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Pruning is an effective method to reduce the memory footprint and FLOPs associated with neural network models.
Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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Second order derivatives for network pruning: Optimal brain surgeon
Babak Hassibi and David G Stork · 1993
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Optimal brain surgeon and general network pruning
Babak Hassibi, David G Stork, and Gregory J Wolff · 1993
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Some large-scale matrix computation problems
Zhaojun Bai, Gark Fahey, and Gene Golub · 1996
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Automatically constructing a corpus of sentential paraphrases
William B Dolan and Chris Brockett · 2005
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Challenges and advances in parallel sparse matrix-matrix multiplication
Aydin Buluc and John R Gilbert · 2008
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Cifar-10 (canadian institute for advanced research)
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2010
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Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix
Haim Avron and Sivan Toledo · 2011
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Randomized algorithms for matrices and data
M. W. Mahoney · 2011
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ImageNet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 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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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
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Pruning convolutional neural networks for resource efficient inference
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Quantized convolutional neural networks for mobile devices
Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, and Jian Cheng · 2016
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Learning to prune deep neural networks via layer-wise optimal brain surgeon
Xin Dong, Shangyu Chen, and Sinno Pan · 2017
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Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
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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
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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
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Learning efficient convolutional networks through network slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 2017
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Thinet: A filter level pruning method for deep neural network compression
Jian-Hao Luo, Jianxin Wu, and Weiyao Lin · 2017
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Exploring the regularity of sparse structure in convolutional neural networks
Huizi Mao, Song Han, Jeff Pool, Wenshuo Li, Xingyu Liu, Yu Wang, and William J Dally · 2017
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Apprentice: Using knowledge distillation techniques to improve low-precision network accuracy
Asit Mishra and Debbie Marr · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Designing energy-efficient convolutional neural networks using energy-aware pruning
Tien-Ju Yang, Yu-Hsin Chen, and Vivienne Sze · 2017
Cited alongside, same era.
To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta · 2017
Cited alongside, same era.
SqueezeNext: Hardware-aware neural network design
Amir Gholami, Kiseok Kwon, Bichen Wu, Zizheng Tai, Xiangyu Yue, Peter Jin, Sicheng Zhao, and Kurt Keutzer · 2018
Cited alongside, same era.
Quantized guided pruning for efficient hardware implementations of convolutional neural networks
Ghouthi Boukli Hacene, Vincent Gripon, Matthieu Arzel, Nicolas Farrugia, and Yoshua Bengio · 2018
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
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Searching for mobilenetv3
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al · 2019
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Towards optimal structured cnn pruning via generative adversarial learning
Shaohui Lin, Rongrong Ji, Chenqian Yan, Baochang Zhang, Liujuan Cao, Qixiang Ye, Feiyue Huang, and David Doermann · 2019
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RoBERTa: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Metapruning: Meta learning for automatic neural network channel pruning
Zechun Liu, Haoyuan Mu, Xiangyu Zhang, Zichao Guo, Xin Yang, Kwang-Ting Cheng, and Jian Sun · 2019
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Amc: Automl for model compression and acceleration on mobile devices
Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, and Song Han · 2018
Cited alongside, same era.
Data-driven sparse structure selection for deep neural networks
Zehao Huang and Naiyan Wang · 2018
Cited alongside, same era.
Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018
Cited alongside, same era.
Quantizing deep convolutional networks for efficient inference: A whitepaper
Raghuraman Krishnamoorthi · 2018
Cited alongside, same era.
Snip: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
Cited alongside, same era.
Accelerating convolutional networks via global & dynamic filter pruning
Shaohui Lin, Rongrong Ji, Yuchao Li, Yongjian Wu, Feiyue Huang, and Baochang Zhang · 2018
Cited alongside, same era.
Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell · 2018
Cited alongside, same era.
Are sixteen heads really better than one?
Paul Michel, Omer Levy, and Graham Neubig · 2019
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Mnasnet: Platform-aware neural architecture search for mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, and Quoc V Le · 2019
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Eigendamage: Structured pruning in the kronecker-factored eigenbasis
Chaoqi Wang, Roger Grosse, Sanja Fidler, and Guodong Zhang · 2019
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FBNet: Hardware-aware efficient convnet design via differentiable neural architecture search
Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer · 2019
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Autoprune: Automatic network pruning by regularizing auxiliary parameters
Xia Xiao, Zigeng Wang, and Sanguthevar Rajasekaran · 2019
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PyHessian: Neural networks through the lens of the Hessian
Zhewei Yao, Amir Gholami, Kurt Keutzer, and Michael W. Mahoney · 2019
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Variational convolutional neural network pruning
Chenglong Zhao, Bingbing Ni, Jian Zhang, Qiwei Zhao, Wenjun Zhang, and Qi Tian · 2019
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HAWQ-V2: Hessian aware trace-weighted quantization of neural networks
Zhen Dong, Zhewei Yao, Daiyaan Arfeen, Amir Gholami, Michael W. Mahoney, and Kurt Keutzer · 2020
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Automatic pruning for quantized neural networks
Luis Guerra, Bohan Zhuang, Ian Reid, and Tom Drummond · 2020
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Dmcp: Differentiable markov channel pruning for neural networks
Shaopeng Guo, Yujie Wang, Quanquan Li, and Junjie Yan · 2020
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Learning filter pruning criteria for deep convolutional neural networks acceleration
Yang He, Yuhang Ding, Ping Liu, Linchao Zhu, Hanwang Zhang, and Yi Yang · 2020
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Structured compression by weight encryption for unstructured pruning and quantization
Se Jung Kwon, Dongsoo Lee, Byeongwook Kim, Parichay Kapoor, Baeseong Park, and Gu-Yeon Wei · 2020
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Eagleeye: Fast sub-net evaluation for efficient neural network pruning
Bailin Li, Bowen Wu, Jiang Su, and Guangrun Wang · 2020
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Hrank: Filter pruning using high-rank feature map
Mingbao Lin, Rongrong Ji, Yan Wang, Yichen Zhang, Baochang Zhang, Yonghong Tian, and Ling Shao · 2020
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Fast hardware-aware neural architecture search
Li Lyna Zhang, Yuqing Yang, Yuhang Jiang, Wenwu Zhu, and Yunxin Liu · 2020
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Lookahead: a far-sighted alternative of magnitude-based pruning
Sejun Park, Jaeho Lee, Sangwoo Mo, and Jinwoo Shin · 2020
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Differentiable joint pruning and quantization for hardware efficiency
Ying Wang, Yadong Lu, and Tijmen Blankevoort · 2020
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Adahessian: An adaptive second order optimizer for machine learning
Zhewei Yao, Amir Gholami, Sheng Shen, Kurt Keutzer, and Michael W Mahoney · 2020
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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
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https://github.com/neuripssubmission5022/hessianawarepruning, May 2021
2021
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