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Residual networks (Resnets) have become a prominent architecture in deep learning.
Speed of processing in the human visual system
S. Thorpe, D. Fize, and C. Marlot · 1996
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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Rectified linear units improve restricted boltzmann machines
V. Nair and G. Hinton · 2010
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Normalization propagation: A parametric technique for removing internal covariate shift in deep networks
D. Arpit, Y. Zhou, B. U Kota, and V. Govindaraju · 2016
Cited alongside, same era.
Tim Cooijmans, Nicolas Ballas, César Laurent, Çağlar Gülçehre, and Aaron Courville · 2016
Cited alongside, same era.
Recurrent orthogonal networks and long-memory tasks
M. Henaff, A. Szlam, and Y. LeCun · 2016
Later among the works it cites.
Bridging the gaps between residual learning, recurrent neural networks and visual cortex
Q. Liao and T. Poggio · 2016
Later among the works it cites.
The loss surface of residual networks: Ensembles and the role of batch normalization
E. Littwin and L. Wolf · 2016
Later among the works it cites.
The time-course of ultrarapid categorization: The influence of scene congruency and top-down processing
S. Vanmarcke, F. Calders, and F. Wagemans · 2016
Later among the works it cites.
Residual networks are exponential ensembles of relatively shallow networks
A. Veit, M. Wilber, and S. Belongie · 2016
Later among the works it cites.
Learning deep resnet blocks sequentially using boosting theory
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Highway and residual networks learn unrolled iterative estimation
K. Greff, R. Srivastava, and J. Schmidhuber · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun
Cited in the paper.
Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun
Cited in the paper.
Furong Huang, Jordan Ash, John Langford, and Robert Schapire · 2017
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