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Unstructured pruning reduces the memory footprint in deep neural networks (DNNs).
Network flows
Ravindra K Ahuja, Thomas L Magnanti, and James B Orlin · 1988
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Pruning versus clipping in neural networks
S. A. Janowsky · 1989
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A simple procedure for pruning backpropagation trained neural networks
E. D. Karnin · 1990
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Comparing rewinding and fine-tuning in neural network pruning
A. Renda, Jonathan Frankle, and Michael Carbin · 2003
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Torchvision the machine-vision package of torch
Sébastien Marcel and Yann Rodriguez · 2010
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Learning both weights and connections for efficient neural network
Song Han, J. Pool, John Tran, and W. Dally · 2015
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Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and J. Dean · 2015
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 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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Deep rewiring: Training very sparse deep networks
Guillaume Bellec, David Kappel, Wolfgang Maass, and Robert Legenstein · 2017
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Gpu kernels for block-sparse weights
Scott Gray, Alec Radford, and Diederik P Kingma · 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
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2017
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Thinet: A filter level pruning method for deep neural network compression
Jian-Hao Luo, Jianxin Wu, and W. Lin · 2017
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell · 2018
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Learning sparse neural networks through l0 regularization
Christos Louizos, M. Welling, and Diederik P. Kingma · 2018
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Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search
Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer · 2019
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Language models are few-shot learners
T. Brown, B. Mann, Nick Ryder, Melanie Subbiah, J. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, G. Krüger, T. Henighan, R. Child, Aditya Ramesh, D. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, E. Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, J. Clark, Christopher Berner, Sam McCandlish, A. Radford, Ilya Sutskever, and Dario Amodei · 2020
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Fast sparse convnets
Erich Elsen, Marat Dukhan, Trevor Gale, and Karen Simonyan · 2020
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Rigging the lottery: Making all tickets winners
Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, and Erich Elsen · 2020
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Sparse gpu kernels for deep learning
Trevor Gale, Matei A. Zaharia, Cliff Young, and Erich Elsen · 2020
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Decebal Constantin Mocanu, Elena Mocanu, Peter Stone, Phuong H Nguyen, Madeleine Gibescu, and Antonio Liotta · 2018
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Nvidia deep learning examples for tensor cores
Nvidia · 2018
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Snip: Single-shot network pruning based on connection sensitivity
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Up or down? adaptive rounding for post-training quantization
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a100 tensor core gpu architecture
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
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Learning n:m fine-grained structures sparse neural networks from scratch
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