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The lottery ticket hypothesis proposes that over-parameterization of deep neural networks (DNNs) aids training by increasing the probability of a "lucky" sub-network initialization being present rather than by helping the optimization process (Frankle & Carbin, 2019).
The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker · 1902
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The lottery ticket hypothesis at scale
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M Roy, and Michael Carbin · 1903
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The arcade learning environment: An evaluation platform for general agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
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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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Cnn features off-the-shelf: An astounding baseline for recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson · 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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A unified game-theoretic approach to multiagent reinforcement learning
Marc Lanctot, Vinicius Zambaldi, Audrūnas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Pérolat, David Silver, and Thore Graepel · 2017
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2017
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Elf: An extensive, lightweight and flexible research platform for real-time strategy games
Yuandong Tian, Qucheng Gong, Wenling Shang, Yuxin Wu, and C. Lawrence Zitnick · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Learning and generalization in overparameterized neural networks, going beyond two layers
Zeyuan Allen-Zhu, Yuanzhi Li, and Yingyu Liang · 2018
Cited alongside, same era.
Quantifying generalization in reinforcement learning
Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, and John Schulman · 2018
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On the power of over-parametrization in neural networks with quadratic activation
Simon S Du and Jason D Lee · 2018
Cited alongside, same era.
Gradient descent provably optimizes over-parameterized neural networks
Simon S Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh · 2019
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
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Do better ImageNet models transfer better?
Simon Kornblith, Jonathon Shlens, and Quoc V Le · 2019
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Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell · 2019
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The role of over-parametrization in generalization of neural networks
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, and Nathan‘ Srebro · 2019
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Scaling neural machine translation
Myle Ott, Sergey Edunov, David Grangier, and Michael Auli · 2018
Cited alongside, same era.
Can deep reinforcement learning solve Erdos-Selfridge-Spencer games?
Maithra Raghu, Alex Irpan, Jacob Andreas, Robert Kleinberg, Quoc V Le, and Jon Kleinberg · 2018
Cited alongside, same era.
A convergence theory for deep learning via over-parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2019
Cited alongside, same era.
Redundant feature pruning for accelerated inference in deep neural networks
Babajide O Ayinde, Tamer Inanc, and Jacek M Zurada · 2019
Cited alongside, same era.
Variational dropout sparsifies deep neural networks
Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov
Cited in the paper.
Pruning convolutional neural networks for resource efficient inference
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz
Cited in the paper.
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli · 2019
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Interpretable convolutional filter pruning, 2019
Zhuwei Qin, Fuxun Yu, Chenchen Liu, and Xiang Chen · 2019
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Uncovering surprising behaviors in reinforcement learning via worst-case analysis, 2019
Avraham Ruderman, Richard Everett, Bristy Sikder, Hubert Soyer, Jonathan Uesato, Ananya Kumar, Charlie Beattie, and Pushmeet Kohli · 2019
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Deconstructing lottery tickets: Zeros, signs, and the supermask
Hattie Zhou, Janice Lan, Rosanne Liu, and Jason Yosinski · 2019
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