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The success of lottery ticket initializations (Frankle and Carbin, 2019) suggests that small, sparsified networks can be trained so long as the network is initialized appropriately.
The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker · 1902
Earlier work this paper cites.
The lottery ticket hypothesis at scale
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M Roy, and Michael Carbin · 1903
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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In search of the real inductive bias: On the role of implicit regularization in deep learning
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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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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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Dynamic network surgery for efficient dnns
Yiwen Guo, Anbang Yao, and Yurong Chen · 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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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
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Pruning convolutional neural networks for resource efficient inference
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2016
Cited alongside, same era.
Samuel S Schoenholz, Justin Gilmer, Surya Ganguli, and Jascha Sohl-Dickstein · 2016
Cited alongside, same era.
Variational dropout sparsifies deep neural networks
Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov · 2017
Cited alongside, same era.
Do better ImageNet models transfer better?
Simon Kornblith, Jonathon Shlens, and Quoc V Le · 2018
Later among the works it cites.
Critical initialisation for deep signal propagation in noisy rectifier neural networks
Arnu Pretorius, Elan van Biljon, Steve Kroon, and Herman Kamper · 2018
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A survey on deep transfer learning
Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu · 2018
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Deep mean field theory: Layerwise variance and width variation as methods to control gradient explosion
Greg Yang and Sam S. Schoenholz · 2018
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To prune, or not to prune: Exploring the efficacy of pruning for model compression
Michael H Zhu and Suyog Gupta · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
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