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Scaling of deep neural networks, especially Transformers, is pivotal for their surging performance and has further led to the emergence of sophisticated reasoning capabilities in foundation models.
Optimal brain damage
Yann LeCun, John Denker, and Sara Solla · 1989
Earlier work this paper cites.
Optimal brain surgeon and general network pruning
Babak Hassibi, David G Stork, and Gregory J Wolff · 1993
Earlier work this paper cites.
Picking winning tickets before training by preserving gradient flow
Chaoqi Wang, Guodong Zhang, and Roger Grosse · 2002
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Earlier work this paper cites.
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M Saxe, James L McClelland, and Surya Ganguli · 2013
Earlier work this paper cites.
Net2net: Accelerating learning via knowledge transfer
Tianqi Chen, Ian Goodfellow, and Jonathon Shlens · 2015
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Designing neural network architectures using reinforcement learning
Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Exponential expressivity in deep neural networks through transient chaos
Ben Poole, Subhaneil Lahiri, Maithra Raghu, Jascha Sohl-Dickstein, and Surya Ganguli · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
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Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
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Deep rewiring: Training very sparse deep networks
Guillaume Bellec, David Kappel, Wolfgang Maass, and Robert Legenstein · 2017
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Multi-level residual networks from dynamical systems view
Bo Chang, Lili Meng, Eldad Haber, Frederick Tung, and David Begert · 2017
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Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2017
Earlier work this paper cites.
Learning sparse neural networks through l _ 0 l\_0 regularization
Christos Louizos, Max Welling, and Diederik P Kingma · 2017
Earlier work this paper cites.
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.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Snip: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
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Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
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Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
Decebal Constantin Mocanu, Elena Mocanu, Peter Stone, Phuong H Nguyen, Madeleine Gibescu, and Antonio Liotta · 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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Dynamical isometry and a mean field theory of cnns: How to train 10,000-layer vanilla convolutional neural networks
Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel Schoenholz, and Jeffrey Pennington · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding, 2019
Progressively stacking 2.0: A multi-stage layerwise training method for bert training speedup
Cheng Yang, Shengnan Wang, Chao Yang, Yuechuan Li, Ru He, and Jingqiao Zhang · 2020
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Zero-cost proxies for lightweight nas
Mohamed S Abdelfattah, Abhinav Mehrotra, Łukasz Dudziak, and Nicholas D Lane · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale, 2021
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Keepaugment: A simple information-preserving data augmentation approach
Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, and Qiang Liu · 2021
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Efficient training of bert by progressively stacking
Linyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang, and Tieyan Liu · 2019
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A signal propagation perspective for pruning neural networks at initialization
Namhoon Lee, Thalaiyasingam Ajanthan, Stephen Gould, and Philip HS Torr · 2019
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Roberta: A robustly optimized bert pretraining approach, 2019
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Aging evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al · 2019
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Evaluating the search phase of neural architecture search
Kaicheng Yu, Christian Sciuto, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann · 2019
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Root mean square layer normalization
Biao Zhang and Rico Sennrich · 2019
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Joel Jang, Seonghyeon Ye, Sohee Yang, Joongbo Shin, Janghoon Han, Gyeonghun Kim, Stanley Jungkyu Choi, and Minjoon Seo · 2021
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Neural architecture search without training
Joe Mellor, Jack Turner, Amos Storkey, and Elliot J Crowley · 2021
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Knowledge inheritance for pre-trained language models
Yujia Qin, Yankai Lin, Jing Yi, Jiajie Zhang, Xu Han, Zhengyan Zhang, Yusheng Su, Zhiyuan Liu, Peng Li, Maosong Sun, et al · 2021
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Training data-efficient image transformers & distillation through attention, 2021
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Knas: green neural architecture search
Jingjing Xu, Liang Zhao, Junyang Lin, Rundong Gao, Xu Sun, and Hongxia Yang · 2021
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Continual pre-training of language models
Zixuan Ke, Yijia Shao, Haowei Lin, Tatsuya Konishi, Gyuhak Kim, and Bing Liu · 2022
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Swin transformer v2: Scaling up capacity and resolution
Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, et al · 2022
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Timelms: Diachronic language models from twitter
Daniel Loureiro, Francesco Barbieri, Leonardo Neves, Luis Espinosa Anke, and Jose Camacho-Collados · 2022
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Signal propagation in transformers: Theoretical perspectives and the role of rank collapse
Lorenzo Noci, Sotiris Anagnostidis, Luca Biggio, Antonio Orvieto, Sidak Pal Singh, and Aurelien Lucchi · 2022
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Elle: Efficient lifelong pre-training for emerging data
Yujia Qin, Jiajie Zhang, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou · 2022
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Fine-tuned language models are continual learners
Thomas Scialom, Tuhin Chakrabarty, and Smaranda Muresan · 2022
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Staged training for transformer language models, 2022
Sheng Shen, Pete Walsh, Kurt Keutzer, Jesse Dodge, Matthew Peters, and Iz Beltagy · 2022
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Continual pre-training of large language models: How to (re) warm your model?
Kshitij Gupta, Benjamin Thérien, Adam Ibrahim, Mats L Richter, Quentin Anthony, Eugene Belilovsky, Irina Rish, and Timothée Lesort · 2023
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Deep transformers without shortcuts: Modifying self-attention for faithful signal propagation
Bobby He, James Martens, Guodong Zhang, Aleksandar Botev, Andrew Brock, Samuel L Smith, and Yee Whye Teh · 2023
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Openai’s gpt-3 language model: A technical overview
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Mimetic initialization of self-attention layers
Asher Trockman and J Zico Kolter · 2023
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Baichuan 2: Open large-scale language models
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