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The strong lottery ticket hypothesis has highlighted the potential for training deep neural networks by pruning, which has inspired interesting practical and theoretical insights into how neural networks can represent functions.
Skeletonization: A technique for trimming the fat from a network via relevance assessment
Michael C Mozer and Paul Smolensky · 1989
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Optimal brain damage
Yann LeCun, John Denker, and Sara Solla · 1990
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Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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Generalization by weight-elimination with application to forecasting
Andreas Weigend, David Rumelhart, and Bernardo Huberman · 1991
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Second order derivatives for network pruning: Optimal brain surgeon
Babak Hassibi and David G. Stork · 1992
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Exponentially small bounds on the expected optimum of the partition and subset sum problems
George S. Lueker · 1998
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
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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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Understanding and improving convolutional neural networks via concatenated rectified linear units
Wenling Shang, Kihyuk Sohn, Diogo Almeida, and Honglak Lee · 2016
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Suraj Srinivas and R. Venkatesh Babu · 2016
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Learning to prune deep neural networks via layer-wise optimal brain surgeon
Xin Dong, Shangyu Chen, and Sinno Jialin Pan · 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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When and why are deep networks better than shallow ones?
Hrushikesh Mhaskar, Qianli Liao, and Tomaso Poggio · 2017
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Pruning convolutional neural networks for resource efficient inference
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2017
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Optimal approximation of continuous functions by very deep relu networks
Dmitry Yarotsky · 2018
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Initialization of ReLUs for dynamical isometry
Rebekka Burkholz and Alina Dubatovka · 2019
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
Cited alongside, same era.
Snip: single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip H. S. Torr · 2019
Cited alongside, same era.
Deconstructing lottery tickets: Zeros, signs, and the supermask
Hattie Zhou, Janice Lan, Rosanne Liu, and Jason Yosinski · 2019
Cited alongside, same era.
Rigging the lottery: Making all tickets winners
Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, and Erich Elsen · 2020
Cited alongside, same era.
Linear mode connectivity and the lottery ticket hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, and Michael Carbin · 2020
Cited alongside, same era.
A signal propagation perspective for pruning neural networks at initialization
Namhoon Lee, Thalaiyasingam Ajanthan, Stephen Gould, and Philip H. S. Torr · 2020
Data-efficient GAN training beyond (just) augmentations: A lottery ticket perspective
Tianlong Chen, Yu Cheng, Zhe Gan, Jingjing Liu, and Zhangyang Wang · 2021
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A unified lottery ticket hypothesis for graph neural networks
Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, and Zhangyang Wang · 2021
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The elastic lottery ticket hypothesis
Xiaohan Chen, Yu Cheng, Shuohang Wang, Zhe Gan, Jingjing Liu, and Zhangyang Wang · 2021
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Multi-prize lottery ticket hypothesis: Finding accurate binary neural networks by pruning a randomly weighted network
James Diffenderfer and Bhavya Kailkhura · 2021
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Lottery tickets with nonzero biases, 2021
Jonas Fischer, Advait Gadhikar, and Rebekka Burkholz · 2021
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Pruning neural networks at initialization: Why are we missing the mark?
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Cited alongside, same era.
Proving the lottery ticket hypothesis: Pruning is all you need
Eran Malach, Gilad Yehudai, Shai Shalev-Schwartz, and Ohad Shamir · 2020
Cited alongside, same era.
Logarithmic pruning is all you need
Laurent Orseau, Marcus Hutter, and Omar Rivasplata · 2020
Cited alongside, same era.
Optimal lottery tickets via subset sum: Logarithmic over-parameterization is sufficient
Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, and Dimitris Papailiopoulos · 2020
Cited alongside, same era.
What’s hidden in a randomly weighted neural network?
Vivek Ramanujan, Mitchell Wortsman, Aniruddha Kembhavi, Ali Farhadi, and Mohammad Rastegari · 2020
Cited alongside, same era.
Comparing rewinding and fine-tuning in neural network pruning
Alex Renda, Jonathan Frankle, and Michael Carbin · 2020
Cited alongside, same era.
Winning the lottery with continuous sparsification
Pedro Savarese, Hugo Silva, and Michael Maire · 2020
Cited alongside, same era.
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, and Michael Carbin · 2021
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Lottery ticket preserves weight correlation: Is it desirable or not?
Ning Liu, Geng Yuan, Zhengping Che, Xuan Shen, Xiaolong Ma, Qing Jin, Jian Ren, Jian Tang, Sijia Liu, and Yanzhi Wang · 2021
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Lottery ticket preserves weight correlation: Is it desirable or not?
Ning Liu, Geng Yuan, Zhengping Che, Xuan Shen, Xiaolong Ma, Qing Jin, Jian Ren, Jian Tang, Sijia Liu, and Yanzhi Wang · 2021
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Sparse training via boosting pruning plasticity with neuroregeneration
Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, and Decebal Constantin Mocanu · 2021
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Sanity checks for lottery tickets: Does your winning ticket really win the jackpot?
Xiaolong Ma, Geng Yuan, Xuan Shen, Tianlong Chen, Xuxi Chen, Xiaohan Chen, Ning Liu, Minghai Qin, Sijia Liu, Zhangyang Wang, and Yanzhi Wang · 2021
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Why lottery ticket wins? a theoretical perspective of sample complexity on sparse neural networks
Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, and Jinjun Xiong · 2021
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Validating the lottery ticket hypothesis with inertial manifold theory
Zeru Zhang, Jiayin Jin, Zijie Zhang, Yang Zhou, Xin Zhao, Jiaxiang Ren, Ji Liu, Lingfei Wu, Ruoming Jin, and Dejing Dou · 2021
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Efficient lottery ticket finding: Less data is more
Zhenyu Zhang, Xuxi Chen, Tianlong Chen, and Zhangyang Wang · 2021
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Convolutional and residual networks provably contain lottery tickets
Rebekka Burkholz · 2022
Closest in time.
On the existence of universal lottery tickets
Rebekka Burkholz, Nilanjana Laha, Rajarshi Mukherjee, and Alkis Gotovos · 2022
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Plant ’n’ seek: Can you find the winning ticket?
Jonas Fischer and Rebekka Burkholz · 2022
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Dynamical isometry for residual networks
Advait Gadhikar and Rebekka Burkholz · 2022
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How Erdoes and Renyi win the lottery
Advait Gadhikar, Sohom Mukherjee, and Rebekka Burkholz · 2022
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