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Pruning Deep Neural Networks (DNNs) is a prominent field of study in the goal of inference runtime acceleration.
Least squares quantization in pcm
Stuart Lloyd · 1982
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The mean, median, and mode of unimodal distributions: a characterization
Sanjib Basu and Anirban DasGupta · 1997
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Nombres de bell et somme de factorielles
Daniel Barsky and Bénali Benzaghou · 2004
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, et al · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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cudnn: Efficient primitives for deep learning
Sharan Chetlur, Cliff Woolley, et al · 2014
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Big data ethics
Neil M Richards and Jonathan H King · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Data-free parameter pruning for deep neural networks
Suraj Srinivas and R Venkatesh Babu · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, et al · 2016
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Efficient inference with tensorrt, 2016
Han Vanholder · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen et al · 2017
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A survey of model compression and acceleration for deep neural networks
Yu Cheng, Duo Wang, et al · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, et al · 2017
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Pruning filters for efficient convnets
Hao Li et al · 2017
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Deep learning hashing for mobile visual search
Wu Liu, Huadong Ma, et al · 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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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Soft filter pruning for accelerating deep convolutional neural networks
Yang He, Guoliang Kang, et al · 2018
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Quantizing deep convolutional networks for efficient inference: A whitepaper
Raghuraman Krishnamoorthi · 2018
Cited alongside, same era.
Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, et al · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, et al · 2018
Cited alongside, same era.
The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker · 2019
Cited alongside, same era.
Openvino deep learning workbench: Comprehensive analysis and tuning of neural networks inference
Yury Gorbachev, Mikhail Fedorov, Iliya Slavutin, Artyom Tugarev, Marat Fatekhov, and Yaroslav Tarkan · 2019
Cited alongside, same era.
Learning sparse & ternary neural networks with entropy-constrained trained ternarization (ec2t)
Arturo Marban, Daniel Becking, Simon Wiedemann, and Wojciech Samek · 2020
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Pruning filter in filter
Fanxu Meng, Hao Cheng, et al · 2020
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Lookahead: a far-sighted alternative of magnitude-based pruning
Sejun Park, Jaeho Lee, et al · 2020
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Comparing rewinding and fine-tuning in neural network pruning
Alex Renda, Jonathan Frankle, and Michael Carbin · 2020
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And the bit goes down: Revisiting the quantization of neural networks
Pierre Stock et al · 2020
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Pruning neural networks without any data by iteratively conserving synaptic flow
Hidenori Tanaka, Daniel Kunin, et al · 2020
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Same, same but different: Recovering neural network quantization error through weight factorization
Eldad Meller, Alexander Finkelstein, Uri Almog, and Mark Grobman · 2019
Cited alongside, same era.
Data-free quantization through weight equalization and bias correction
Markus Nagel, Mart van Baalen, et al · 2019
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V Le · 2019
Cited alongside, same era.
Haq: Hardware-aware automated quantization with mixed precision
Kuan Wang, Zhijian Liu, et al · 2019
Cited alongside, same era.
Improving neural network quantization without retraining using outlier channel splitting
Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Chris De Sa, and Zhiru Zhang · 2019
Cited alongside, same era.
Structured convolutions for efficient neural network design
Yash Bhalgat, Yizhe Zhang, Jamie Lin, and Fatih Porikli · 2020
Cited alongside, same era.
What is the state of neural network pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag · 2020
Cited alongside, same era.
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Scop: Scientific control for reliable neural network pruning
Yehui Tang, Yunhe Wang, et al · 2020
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2020
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Accelerate your cnn from three dimensions: A comprehensive pruning framework
Wenxiao Wang, Minghao Chen, Shuai Zhao, Jinming Hu, Boxi Wu, Zhengxu Yu, Deng Cai, and Haifeng Liu · 2020
Later among the works it cites.
Good subnetworks provably exist: Pruning via greedy forward selection
Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans, and Qiang Liu · 2020
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Dreaming to distill: Data-free knowledge transfer via deepinversion
Hongxu Yin, Pavlo Molchanov, et al · 2020
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Neuron-level structured pruning using polarization regularizer
Tao Zhuang, Zhixuan Zhang, et al · 2020
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Levit: a vision transformer in convnet’s clothing for faster inference
Benjamin Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, and Matthijs Douze · 2021
Closest in time.
Network pruning via resource reallocation
Yuenan Hou, Zheng Ma, Chunxiao Liu, Zhe Wang, and Chen Change Loy · 2021
Closest in time.
Discrimination-aware network pruning for deep model compression
Jing Liu, Bohan Zhuang, Zhuangwei Zhuang, Yong Guo, Junzhou Huang, Jinhui Zhu, and Mingkui Tan · 2021
Closest in time.
Accelerating sparse deep neural networks
Asit Mishra, Jorge Albericio Latorre, Jeff Pool, Darko Stosic, Dusan Stosic, Ganesh Venkatesh, Chong Yu, and Paulius Micikevicius · 2021
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Manifold regularized dynamic network pruning
Yehui Tang, Yunhe Wang, Yixing Xu, Yiping Deng, Chao Xu, Dacheng Tao, and Chang Xu · 2021
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Going deeper with image transformers
Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, and Hervé Jégou · 2021
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Red : Looking for redundancies for data-free structured compression of deep neural networks
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord, and Kevin Bailly · 2021
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