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Pruning the weights of randomly initialized neural networks plays an important role in the context of lottery ticket hypothesis.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
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Comparing biases for minimal network construction with back-propagation
Stephen Hanson and Lorien Pratt · 1988
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Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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Learning both weights and connections for efficient neural networks
Song Han, Jeff Pool, John Tran, and William J 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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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Dsd: Dense-sparse-dense training for deep neural networks
Song Han, Jeff Pool, Sharan Narang, Huizi Mao, Enhao Gong, Shijian Tang, Erich Elsen, Peter Vajda, Manohar Paluri, John Tran, et al · 2017
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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Variational dropout sparsifies deep neural networks
Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov · 2017
Cited alongside, same era.
Deep rewiring: Training very sparse deep networks
Guillaume Bellec, David Kappel, Wolfgang Maass, and Robert Legenstein · 2018
Cited alongside, same era.
Snip: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip Torr · 2018
Cited alongside, same era.
Learning sparse neural networks through l_0 regularization
Christos Louizos, Max Welling, and Diederik P Kingma · 2018
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Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
Decebal Constantin Mocanu, Elena Mocanu, Peter Stone, Phuong H Nguyen, Madeleine Gibescu, and Antonio Liotta · 2018
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Logarithmic pruning is all you need
Laurent Orseau, Marcus Hutter, and Omar Rivasplata · 2020
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Optimal lottery tickets via subset sum: Logarithmic over-parameterization is sufficient
Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, and Dimitris Papailiopoulos · 2020
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What’s hidden in a randomly weighted neural network?
Vivek Ramanujan, Mitchell Wortsman, Aniruddha Kembhavi, Ali Farhadi, and Mohammad Rastegari · 2020
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Pruning neural networks without any data by iteratively conserving synaptic flow
Hidenori Tanaka, Daniel Kunin, Daniel L Yamins, and Surya Ganguli · 2020
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Picking winning tickets before training by preserving gradient flow
Chaoqi Wang, Guodong Zhang, and Roger Grosse · 2020
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Pruning from scratch
Yulong Wang, Xiaolu Zhang, Lingxi Xie, Jun Zhou, Hang Su, Bo Zhang, and Xiaolin Hu · 2020
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Jonathan Frankle and Michael Carbin · 2019
Cited alongside, same era.
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
Ari Morcos, Haonan Yu, Michela Paganini, and Yuandong Tian · 2019
Cited alongside, same era.
Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization
Hesham Mostafa and Xin Wang · 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.
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.
Later among the works it cites.
https://pytorch.org/docs/stable/nn.init.html
torch.nn.init – PyTorch 1.8.1 documentation · 2021
Closest in time.
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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facebookresearch/open_lth
Facebook · 2021
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Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, and Alexandra Peste · 2021
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bentrevett/pytorch-sentiment-analysis
Ben Trevett · 2021
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