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In seeking for sparse and efficient neural network models, many previous works investigated on enforcing L1 or L0 regularizers to encourage weight sparsity during training.
Exponentially many local minima for single neurons
Peter Auer, Mark Herbster, and Manfred K Warmuth · 1996
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Variable selection via nonconcave penalized likelihood and its oracle properties
Jianqing Fan and Runze Li · 2001
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Non-negative matrix factorization with sparseness constraints
Patrik O Hoyer · 2004
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Model selection and estimation in regression with grouped variables
Ming Yuan and Yi Lin · 2006
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Comparing measures of sparsity
Niall Hurley and Scott Rickard · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Nearly unbiased variable selection under minimax concave penalty
Cun-Hui Zhang et al · 2010
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Blind deconvolution using a normalized sparsity measure
Dilip Krishnan, Terence Tay, and Rob Fergus · 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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A method for finding structured sparse solutions to nonnegative least squares problems with applications
Ernie Esser, Yifei Lou, and Jack Xin · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Ratio and difference of l1 and l2 norms and sparse representation with coherent dictionaries
Penghang Yin, Ernie Esser, and Jack Xin · 2014
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Sparse convolutional neural networks
Baoyuan Liu, Min Wang, Hassan Foroosh, Marshall Tappen, and Marianna Pensky · 2015
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Deep supervised learning for hyperspectral data classification through convolutional neural networks
Konstantinos Makantasis, Konstantinos Karantzalos, Anastasios Doulamis, and Nikolaos Doulamis · 2015
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Euclid in a taxicab: Sparse blind deconvolution with smoothed ℓ 1 \ell_{1} / ℓ 2 \ell_{2} regularization
Audrey Repetti, Mai Quyen Pham, Laurent Duval, Emilie Chouzenoux, and Jean-Christophe Pesquet · 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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Learning the number of neurons in deep networks
Jose M Alvarez and Mathieu Salzmann · 2016
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Learning intrinsic sparse structures within long short-term memory
Wei Wen, Yuxiong He, Samyam Rajbhandari, Minjia Zhang, Wenhan Wang, Fang Liu, Bin Hu, Yiran Chen, and Hai Li · 2017
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Morphnet: Fast & simple resource-constrained structure learning of deep networks
Ariel Gordon, Elad Eban, Ofir Nachum, Bo Chen, Hao Wu, Tien-Ju Yang, and Edward Choi · 2018
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Data-driven sparse structure selection for deep neural networks
Zehao Huang and Naiyan Wang · 2018
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Accelerating convolutional networks via global & dynamic filter pruning
Shaohui Lin, Rongrong Ji, Yuchao Li, Yongjian Wu, Feiyue Huang, and Baochang Zhang · 2018
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Autopruner: An end-to-end trainable filter pruning method for efficient deep model inference
Jian-Hao Luo and Jianxin Wu · 2018
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Yiwen Guo, Anbang Yao, and Yurong Chen · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
Cited alongside, same era.
Faster cnns with direct sparse convolutions and guided pruning
Jongsoo Park, Sheng Li, Wei Wen, Ping Tak Peter Tang, Hai Li, Yiran Chen, and Pradeep Dubey · 2016
Cited alongside, same era.
Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
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Nest: A neural network synthesis tool based on a grow-and-prune paradigm
Xiaoliang Dai, Hongxu Yin, and Niraj K. Jha · 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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Leveraging filter correlations for deep model compression
Pravendra Singh, Vinay Kumar Verma, Piyush Rai, and Vinay P Namboodiri · 2018
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Reconstruction of jointly sparse vectors via manifold optimization
Armenak Petrosyan Tran, Clayton Webster, et al · 2018
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A survey on nonconvex regularization-based sparse and low-rank recovery in signal processing, statistics, and machine learning
Fei Wen, Lei Chu, Peilin Liu, and Robert C Qiu · 2018
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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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A systematic dnn weight pruning framework using alternating direction method of multipliers
Tianyun Zhang, Shaokai Ye, Kaiqi Zhang, Jian Tang, Wujie Wen, Makan Fardad, and Yanzhi Wang · 2018
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Discrimination-aware channel pruning for deep neural networks
Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang, Jing Liu, Yong Guo, Qingyao Wu, Junzhou Huang, and Jinhui Zhu · 2018
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Centripetal sgd for pruning very deep convolutional networks with complicated structure
Xiaohan Ding, Guiguang Ding, Yuchen Guo, and Jungong Han · 2019
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SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip Torr · 2019
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Compressing convolutional neural networks via factorized convolutional filters
Tuanhui Li, Baoyuan Wu, Yujiu Yang, Yanbo Fan, Yong Zhang, and Wei Liu · 2019
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Toward compact convnets via structure-sparsity regularized filter pruning
Shaohui Lin, Rongrong Ji, Yuchao Li, Cheng Deng, and Xuelong Li · 2019
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Transformed ℓ 1 \ell_{1} regularization for learning sparse deep neural networks
Rongrong Ma, Jianyu Miao, Lingfeng Niu, and Peng Zhang · 2019
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Trimming the ℓ 1 \ell_{1} regularizer: Statistical analysis, optimization, and applications to deep learning
Jihun Yun, Peng Zheng, Eunho Yang, Aurelie Lozano, and Aleksandr Aravkin · 2019
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