
Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou · 2020
Cited alongside, same era.
Dynabert: Dynamic bert with adaptive width and depth
Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, and Qun Liu · 2020
Cited alongside, same era.
Movement pruning: Adaptive sparsity by fine-tuning
Victor Sanh, Thomas Wolf, and Alexander Rush · 2020
Cited alongside, same era.
Structured pruning of large language models
Ziheng Wang, Jeremy Wohlwend, and Tao Lei · 2020
Cited alongside, same era.
Compressing pre-trained language models by matrix decomposition
Matan Ben Noach and Yoav Goldberg · 2020
Cited alongside, same era.
Chip: Channel independence-based pruning for compact neural networks
Yang Sui, Miao Yin, Yi Xie, Huy Phan, Saman Aliari Zonouz, and Bo Yuan · 2021
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Accelerated sparse neural training: A provable and efficient method to find n: m transposable masks
Itay Hubara, Brian Chmiel, Moshe Island, Ron Banner, Joseph Naor, and Daniel Soudry · 2021
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M-fac: Efficient matrix-free approximations of second-order information
Elias Frantar, Eldar Kurtic, and Dan Alistarh · 2021
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Deep sparse: A fast cpu inference engine, 2021
NeuralMagic · 2021
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Datasets: A community library for natural language processing
Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, Abhishek Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario Šaško, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clément Delangue, Théo Matussière, Lysandre Debut, Stas Bekman, Pierric Cistac, Thibault Goehringer, Victor Mustar, François Lagunas, Alexander Rush, and Thomas Wolf · 2021
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Structured pruning learns compact and accurate models
Mengzhou Xia, Zexuan Zhong, and Danqi Chen · 2022
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Gptq: Accurate post-training quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh · 2022
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Optimal Brain Compression: A framework for accurate post-training quantization and pruning
Elias Frantar, Sidak Pal Singh, and Dan Alistarh · 2022
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The optimal bert surgeon: Scalable and accurate second-order pruning for large language models
Eldar Kurtic, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, and Dan Alistarh · 2022
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SPDY: Accurate pruning with speedup guarantees
Elias Frantar and Dan Alistarh · 2022
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Revisiting random channel pruning for neural network compression
Yawei Li, Kamil Adamczewski, Wen Li, Shuhang Gu, Radu Timofte, and Luc Van Gool · 2022
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A fast post-training pruning framework for transformers
Woosuk Kwon, Sehoon Kim, Michael W Mahoney, Joseph Hassoun, Kurt Keutzer, and Amir Gholami · 2022
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Gmp*: Well-tuned global magnitude pruning can outperform most bert-pruning methods
Eldar Kurtic and Dan Alistarh · 2022
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Deja vu: Contextual sparsity for efficient llms at inference time
Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Re, et al · 2023
Closest in time.
Awq: Activation-aware weight quantization for llm compression and acceleration
Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Xingyu Dang, and Song Han · 2023
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