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Historically, the pursuit of efficient inference has been one of the driving forces behind research into new deep learning architectures and building blocks.
Evaluating pruning methods
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Holistic sparsecnn: Forging the trident of accuracy, speed, and size
Jongsoo Park, Sheng R. Li, Wei Wen, Hai Li, Yiran Chen, and Pradeep Dubey · 2016
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Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang · 2017
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Xception: Deep learning with depthwise separable convolutions
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Andreas Haas, Andreas Rossberg, Derek L. Schuff, Ben L. Titzer, Michael Holman, Dan Gohman, Luke Wagner, Alon Zakai, and JF Bastien · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
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Densely connected convolutional networks
G. Huang, Z. Liu, L. v. d. Maaten, and K. Q. Weinberger · 2017
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Thinet: A Filter Level Pruning Method for Deep Neural Network Compression
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
Mobilenetv2: Inverted residuals and linear bottlenecks
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Faster gaze prediction with dense networks and Fisher pruning
Lucas Theis, Iryna Korshunova, Alykhan Tejani, and Ferenc Huszár · 2018
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Design principles for sparse matrix multiplication on the gpu
Carl Yang, Aydm Buluc, and John D. Owens · 2018
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To prune, or not to prune: Exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta · 2018
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Learning fast algorithms for linear transforms using butterfly factorizations
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Barret Zoph and Quoc V. Le · 2017
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Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le · 2017
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Compressing neural networks using the variational information bottleneck
Bin Dai, Chen Zhu, Baining Guo, and David Wipf · 2018
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Google, 2018
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Morphnet: Fast simple resource-constrained structure learning of deep networks
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Webml-polyfill, 2019
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Regularized evolution for image classifier architecture search
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Tensorflow.js: Machine learning for the web and beyond
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