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We introduce a pruning algorithm that provably sparsifies the parameters of a trained model in a way that approximately preserves the model's predictive accuracy.
Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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Improved approximation guarantees for packing and covering integer programs
Aravind Srinivasan · 1999
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80 million tiny images: A large data set for nonparametric object and scene recognition
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Emily Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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An exploration of parameter redundancy in deep networks with circulant projections
Yu Cheng, Felix X Yu, Rogerio S Feris, Sanjiv Kumar, Alok Choudhary, and Shi-Fu Chang · 2015
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Song Han, Huizi Mao, and William J. Dally · 2015
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Training cnns with low-rank filters for efficient image classification
Yani Ioannou, Duncan Robertson, Jamie Shotton, Roberto Cipolla, and Antonio Criminisi · 2015
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Compression of deep convolutional neural networks for fast and low power mobile applications
Yong-Deok Kim, Eunhyeok Park, Sungjoo Yoo, Taelim Choi, Lu Yang, and Dongjun Shin · 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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Structured transforms for small-footprint deep learning
Vikas Sindhwani, Tara Sainath, and Sanjiv Kumar · 2015
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Convolutional neural networks with low-rank regularization
Cheng Tai, Tong Xiao, Yi Zhang, Xiaogang Wang, et al · 2015
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New frameworks for offline and streaming coreset constructions
Vladimir Braverman, Dan Feldman, and Harry Lang · 2016
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Binary embeddings with structured hashed projections
Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, and Yann LeCun · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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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Fast convnets using group-wise brain damage
Vadim Lebedev and Victor Lempitsky · 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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High-dimensional probability
Roman Vershynin · 2016
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Learning structured sparsity in deep neural networks
Nisp: Pruning networks using neuron importance score propagation
Ruichi Yu, Ang Li, Chun-Fu Chen, Jui-Hsin Lai, Vlad I Morariu, Xintong Han, Mingfei Gao, Ching-Yung Lin, and Larry S Davis · 2018
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Non-vacuous generalization bounds at the imagenet scale: a pac-bayesian compression approach
Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P Adams, and Peter Orbanz · 2018
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Learning and generalization in overparameterized neural networks, going beyond two layers
Zeyuan Allen-Zhu, Yuanzhi Li, and Yingyu Liang · 2019
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Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
Sanjeev Arora, Simon Du, Wei Hu, Zhiyuan Li, and Ruosong Wang · 2019
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Data-dependent coresets for compressing neural networks with applications to generalization bounds
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, and Daniela Rus · 2019
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Net-trim: Convex pruning of deep neural networks with performance guarantee
Alireza Aghasi, Afshin Abdi, Nam Nguyen, and Justin Romberg · 2017
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Compression-aware training of deep networks
Jose M Alvarez and Mathieu Salzmann · 2017
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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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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Runtime neural pruning
Ji Lin, Yongming Rao, Jiwen Lu, and Jie Zhou · 2017
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Learning filter basis for convolutional neural network compression
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Importance estimation for neural network pruning
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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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What is the state of neural network pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag · 2020
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Non-linear activations (weighted sum, nonlinearity)
PyTorch contributors · 2020
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Comparing fine-tuning and rewinding in neural network pruning
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Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network
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