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Pruning is a well-established technique for removing unnecessary structure from neural networks after training to improve the performance of inference.
Uniform convergence may be unable to explain generalization in deep learning
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Imagenet large scale visual recognition challenge
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Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size
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Thinet: A filter level pruning method for deep neural network compression
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Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
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Dynamic parameter reallocation improves trainability of deep convolutional networks
Hesham Mostafa and Xin Wang. 2018 · 2018
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Jonathan Frankle and Michael Carbin. 2019 · 2019
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The State of Sparsity in Deep Neural Networks
Trevor Gale, Erich Elsen, and Sara Hooker. 2019 · 2019
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SNIP: Single-shot Network Pruning based on Connection Sensitivity
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Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov. 2017 · 2017
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Exploring sparsity in recurrent neural networks
Sharan Narang, Erich Elsen, Gregory Diamos, and Shubho Sengupta. 2017 · 2017
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Deep Rewiring: Training very sparse deep networks
Guillaume Bellec, David Kappel, Wolfgang Maass, and Robert Legenstein. 2018 · 2018
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Networks for Imagenet on TPUs
Google. 2018 · 2018
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Learning Sparse Neural Networks through L _ 0 L\_0 Regularization
Christos Louizos, Max Welling, and Diederik P Kingma. 2018 · 2018
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Rethinking the Value of Network Pruning. In International Conference on Learning Representations
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell. 2019 · 2019
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PruneTrain: Gradual Structured Pruning from Scratch for Faster Neural Network Training
Sangkug Lym, Esha Choukse, Siavash Zangeneh, Wei Wen, Mattan Erez, and Sujay Shanghavi. 2019 · 2019
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To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta. 2017 · 2019
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Linear Mode Connectivity and the Lottery Ticket Hypothesis. In International Conference on Machine Learning
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M Roy, and Michael Carbin. 2020 · 2020
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