Fetching the paper…
Reading the bibliography…
Pruning enables appealing reductions in network memory footprint and time complexity.
A new measure of rank correlation
Maurice G Kendall · 1938
Earlier work this paper cites.
The proof and measurement of association between two things
Charles Spearman · 1961
Earlier work this paper cites.
Pruning neural networks during training by backpropagation
Yue-Seng Goh and Eng-Chong Tan · 1994
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
Earlier work this paper cites.
Song Han, Huizi Mao, and William J Dally · 2015
Earlier work this paper cites.
Deep residual learning for image recognition. corr abs/1512.03385 (2015), 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Learning the number of neurons in deep networks
Jose M Alvarez and Mathieu Salzmann · 2016
Earlier work this paper cites.
Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
Earlier work this paper cites.
Pruning convolutional neural networks for resource efficient inference
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2016
Earlier work this paper cites.
Chenzhuo Zhu, Song Han, Huizi Mao, and William J Dally · 2016
Earlier work this paper cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. Howard, Menglong Zhu, Bo Chen, D. Kalenichenko, W. Wang, Tobias Weyand, M. Andreetto, and H. Adam · 2017
Earlier work this paper cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Earlier work this paper cites.
Thinet: A filter level pruning method for deep neural network compression
Jian-Hao Luo, Jianxin Wu, and Weiyao Lin · 2017
Cited alongside, same era.
Critical learning periods in deep networks
Alessandro Achille, Matteo Rovere, and Stefano Soatto · 2018
Cited alongside, same era.
Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 2018
Cited alongside, same era.
ChamNet: Towards efficient network design through platform-aware model adaptation
X. Dai, P. Zhang, B. Wu, H. Yin, F. Sun, Y. Wang, M. Dukhan, Y. Hu, Y. Wu, Y. Jia, P. Vajda, M. Uyttendaele, and N. Jha · 2019
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
Cited alongside, same era.
Vacl: Variance-aware cross-layer regularization for pruning deep residual networks
Gradual channel pruning while training using feature relevance scores for convolutional neural networks
S. A. Aketi, S. Roy, A. Raghunathan, and K. Roy · 2020
Later among the works it cites.
Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Later among the works it cites.
Zeroq: A novel zero shot quantization framework
Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 2020
Later among the works it cites.
Progressive skeletonization: Trimming more fat from a network at initialization
Pau de Jorge, Amartya Sanyal, Harkirat S Behl, Philip HS Torr, Gregory Rogez, and Puneet K Dokania · 2020
Later among the works it cites.
Linear mode connectivity and the lottery ticket hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M Roy, and Michael Carbin · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Susan Gao, Xin Liu, Lung-Sheng Chien, William Zhang, and Jose M Alvarez · 2019
Cited alongside, same era.
Filter pruning via geometric median for deep convolutional neural networks acceleration
Yang He, Ping Liu, Ziwei Wang, Zhilan Hu, and Yi Yang · 2019
Cited alongside, same era.
Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton · 2019
Cited alongside, same era.
SNIP: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip Torr · 2019
Cited alongside, same era.
Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell · 2019
Cited alongside, same era.
Prunetrain: fast neural network training by dynamic sparse model reconfiguration
Sangkug Lym, Esha Choukse, Siavash Zangeneh, Wei Wen, Sujay Sanghavi, and Mattan Erez · 2019
Cited alongside, same era.
Importance estimation for neural network pruning
Pavlo Molchanov, Arun Mallya, Stephen Tyree, Iuri Frosio, and Jan Kautz · 2019
Cited alongside, same era.
Later among the works it cites.
The early phase of neural network training
Jonathan Frankle, David J. Schwab, and Ari S. Morcos · 2020
Later among the works it cites.
The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker · 2020
Later among the works it cites.
Learning filter pruning criteria for deep convolutional neural networks acceleration
Yang He, Yuhang Ding, Ping Liu, Linchao Zhu, Hanwang Zhang, and Yi Yang · 2020
Later among the works it cites.
Convolutional networks for image classification in pytorch
Nvidia · 2020
Later among the works it cites.
Structured compression of deep neural networks with debiased elastic group lasso
Oyebade Oyedotun, Djamila Aouada, and Bjorn Ottersten · 2020
Later among the works it cites.
Pruning neural networks without any data by iteratively conserving synaptic flow
Hidenori Tanaka, Daniel Kunin, Daniel LK Yamins, and Surya Ganguli · 2020
Later among the works it cites.
Unas: Differentiable architecture search meets reinforcement learning
Arash Vahdat, Arun Mallya, Ming-Yu Liu, and Jan Kautz · 2020
Later among the works it cites.
Picking winning tickets before training by preserving gradient flow
Chaoqi Wang, Guodong Zhang, and Roger Grosse · 2020
Later among the works it cites.
Apq: Joint search for network architecture, pruning and quantization policy
Tianzhe Wang, Kuan Wang, Han Cai, Ji Lin, Zhijian Liu, Hanrui Wang, Yujun Lin, and Song Han · 2020
Later among the works it cites.
Dreaming to distill: Data-free knowledge transfer via DeepInversion
Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz · 2020
Later among the works it cites.