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The Lottery Ticket Hypothesis suggests large, over-parameterized neural networks consist of small, sparse subnetworks that can be trained in isolation to reach a similar (or better) test accuracy.
Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H Brendan McMahan, et al. 2019 · 1902
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The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker. 2019 · 1902
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Stabilizing the lottery ticket hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M Roy, and Michael Carbin. 2019 · 1903
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Robust and communication-efficient federated learning from non-iid data
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek. 2019 · 1903
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Ari S Morcos, Haonan Yu, Michela Paganini, and Yuandong Tian. 2019 · 1906
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Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP
Haonan Yu, Sergey Edunov, Yuandong Tian, and Ari S. Morcos. 2019 · 1906
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton. 2010 · 2010
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Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011 · 2011
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Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams. 2012 · 2012
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Sparse autoencoder-based feature transfer learning for speech emotion recognition
Jun Deng, Zixing Zhang, Erik Marchi, and Björn Schuller. 2013 · 2013
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Hidden factors and hidden topics: understanding rating dimensions with review text
Julian McAuley and Jure Leskovec. 2013 · 2013
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Compressing deep convolutional networks using vector quantization
Yunchao Gong, Liu Liu, Ming Yang, and Lubomir Bourdev. 2014 · 2014
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Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. 2014 · 2014
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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 · 2017
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Non-convex optimization for machine learning
Prateek Jain, Purushottam Kar, et al. 2017 · 2017
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Sparse deep transfer learning for convolutional neural network
Jiaming Liu, Yali Wang, and Yu Qiao. 2017 · 2017
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To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta. 2017 · 2017
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Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. 2018 · 2018
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Song Han, Jeff Pool, John Tran, and William Dally. 2015 · 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 · 2015
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Federated learning: Strategies for improving communication efficiency
Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtarik, Ananda Theertha Suresh, and Dave Bacon. 2016 · 2016
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How transferable are neural networks in nlp applications?
Lili Mou, Zhao Meng, Rui Yan, Ge Li, Yan Xu, Lu Zhang, and Zhi Jin. 2016 · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. 2017 · 2017
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Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang. 2018a
Cited in the paper.
Taku Kudo and John Richardson. 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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Cross-domain sentiment classification with target domain specific information
Minlong Peng, Qi Zhang, Yu-gang Jiang, and Xuanjing Huang. 2018 · 2018
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le. 2018 · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin. 2019 · 2019
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Do better imagenet models transfer better?
Simon Kornblith, Jonathon Shlens, and Quoc V Le. 2019 · 2019
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