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Non-linear activation functions, e.g., Sigmoid, ReLU, and Tanh, have achieved great success in neural networks (NNs).
“Multilayer feedforward networks are universal approximators,”
Kurt Hornik, Maxwell Stinchcombe, and Halbert White, · 1989
Earlier work this paper cites.
“Learning multiple layers of features from tiny images,”
Alex Krizhevsky, Geoffrey Hinton, et al., · 2009
Earlier work this paper cites.
“Rectified linear units improve restricted boltzmann machines,”
Vinod Nair and Geoffrey E. Hinton, · 2010
Earlier work this paper cites.
“Reading digits in natural images with unsupervised feature learning,”
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, and Andrew Y Ng, · 2011
Earlier work this paper cites.
“An analysis of single-layer networks in unsupervised feature learning,”
Adam Coates, Honglak Lee, Andrew Y Ng, Adam Coates, Honglak Lee, and Andrew Y Ng, · 2011
Earlier work this paper cites.
“Estimating or propagating gradients through stochastic neurons for conditional computation,”
Y. Bengio, Nicholas Léonard, and A. Courville, · 2013
Earlier work this paper cites.
“Rectifier nonlinearities improve neural network acoustic models,”
Andrew L Maas, Awni Y Hannun, Andrew Y Ng, et al., · 2013
Earlier work this paper cites.
“Deep learning,”
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, · 2015
Cited alongside, same era.
“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
Cited alongside, same era.
“Searching for activation functions,”
Prajit Ramachandran, Barret Zoph, and Quoc V Le, · 2017
Cited alongside, same era.
“Pruning filters for efficient convnets,”
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf, · 2017
Cited alongside, same era.
“Soft filter pruning for accelerating deep convolutional neural networks,”
Yang He, Guoliang Kang, Xuanyi Dong, Yanwei Fu, and Yi Yang, · 2018
Cited alongside, same era.
“Fast image restoration with multi-bin trainable linear units,”
Shuhang Gu, Wen Li, Luc Van Gool, and Radu Timofte, · 2019
“Exploiting kernel sparsity and entropy for interpretable cnn compression,”
Yuchao Li, Shaohui Lin, Baochang Zhang, Jianzhuang Liu, David Doermann, Yongjian Wu, Feiyue Huang, and Rongrong Ji, · 2019
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“Towards optimal structured cnn pruning via generative adversarial learning,”
S. Lin, R. Ji, C. Yan, B. Zhang, L. Cao, Q. Ye, F. Huang, and D. Doermann, · 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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“Filter pruning via geometric median for deep convolutional neural networks acceleration,”
Yang He, Ping Liu, Ziwei Wang, Zhilan Hu, and Yi Yang, · 2019
Later among the works it cites.
“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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Cited alongside, same era.
Ningning Ma, Xiangyu Zhang, Ming Liu, and Jian Sun, · 2021
Later among the works it cites.