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Residual units are wildly used for alleviating optimization difficulties when building deep neural networks.
Some mathematical notes on three-mode factor analysis
Tucker, Ledyard R · 1966
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Decompositions of a higher-order tensor in block terms–part ii: Definitions and uniqueness
De Lathauwer, Lieven · 2008
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Tensor decompositions and applications
Kolda, Tamara G and Bader, Brett W · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, Alex · 2009
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Tensor-train decomposition
Oseledets, Ivan V · 2011
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Understanding deep architectures using a recursive convolutional network
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Speeding up convolutional neural networks with low rank expansions
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Adam: A method for stochastic optimization
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Speeding-up convolutional neural networks using fine-tuned cp-decomposition
Lebedev, Vadim, Ganin, Yaroslav, Rakhuba, Maksim, Oseledets, Ivan, and Lempitsky, Victor · 2014
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Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, Sergey and Szegedy, Christian · 2015
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Tensorizing neural networks
Novikov, Alexander, Podoprikhin, Dmitrii, Osokin, Anton, and Vetrov, Dmitry P · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, Olga, Deng, Jia, Su, Hao, Krause, Jonathan, Satheesh, Sanjeev, Ma, Sean, Huang, Zhiheng, Karpathy, Andrej, Khosla, Aditya, Bernstein, Michael, Berg, Alexander C., and Fei-Fei, Li · 2015
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Convolutional rectifier networks as generalized tensor decompositions
Bridging the gaps between residual learning, recurrent neural networks and visual cortex
Liao, Qianli and Poggio, Tomaso · 2016
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Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, Christian, Ioffe, Sergey, Vanhoucke, Vincent, and Alemi, Alex · 2016
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Residual networks behave like ensembles of relatively shallow networks
Veit, Andreas, Wilber, Michael J, and Belongie, Serge · 2016
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Wider or deeper: Revisiting the resnet model for visual recognition
Wu, Zifeng, Shen, Chunhua, and Hengel, Anton van den · 2016
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Aggregated residual transformations for deep neural networks
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Deep simnets
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Ultimate tensorization: compressing convolutional and fc layers alike
Garipov, Timur, Podoprikhin, Dmitry, Novikov, Alexander, and Vetrov, Dmitry · 2016
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Ha, David, Dai, Andrew, and Le, Quoc V · 2016
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Deep residual learning for image recognition
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian
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Identity mappings in deep residual networks
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian
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Xie, Saining, Girshick, Ross, Dollár, Piotr, Tu, Zhuowen, and He, Kaiming · 2016
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Zagoruyko, Sergey and Komodakis, Nikos · 2016
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Polynet: A pursuit of structural diversity in very deep networks
Zhang, Xingcheng, Li, Zhizhong, Loy, Chen Change, and Lin, Dahua · 2016
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Zhao, Hengshuang, Shi, Jianping, Qi, Xiaojuan, Wang, Xiaogang, and Jia, Jiaya · 2016
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Places: An image database for deep scene understanding
Zhou, Bolei, Khosla, Aditya, Lapedriza, Agata, Torralba, Antonio, and Oliva, Aude · 2016
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